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Sample records for model based diagnosis

  1. Efficient Model-Based Diagnosis Engine

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Vatan, Farrokh; Barrett, Anthony; James, Mark; Mackey, Ryan; Williams, Colin

    2009-01-01

    An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.

  2. Model-based reconfiguration: Diagnosis and recovery

    NASA Technical Reports Server (NTRS)

    Crow, Judy; Rushby, John

    1994-01-01

    We extend Reiter's general theory of model-based diagnosis to a theory of fault detection, identification, and reconfiguration (FDIR). The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analog of the problem of diagnosis. Using a reconfiguration predicate 'rcfg' analogous to the abnormality predicate 'ab,' we derive a strategy for reconfiguration by transforming the corresponding strategy for diagnosis. There are two obvious benefits of this approach: algorithms for diagnosis can be exploited as algorithms for reconfiguration and we have a theoretical framework for an integrated approach to FDIR. As a first step toward realizing these benefits we show that a class of diagnosis engines can be used for reconfiguration and we discuss algorithms for integrated FDIR. We argue that integrating recovery and diagnosis is an essential next step if this technology is to be useful for practical applications.

  3. Fast Algorithms for Model-Based Diagnosis

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Barrett, Anthony; Vatan, Farrokh; Mackey, Ryan

    2005-01-01

    Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.

  4. Distributed real-time model-based diagnosis

    NASA Technical Reports Server (NTRS)

    Barrett, A. C.; Chung, S. H.

    2003-01-01

    This paper presents an approach to onboard anomaly diagnosis that combines the simplicity and real-time guarantee of a rule-based diagnosis system with the specification ease and coverage guarantees of a model-based diagnosis system.

  5. Fault diagnosis based on continuous simulation models

    NASA Technical Reports Server (NTRS)

    Feyock, Stefan

    1987-01-01

    The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.

  6. Qualitative model-based diagnosis using possibility theory

    NASA Technical Reports Server (NTRS)

    Joslyn, Cliff

    1994-01-01

    The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.

  7. An intelligent diagnosis model based on rough set theory

    NASA Astrophysics Data System (ADS)

    Li, Ze; Huang, Hong-Xing; Zheng, Ye-Lu; Wang, Zhou-Yuan

    2013-03-01

    Along with the popularity of computer and rapid development of information technology, how to increase the accuracy of the agricultural diagnosis becomes a difficult problem of popularizing the agricultural expert system. Analyzing existing research, baseing on the knowledge acquisition technology of rough set theory, towards great sample data, we put forward a intelligent diagnosis model. Extract rough set decision table from the samples property, use decision table to categorize the inference relation, acquire property rules related to inference diagnosis, through the means of rough set knowledge reasoning algorithm to realize intelligent diagnosis. Finally, we validate this diagnosis model by experiments. Introduce the rough set theory to provide the agricultural expert system of great sample data a effective diagnosis model.

  8. An Evidential Approach To Model-Based Satellite Diagnosis

    NASA Astrophysics Data System (ADS)

    Bickmore, Timothy W.; Yoshimoto, Glenn M.

    1987-10-01

    Satellite diagnosis presents many unusual problems in the application of current knowledge-based diagnosis technology. The operation of satellite systems involves expertise that spans a large variety of systems, hardware, and software design areas. This expertise includes knowledge of design rationale and sensitivities, development history, test methods, test history, fault history and other indications of pedigree, and operational scenarios and environments. We have developed an approach to satellite diagnosis which can integrate evidence from a variety of diagnostic strategies encompassing this expertise. The system utilizes a structural and behavioral model of the satellite, and uses a form of spreading activation to perform the diagnostic procedures on the model. The various sources of diagnostic evidence are combined using a specially-tailored Dempster-Shafer based utility for modelling uncertainty. A prototype of a diagnostic system based on this approach has been implemented.

  9. Skills Diagnosis Using IRT-Based Continuous Latent Trait Models

    ERIC Educational Resources Information Center

    Stout, William

    2007-01-01

    This article summarizes the continuous latent trait IRT approach to skills diagnosis as particularized by a representative variety of continuous latent trait models using item response functions (IRFs). First, several basic IRT-based continuous latent trait approaches are presented in some detail. Then a brief summary of estimation, model…

  10. The use of multiple models in case-based diagnosis

    NASA Technical Reports Server (NTRS)

    Karamouzis, Stamos T.; Feyock, Stefan

    1993-01-01

    The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation.

  11. Biased Randomized Algorithm for Fast Model-Based Diagnosis

    NASA Technical Reports Server (NTRS)

    Williams, Colin; Vartan, Farrokh

    2005-01-01

    A biased randomized algorithm has been developed to enable the rapid computational solution of a propositional- satisfiability (SAT) problem equivalent to a diagnosis problem. The closest competing methods of automated diagnosis are described in the preceding article "Fast Algorithms for Model-Based Diagnosis" and "Two Methods of Efficient Solution of the Hitting-Set Problem" (NPO-30584), which appears elsewhere in this issue. It is necessary to recapitulate some of the information from the cited articles as a prerequisite to a description of the present method. As used here, "diagnosis" signifies, more precisely, a type of model-based diagnosis in which one explores any logical inconsistencies between the observed and expected behaviors of an engineering system. The function of each component and the interconnections among all the components of the engineering system are represented as a logical system. Hence, the expected behavior of the engineering system is represented as a set of logical consequences. Faulty components lead to inconsistency between the observed and expected behaviors of the system, represented by logical inconsistencies. Diagnosis - the task of finding the faulty components - reduces to finding the components, the abnormalities of which could explain all the logical inconsistencies. One seeks a minimal set of faulty components (denoted a minimal diagnosis), because the trivial solution, in which all components are deemed to be faulty, always explains all inconsistencies. In the methods of the cited articles, the minimal-diagnosis problem is treated as equivalent to a minimal-hitting-set problem, which is translated from a combinatorial to a computational problem by mapping it onto the Boolean-satisfiability and integer-programming problems. The integer-programming approach taken in one of the prior methods is complete (in the sense that it is guaranteed to find a solution if one exists) and slow and yields a lower bound on the size of the

  12. Diagnosing Students' Mental Models via the Web-Based Mental Models Diagnosis System

    ERIC Educational Resources Information Center

    Wang, Tzu-Hua; Chiu, Mei-Hung; Lin, Jing-Wen; Chou, Chin-Cheng

    2013-01-01

    Mental models play an important role in science education research. To extend the effectiveness of conceptual change research and to improve mental model identi?cation and diagnosis, the authors developed and tested the Web-Based Mental Models Diagnosis (WMMD) system. In this article, they describe their WMMD system, which goes beyond the…

  13. Model-based diagnosis of a carbon dioxide removal assembly

    NASA Astrophysics Data System (ADS)

    Throop, David R.; Scarl, Ethan A.

    1992-03-01

    Model-based diagnosis (MBD) has been applied to a variety of mechanisms, but few of these have been in fluid flow domains. Important mechanism variables in these domains are continuous, and the mechanisms commonly contain complex recycle patterns. These properties violate some of the common assumptions for MBD. The CO2 removal assembly (CDRA) for the cabin atmosphere aboard NASA's Space Station Freedom is such a mechanism. Early work on diagnosing similar mechanisms showed that purely associative diagnostic systems could not adequately handle these mechanisms' frequent reconfigurations. This suggested a model-based approach and KATE was adapted to the domain. KATE is a constraint-based MBD shell. It has been successfully applied to liquid flow problems in handling liquid oxygen. However, that domain does not involve complex recycle streams, but the CDRA does. KATE had solved constraint sets by propagating parameter values through constraints; this method often fails on constraints sets which describe recycle systems. KATE was therefore extended to allow it to use external algebraic programs to solve its constraint sets. This paper describes the representational challenges involved in that extension, and describes adaptions which allowed KATE to work within the representational limitations imposed by those algebraic programs. It also presents preliminary results of the CDRA modeling.

  14. Problem-Based Learning: A Tutorial Model Incorporating Pharmaceutical Diagnosis.

    ERIC Educational Resources Information Center

    Culbertson, Vaughn L.; And Others

    1997-01-01

    Describes the problem-based learning methodology used at Idaho State University College of Pharmacy. Objectives of the four-semester course sequence are to emphasize fundamental basic science concepts, develop a system for pharmaceutical diagnosis, facilitate development of clinical problem-solving skills before clerkship, foster team-oriented…

  15. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    PubMed Central

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  16. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory.

    PubMed

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  17. An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Narasimhan, Sriram; Roychoudhury, Indranil; Daigle, Matthew; Pulido, Belarmino

    2013-01-01

    Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

  18. Model-based monitoring and diagnosis of a satellite-based instrument

    NASA Technical Reports Server (NTRS)

    Bos, Andre; Callies, Jorg; Lefebvre, Alain

    1995-01-01

    For about a decade model-based reasoning has been propounded by a number of researchers. Maybe one of the most convincing arguments in favor of this kind of reasoning has been given by Davis in his paper on diagnosis from first principles (Davis 1984). Following their guidelines we have developed a system to verify the behavior of a satellite-based instrument GOME (which will be measuring Ozone concentrations in the near future (1995)). We start by giving a description of model-based monitoring. Besides recognizing that something is wrong, we also like to find the cause for misbehaving automatically. Therefore, we show how the monitoring technique can be extended to model-based diagnosis.

  19. Improving model-based diagnosis through algebraic analysis: The Petri net challenge

    SciTech Connect

    Portinale, L.

    1996-12-31

    The present paper describes the empirical evaluation of a linear algebra approach to model-based diagnosis, in case the behavioral model of the device under examination is described through a Petri net model. In particular, we show that algebraic analysis based on P-invariants of the net model, can significantly improve the performance of a model-based diagnostic system, while keeping the integrity of a general framework defined from a formal logical theory. A system called INVADS is described and experimental results, performed on a car fault domain and involving the comparison of different implementations of P-invariant based diagnosis, are then discussed.

  20. Model-Based Diagnosis in a Power Distribution Test-Bed

    NASA Technical Reports Server (NTRS)

    Scarl, E.; McCall, K.

    1998-01-01

    The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.

  1. Implementation of a model based fault detection and diagnosis technique for actuation faults of the SSME

    NASA Technical Reports Server (NTRS)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1991-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the Space Shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the Space Shuttle Main Engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  2. An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

    2012-01-01

    Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

  3. Diagnosis by integrating model-based reasoning with knowledge-based reasoning

    NASA Technical Reports Server (NTRS)

    Bylander, Tom

    1988-01-01

    Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

  4. Online model-based diagnosis to support autonomous operation of an advanced life support system

    NASA Technical Reports Server (NTRS)

    Biswas, Gautam; Manders, Eric-Jan; Ramirez, John; Mahadevan, Nagabhusan; Abdelwahed, Sherif

    2004-01-01

    This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.

  5. Online model-based diagnosis to support autonomous operation of an advanced life support system.

    PubMed

    Biswas, Gautam; Manders, Eric-Jan; Ramirez, John; Mahadevan, Nagabhusan; Abdelwahed, Sherif

    2004-01-01

    This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed. PMID:15880907

  6. A nursing diagnosis based model: guiding nursing practice.

    PubMed

    Krenz, M; Karlik, B; Kiniry, S

    1989-05-01

    Fiscal uncertainty, anxiety about nursing retention, and public scrutiny characterize the hospital milieu. During times such as these, introducing a conceptual model may appear inpractical and untimely. However, the conceptual model at Robert Wood Johnson University Hospital has demonstrated many practical applications. It guides nursing practice and provides a framework for quality assurance, documentation of nursing care, and education of nurses in the hospital. Future plans include using the model as a basis for developing a computerized care planning system and a method for cost accounting for nursing. The authors describe how the model serves to unify, give direction, simplify, and improve nursing practice. PMID:2723785

  7. Development of model-based fault diagnosis algorithms for MASCOTTE cryogenic test bench

    NASA Astrophysics Data System (ADS)

    Iannetti, A.; Marzat, J.; Piet-Lahanier, H.; Ordonneau, G.; Vingert, L.

    2014-12-01

    This article describes the on-going results of a fault diagnosis benchmark for a cryogenic rocket engine demonstrator. The benchmark consists in the use of classical model- based fault diagnosis methods to monitor the status of the cooling circuit of the MASCOTTE cryogenic bench. The algorithms developed are validated on real data from the last 2014 firing campaign (ATAC campaign). The objective of this demonstration is to find practical diagnosis alternatives to classical redline providing more flexible means of data exploitation in real time and for post processing.

  8. Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft

    NASA Astrophysics Data System (ADS)

    Desbazeille, M.; Randall, R. B.; Guillet, F.; El Badaoui, M.; Hoisnard, C.

    2010-07-01

    This work aims at monitoring large diesel engines by analyzing the crankshaft angular speed variations. It focuses on a powerful 20-cylinder diesel engine with crankshaft natural frequencies within the operating speed range. First, the angular speed variations are modeled at the crankshaft free end. This includes modeling both the crankshaft dynamical behavior and the excitation torques. As the engine is very large, the first crankshaft torsional modes are in the low frequency range. A model with the assumption of a flexible crankshaft is required. The excitation torques depend on the in-cylinder pressure curve. The latter is modeled with a phenomenological model. Mechanical and combustion parameters of the model are optimized with the help of actual data. Then, an automated diagnosis based on an artificially intelligent system is proposed. Neural networks are used for pattern recognition of the angular speed waveforms in normal and faulty conditions. Reference patterns required in the training phase are computed with the model, calibrated using a small number of actual measurements. Promising results are obtained. An experimental fuel leakage fault is successfully diagnosed, including detection and localization of the faulty cylinder, as well as the approximation of the fault severity.

  9. HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Brownston, Lee

    2012-01-01

    Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The

  10. Fault diagnosis using noise modeling and a new artificial immune system based algorithm

    NASA Astrophysics Data System (ADS)

    Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad

    2015-12-01

    A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

  11. The Analysis of Organizational Diagnosis on Based Six Box Model in Universities

    ERIC Educational Resources Information Center

    Hamid, Rahimi; Siadat, Sayyed Ali; Reza, Hoveida; Arash, Shahin; Ali, Nasrabadi Hasan; Azizollah, Arbabisarjou

    2011-01-01

    Purpose: The analysis of organizational diagnosis on based six box model at universities. Research method: Research method was descriptive-survey. Statistical population consisted of 1544 faculty members of universities which through random strafed sampling method 218 persons were chosen as the sample. Research Instrument were organizational…

  12. Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Lo, Chun; Lynch, Jerome P.; Liu, Mingyan

    2016-01-01

    Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.

  13. Livingstone Model-Based Diagnosis of Earth Observing One Infusion Experiment

    NASA Technical Reports Server (NTRS)

    Hayden, Sandra C.; Sweet, Adam J.; Christa, Scott E.

    2004-01-01

    The Earth Observing One satellite, launched in November 2000, is an active earth science observation platform. This paper reports on the progress of an infusion experiment in which the Livingstone 2 Model-Based Diagnostic engine is deployed on Earth Observing One, demonstrating the capability to monitor the nominal operation of the spacecraft under command of an on-board planner, and demonstrating on-board diagnosis of spacecraft failures. Design and development of the experiment, specification and validation of diagnostic scenarios, characterization of performance results and benefits of the model- based approach are presented.

  14. Switched Fault Diagnosis Approach for Industrial Processes based on Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Wang, Lin; Yang, Chunjie; Sun, Youxian; Pan, Yijun; An, Ruqiao

    2015-11-01

    Traditional fault diagnosis methods based on hidden Markov model (HMM) use a unified method for feature extraction, such as principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA). However, every method has its own limitations. For example, PCA cannot extract nonlinear relationships among process variables. So it is inappropriate to extract all features of variables by only one method, especially when data characteristics are very complex. This article proposes a switched feature extraction procedure using PCA and KPCA based on nonlinearity measure. By the proposed method, we are able to choose the most suitable feature extraction method, which could improve the accuracy of fault diagnosis. A simulation from the Tennessee Eastman (TE) process demonstrates that the proposed approach is superior to the traditional one based on HMM and could achieve more accurate classification of various process faults.

  15. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, Hussein; Trendafilova, Irina

    2016-05-01

    This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.

  16. Model-Based Diagnosis and Prognosis of a Water Recycling System

    NASA Technical Reports Server (NTRS)

    Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

    2013-01-01

    A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

  17. Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav

    2010-01-01

    Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

  18. MRI model-based non-invasive differential diagnosis in pulmonary hypertension.

    PubMed

    Lungu, A; Wild, J M; Capener, D; Kiely, D G; Swift, A J; Hose, D R

    2014-09-22

    Pulmonary hypertension(PH) is a disorder characterised by increased mean pulmonary arterial pressure. Currently, the diagnosis of PH relies upon measurements taken during invasive right heart catheterisation (RHC). This paper describes a process to derive diagnostic parameters using only non-invasive methods based upon MRI imaging alone. Simultaneous measurements of main pulmonary artery (MPA) anatomy and flow are interpreted by 0D and 1D mathematical models, in order to infer the physiological status of the pulmonary circulation. Results are reported for 35 subjects, 27 of whom were patients clinically investigated for PH and eight of whom were healthy volunteers. The patients were divided into 3 sub-groups according to the severity of the disease state, one of which represented a negative diagnosis (NoPH), depending on the results of the clinical investigation, which included RHC and complementary MR imaging. Diagnostic indices are derived from two independent mathematical models, one based on the 1D wave equation and one based on an RCR Windkessel model. Using the first model it is shown that there is an increase in the ratio of the power in the reflected wave to that in the incident wave (Wpb/Wptotal) according to the classification of the disease state. Similarly, the second model shows an increase in the distal resistance with the disease status. The results of this pilot study demonstrate that there are statistically significant differences in the parameters derived from the proposed models depending on disease status, and thus suggest the potential for development of a non-invasive, image-based diagnostic test for pulmonary hypertension. PMID:25145313

  19. The KATE shell: An implementation of model-based control, monitor and diagnosis

    NASA Technical Reports Server (NTRS)

    Cornell, Matthew

    1987-01-01

    The conventional control and monitor software currently used by the Space Center for Space Shuttle processing has many limitations such as high maintenance costs, limited diagnostic capabilities and simulation support. These limitations have caused the development of a knowledge based (or model based) shell to generically control and monitor electro-mechanical systems. The knowledge base describes the system's structure and function and is used by a software shell to do real time constraints checking, low level control of components, diagnosis of detected faults, sensor validation, automatic generation of schematic diagrams and automatic recovery from failures. This approach is more versatile and more powerful than the conventional hard coded approach and offers many advantages over it, although, for systems which require high speed reaction times or aren't well understood, knowledge based control and monitor systems may not be appropriate.

  20. Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

    PubMed

    Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K

    2008-07-01

    A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made. PMID:18632325

  1. Explanation Constraint Programming for Model-based Diagnosis of Engineered Systems

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Brownston, Lee; Burrows, Daniel

    2004-01-01

    We can expect to see an increase in the deployment of unmanned air and land vehicles for autonomous exploration of space. In order to maintain autonomous control of such systems, it is essential to track the current state of the system. When the system includes safety-critical components, failures or faults in the system must be diagnosed as quickly as possible, and their effects compensated for so that control and safety are maintained under a variety of fault conditions. The Livingstone fault diagnosis and recovery kernel and its temporal extension L2 are examples of model-based reasoning engines for health management. Livingstone has been shown to be effective, it is in demand, and it is being further developed. It was part of the successful Remote Agent demonstration on Deep Space One in 1999. It has been and is being utilized by several projects involving groups from various NASA centers, including the In Situ Propellant Production (ISPP) simulation at Kennedy Space Center, the X-34 and X-37 experimental reusable launch vehicle missions, Techsat-21, and advanced life support projects. Model-based and consistency-based diagnostic systems like Livingstone work only with discrete and finite domain models. When quantitative and continuous behaviors are involved, these are abstracted to discrete form using some mapping. This mapping from the quantitative domain to the qualitative domain is sometimes very involved and requires the design of highly sophisticated and complex monitors. We propose a diagnostic methodology that deals directly with quantitative models and behaviors, thereby mitigating the need for these sophisticated mappings. Our work brings together ideas from model-based diagnosis systems like Livingstone and concurrent constraint programming concepts. The system uses explanations derived from the propagation of quantitative constraints to generate conflicts. Fast conflict generation algorithms are used to generate and maintain multiple candidates

  2. Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

    NASA Technical Reports Server (NTRS)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1992-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  3. Improved automated diagnosis of misfire in internal combustion engines based on simulation models

    NASA Astrophysics Data System (ADS)

    Chen, Jian; Bond Randall, Robert

    2015-12-01

    In this paper, a new advance in the application of Artificial Neural Networks (ANNs) to the automated diagnosis of misfires in Internal Combustion engines(IC engines) is detailed. The automated diagnostic system comprises three stages: fault detection, fault localization and fault severity identification. Particularly, in the severity identification stage, separate Multi-Layer Perceptron networks (MLPs) with saturating linear transfer functions were designed for individual speed conditions, so they could achieve finer classification. In order to obtain sufficient data for the network training, numerical simulation was used to simulate different ranges of misfires in the engine. The simulation models need to be updated and evaluated using experimental data, so a series of experiments were first carried out on the engine test rig to capture the vibration signals for both normal condition and with a range of misfires. Two methods were used for the misfire diagnosis: one is based on the torsional vibration signals of the crankshaft and the other on the angular acceleration signals (rotational motion) of the engine block. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The ANN systems were trained using only the simulated data and tested using real experimental cases, indicating that the simulation model can be used for a wider range of faults for which it can still be considered valid. The final results have shown that the diagnostic system based on simulation can efficiently diagnose misfire, including location and severity.

  4. An evidential reasoning based model for diagnosis of lymph node metastasis in gastric cancer

    PubMed Central

    2013-01-01

    Background Lymph node metastasis (LNM) in gastric cancer is a very important prognostic factor affecting long-term survival. Currently, several common imaging techniques are used to evaluate the lymph node status. However, they are incapable of achieving both high sensitivity and specificity simultaneously. In order to deal with this complex issue, a new evidential reasoning (ER) based model is proposed to support diagnosis of LNM in gastric cancer. Methods There are 175 consecutive patients who went through multidetector computed tomography (MDCT) consecutively before the surgery. Eight indicators, which are serosal invasion, tumor classification, tumor enhancement pattern, tumor thickness, number of lymph nodes, maximum lymph node size, lymph node station and lymph node enhancement are utilized to evaluate the tumor and lymph node through CT images. All of the above indicators reflect the biological behavior of gastric cancer. An ER based model is constructed by taking the above indicators as input index. The output index determines whether LNM occurs for the patients, which is decided by the surgery and histopathology. A technique called k-fold cross-validation is used for training and testing the new model. The diagnostic capability of LNM is evaluated by receiver operating characteristic (ROC) curves. A Radiologist classifies LNM by adopting lymph node size for comparison. Results 134 out of 175 cases are cases of LNM, and the remains are not. Eight indicators have statistically significant difference between the positive and negative groups. The sensitivity, specificity and AUC of the ER based model are 88.41%, 77.57% and 0.813, respectively. However, for the radiologist evaluating LNM by maximum lymph node size, the corresponding values are only 63.4%, 75.6% and 0.757. Therefore, the proposed model can obtain better performance than the radiologist. Besides, the proposed model also outperforms other machine learning methods. Conclusions According to the

  5. A Cognitive Diagnosis Model for Cognitively Based Multiple-Choice Options

    ERIC Educational Resources Information Center

    de la Torre, Jimmy

    2009-01-01

    Cognitive or skills diagnosis models are discrete latent variable models developed specifically for the purpose of identifying the presence or absence of multiple fine-grained skills. However, applications of these models typically involve dichotomous or dichotomized data, including data from multiple-choice (MC) assessments that are scored as…

  6. Applying Model-based Diagnosis to a Rapid Propellant Loading System

    NASA Technical Reports Server (NTRS)

    Goodrich, Charlie H.; Narasimhan, Sriram; Daigle, Matthew J.; Hatfield, Walter H.; Johnson, Robert G.

    2009-01-01

    The overall objective of the US Air Force Research Laboratory (AFRL) Rapid Propellant Loading (RPL) Program is to develop a launch vehicle, payload and ground support equipment that can support a rapid propellant load and launch within one hour. NASA Kennedy Space Center (KSC) has been funded by AFRL to develop hardware and software to demonstrate this capability. The key features of the software would be the ability to recognize and adapt to failures in the physical hardware components, advise operators of equipment faults and workarounds, and put the system in a safe configuration if unable to fly. In December 2008 NASA KSC and NASA Ames Research Center (ARC) demonstrated model based simulation and diagnosis capabilities for a scaled-down configuration of the RPL hardware. In this paper we present a description of the model-based technologies that were included as part of this demonstration and the results that were achieved. In continuation of this work we are currently testing the technologies on a simulation of the complete RPL system. Later in the year, when the RPL hardware is ready, we will be integrating these technologies with the real-time operation of the system to provide live state estimates. In future years we will be developing the capability to recover from faulty conditions via redundancy and reconfiguration.

  7. A memory-based model of posttraumatic stress disorder: evaluating basic assumptions underlying the PTSD diagnosis.

    PubMed

    Rubin, David C; Berntsen, Dorthe; Bohni, Malene Klindt

    2008-10-01

    In the mnemonic model of posttraumatic stress disorder (PTSD), the current memory of a negative event, not the event itself, determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000). The model accounts for important and reliable findings that are often inconsistent with the current diagnostic view and that have been neglected by theoretical accounts of the disorder, including the following observations. The diagnosis needs objective information about the trauma and peritraumatic emotions but uses retrospective memory reports that can have substantial biases. Negative events and emotions that do not satisfy the current diagnostic criteria for a trauma can be followed by symptoms that would otherwise qualify for PTSD. Predisposing factors that affect the current memory have large effects on symptoms. The inability-to-recall-an-important-aspect-of-the-trauma symptom does not correlate with other symptoms. Loss or enhancement of the trauma memory affects PTSD symptoms in predictable ways. Special mechanisms that apply only to traumatic memories are not needed, increasing parsimony and the knowledge that can be applied to understanding PTSD. PMID:18954211

  8. A Memory Based Model of Posttraumatic Stress Disorder: Evaluating Basic Assumptions Underlying the PTSD Diagnosis

    PubMed Central

    Rubin, David C.; Berntsen, Dorthe; Johansen, Malene Klindt

    2009-01-01

    In the mnemonic model of PTSD, the current memory of a negative event, not the event itself determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the DSM. The model accounts for important and reliable findings that are often inconsistent with the current diagnostic view and that have been neglected by theoretical accounts of the disorder, including the following observations. The diagnosis needs objective information about the trauma and peritraumatic emotions, but uses retrospective memory reports that can have substantial biases. Negative events and emotions that do not satisfy the current diagnostic criteria for a trauma can be followed by symptoms that would otherwise qualify for PTSD. Predisposing factors that affect the current memory have large effects on symptoms. The inability-to-recall-an-important-aspect-of-the-trauma symptom does not correlate with other symptoms. Loss or enhancement of the trauma memory affects PTSD symptoms in predictable ways. Special mechanisms that apply only to traumatic memories are not needed, increasing parsimony and the knowledge that can be applied to understanding PTSD. PMID:18954211

  9. Variable-Length Computerized Adaptive Testing Based on Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Hsu, Chia-Ling; Wang, Wen-Chung; Chen, Shu-Ying

    2013-01-01

    Interest in developing computerized adaptive testing (CAT) under cognitive diagnosis models (CDMs) has increased recently. CAT algorithms that use a fixed-length termination rule frequently lead to different degrees of measurement precision for different examinees. Fixed precision, in which the examinees receive the same degree of measurement…

  10. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    PubMed

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  11. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    PubMed Central

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  12. An evidential reasoning extension to quantitative model-based failure diagnosis

    NASA Technical Reports Server (NTRS)

    Gertler, Janos J.; Anderson, Kenneth C.

    1992-01-01

    The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.

  13. An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

    PubMed

    Chiang, Hsiu-Sen; Pao, Shun-Chi

    2016-05-01

    Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer's disease can significantly reduce deterioration. Early diagnosis is difficult, and early symptoms are frequently overlooked. While much of the literature focuses on disease detection, the use of electroencephalography (EEG) in Alzheimer's diagnosis has received relatively little attention. This study combines the fuzzy and associative Petri net methodologies to develop a model for the effective and objective detection of Alzheimer's disease. Differences in EEG patterns between normal subjects and Alzheimer patients are used to establish prediction criteria for Alzheimer's disease, potentially providing physicians with a reference for early diagnosis, allowing for early action to delay the disease progression. PMID:27059738

  14. Identifying variation in models of care for the genomic-based diagnosis of inherited retinal dystrophies in the United Kingdom.

    PubMed

    Eden, M; Payne, K; Jones, C; Wright, S J; Hall, G; McAllister, M; Black, G

    2016-07-01

    PurposeAdvances in genomic technologies are prompting a realignment of diagnostic and management pathways for rare inherited disease. New models of care are being developed as genomic-based diagnostic testing becomes increasingly relevant within more and more aspects of medicine. This study describes current care models for the provision of a genomic-based diagnosis for patients with inherited retinal dystrophy (IRD) in UK clinical practice.MethodsA structured telephone survey, conducted (in 2014) with all 23 UK Regional Genetics Centres and a sample of specialist ophthalmology centres (n=4), was used to describe models of service delivery and current levels of genomic-based diagnostic testing. Quantitative data were summarised using descriptive statistics. Responses to open-ended questions were summarised using thematic analysis.ResultsOf the 27 centres 10 of them saw IRD patients in 'generic' clinics and 17 centres offered ophthalmic-specific clinics. Extensive regional variation was observed in numbers of patients seen and in how care for the diagnosis and management of IRD was provided.ConclusionsUnderstanding current practice is a necessary first step in the development and evaluation of complex interventions, such as care models for the genomic-based diagnosis of inherited eye conditions. Presented findings here relating to disparities in care provision are potentially linked to previously reported evidence of perceived unmet needs and expectations of IRD service users. This work provides a foundation for the integration of new care models in mainstream medicine. PMID:27080487

  15. Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

    PubMed Central

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  16. Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

    PubMed

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  17. Clinical relevance of model based computer-assisted diagnosis and therapy

    NASA Astrophysics Data System (ADS)

    Schenk, Andrea; Zidowitz, Stephan; Bourquain, Holger; Hindennach, Milo; Hansen, Christian; Hahn, Horst K.; Peitgen, Heinz-Otto

    2008-03-01

    The ability to acquire and store radiological images digitally has made this data available to mathematical and scientific methods. With the step from subjective interpretation to reproducible measurements and knowledge, it is also possible to develop and apply models that give additional information which is not directly visible in the data. In this context, it is important to know the characteristics and limitations of each model. Four characteristics assure the clinical relevance of models for computer-assisted diagnosis and therapy: ability of patient individual adaptation, treatment of errors and uncertainty, dynamic behavior, and in-depth evaluation. We demonstrate the development and clinical application of a model in the context of liver surgery. Here, a model for intrahepatic vascular structures is combined with individual, but in the degree of vascular details limited anatomical information from radiological images. As a result, the model allows for a dedicated risk analysis and preoperative planning of oncologic resections as well as for living donor liver transplantations. The clinical relevance of the method was approved in several evaluation studies of our medical partners and more than 2900 complex surgical cases have been analyzed since 2002.

  18. Completing fault models for abductive diagnosis

    SciTech Connect

    Knill, E.; Cox, P.T.; Pietrzykowski, T.

    1992-11-05

    In logic-based diagnosis, the consistency-based method is used to determine the possible sets of faulty devices. If the fault models of the devices are incomplete or nondeterministic, then this method does not necessarily yield abductive explanations of system behavior. Such explanations give additional information about faulty behavior and can be used for prediction. Unfortunately, system descriptions for the consistency-based method are often not suitable for abductive diagnosis. Methods for completing the fault models for abductive diagnosis have been suggested informally by Poole and by Cox et al. Here we formalize these methods by introducing a standard form for system descriptions. The properties of these methods are determined in relation to consistency-based diagnosis and compared to other ideas for integrating consistency-based and abductive diagnosis.

  19. MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

    EPA Science Inventory

    Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

  20. Immunity-based diagnosis for a motherboard.

    PubMed

    Shida, Haruki; Okamoto, Takeshi; Ishida, Yoshiteru

    2011-01-01

    We have utilized immunity-based diagnosis to detect abnormal behavior of components on a motherboard. The immunity-based diagnostic model monitors voltages of some components, CPU temperatures, and fan speeds. We simulated abnormal behaviors of some components on the motherboard, and we utilized the immunity-based diagnostic model to evaluate motherboard sensors in two experiments. These experiments showed that the immunity-based diagnostic model was an effective method for detecting abnormal behavior of components on the motherboard. PMID:22163857

  1. Multi-Model Diagnosis Method for Lung Cancer based on MOS-SAW Breath Detecting e-Nose

    NASA Astrophysics Data System (ADS)

    Wang, Yishan; Yu, Kai; Wang, Di; Zhao, Cong; Wang, Lin; Wang, Ping

    2011-09-01

    MOS-SAW breath detecting e-Nose combines the metal oxide semiconductor (MOS) sensors and the SAW sensor. We introduce a multi-model diagnosis method, which is used to process the signals of the MOS-SAW e-Nose, to establish the diagnosis models, and to detect unknown samples.

  2. Possible Patient Early Diagnosis by Ultrasonic Noninvasive Estimation of Thermal Gradients into Tissues Based on Spectral Changes Modeling

    PubMed Central

    Bazan, I.; Ramos, A.; Calas, H.; Ramirez, A.; Pintle, R.; Gomez, T. E.; Negreira, C.; Gallegos, F. J.; Rosales, A. J.

    2012-01-01

    To achieve a precise noninvasive temperature estimation, inside patient tissues, would open promising research fields, because its clinic results would provide early-diagnosis tools. In fact, detecting changes of thermal origin in ultrasonic echo spectra could be useful as an early complementary indicator of infections, inflammations, or cancer. But the effective clinic applications to diagnosis of thermometry ultrasonic techniques, proposed previously, require additional research. Before their implementations with ultrasonic probes and real-time electronic and processing systems, rigorous analyses must be still made over transient echotraces acquired from well-controlled biological and computational phantoms, to improve resolutions and evaluate clinic limitations. It must be based on computing improved signal-processing algorithms emulating tissues responses. Some related parameters in echo-traces reflected by semiregular scattering tissues must be carefully quantified to get a precise processing protocols definition. In this paper, approaches for non-invasive spectral ultrasonic detection are analyzed. Extensions of author's innovations for ultrasonic thermometry are shown and applied to computationally modeled echotraces from scattered biological phantoms, attaining high resolution (better than 0.1°C). Computer methods are provided for viability evaluation of thermal estimation from echoes with distinct noise levels, difficult to be interpreted, and its effectiveness is evaluated as possible diagnosis tool in scattered tissues like liver. PMID:22654958

  3. Knowledge-based diagnosis for aerospace systems

    NASA Technical Reports Server (NTRS)

    Atkinson, David J.

    1988-01-01

    The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

  4. Diagnosis of pulmonary hypertension from magnetic resonance imaging–based computational models and decision tree analysis

    PubMed Central

    Swift, Andrew J.; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M.

    2016-01-01

    Abstract Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH. PMID:27252844

  5. Diagnosis of pulmonary hypertension from magnetic resonance imaging-based computational models and decision tree analysis.

    PubMed

    Lungu, Angela; Swift, Andrew J; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M

    2016-06-01

    Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH. PMID:27252844

  6. Comprehensive diagnosis of whole-body acid-base and fluid-electrolyte disorders using a mathematical model and whole-body base excess.

    PubMed

    Wolf, Matthew B

    2015-08-01

    A mathematical model of whole-body acid-base and fluid-electrolyte balance was used to provide information leading to the diagnosis and fluid-therapy treatment in patients with complex acid-base disorders. Given a set of measured laboratory-chemistry values for a patient, a model of their unique, whole-body chemistry was created. This model predicted deficits or excesses in the masses of Na(+), K(+), Cl(-) and H2O as well as the plasma concentration of unknown or unmeasured species, such as ketoacids, in diabetes mellitus. The model further characterized the acid-base disorder by determining the patient's whole-body base excess and quantitatively partitioning it into ten components, each contributing to the overall disorder. The results of this study showed the importance of a complete set of laboratory measurements to obtain sufficient accuracy of the quantitative diagnosis; having only a minimal set, just pH and PCO2, led to a large scatter in the predicted results. A computer module was created that would allow a clinician to achieve this diagnosis at the bedside. This new diagnostic approach should prove to be valuable in the treatment of the critically ill. PMID:25281215

  7. Improving Distributed Diagnosis Through Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew John; Roychoudhury, Indranil; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino

    2011-01-01

    Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

  8. Physically-based modeling of speed sensors for fault diagnosis and fault tolerant control in wind turbines

    NASA Astrophysics Data System (ADS)

    Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

    2014-12-01

    In this paper, a generic physically-based modeling framework for encoder type speed sensors is derived. The consideration takes into account the nominal fault-free and two most relevant fault cases. The advantage of this approach is a reconstruction of the output waveforms in dependence of the internal physical parameter changes which enables a more accurate diagnosis and identification of faulty incremental encoders i.a. in wind turbines. The objectives are to describe the effect of the tilt and eccentric of the encoder disk on the digital output signals and the influence of the accuracy of the speed measurement in wind turbines. Simulation results show the applicability and effectiveness of the proposed approach.

  9. Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

    NASA Technical Reports Server (NTRS)

    Figueroa, Jorge F.; Walker, Mark G.; Kapadia, Ravi; Morris, Jonathan

    2012-01-01

    Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

  10. Knowledge-based nursing diagnosis

    NASA Astrophysics Data System (ADS)

    Roy, Claudette; Hay, D. Robert

    1991-03-01

    Nursing diagnosis is an integral part of the nursing process and determines the interventions leading to outcomes for which the nurse is accountable. Diagnoses under the time constraints of modern nursing can benefit from a computer assist. A knowledge-based engineering approach was developed to address these problems. A number of problems were addressed during system design to make the system practical extended beyond capture of knowledge. The issues involved in implementing a professional knowledge base in a clinical setting are discussed. System functions, structure, interfaces, health care environment, and terminology and taxonomy are discussed. An integrated system concept from assessment through intervention and evaluation is outlined.

  11. The AIMAR recommendations for early diagnosis of chronic obstructive respiratory disease based on the WHO/GARD model*

    PubMed Central

    2014-01-01

    the Italian context; the document of the Agency for Regional Healthcare Services (AGE.NA.S) is a more suited compendium for consultation, and the recent joint statement on integrated COPD management of the three major Italian scientific Associations in the respiratory area together with the contribution of a Society of General Medicine deals prevalently with some critical issues (appropriateness of diagnosis, pharmacological treatment, rehabilitation, continuing care); also the document “Care Continuity: Chronic Obstructive Pulmonary Disease (COPD)” of the Global Alliance against chronic Respiratory Diseases (GARD)-Italy does not treat in depth the issue of early diagnosis. The present document – produced by the AIMAR (Interdisciplinary Association for Research in Lung Disease) Task Force for early diagnosis of chronic respiratory disease based on the WHO/GARD model and on available evidence and expertise –after a general examination of the main epidemiologic aspects, proposes to integrate the above-mentioned existing documents. In particular: a) it formally indicates on the basis of the available evidence the modalities and the instruments necessary for carrying out secondary prevention at the primary care level (a pro-active,‘case-finding’approach; assessment of the individual’s level of risk of COPD; use of short questionnaires for an initial screening based on symptoms; use of simple spirometry for the second level of screening); b) it identifies possible ways of including these activities within primary care practice; c) it places early diagnosis within the “systemic”, consequential management of chronic respiratory diseases, which will be briefly described with the aid of schemes taken from the Italian and international reference documents. PMID:25473523

  12. Final Progress Report on Model-Based Diagnosis of Soil Limitations to Forest Productivity

    SciTech Connect

    Luxmoore, R.J.

    2004-08-30

    This project was undertaken in support of the forest industry to link modeling of nutrients and productivity with field research to identify methods for enhancing soil quality and forest productivity and for alleviating soil limitations to sustainable forest productivity. The project consisted of a series of related tasks, including (1) simulation of changes in biomass and soil carbon with nitrogen fertilization, (2) development of spreadsheet modeling tools for soil nutrient availability and tree nutrient requirements, (3) additional modeling studies, and (4) evaluation of factors involved in the establishment and productivity of southern pine plantations in seasonally wet soils. This report also describes the two Web sites that were developed from the research to assist forest managers with nutrient management of Douglas-fir and loblolly pine plantations.

  13. Parameter estimation for uncertain systems based on fault diagnosis using Takagi-Sugeno model.

    PubMed

    Nagy-Kiss, A M; Schutz, G; Ragot, J

    2015-05-01

    The paper addresses a systematic procedure to deal with state and parameter uncertainty estimation for nonlinear time-varying systems. A robust observer with respect to states, inputs and perturbations is designed, using a Takagi-Sugeno (T-S) approach with unknown premise variables. Tools of the linear automatic to the nonlinear systems are applied, using the Linear Matrix Inequalities optimization. The observer estimates the uncertainties, the states and minimizes the effect of external disturbances on the estimation error. The uncertainties are modelled in a polynomial way which allows considering the uncertainty estimation as a fault detection problem. The residual sensitivity to faults while maintaining robustness according to a noise signal is handled by H∞/H- approach. The method performance is illustrated using the three-tank system. PMID:25677711

  14. Setting health care capitations through diagnosis-based risk adjustment: a suitable model for the English NHS?

    PubMed

    Asthana, Sheena; Gibson, Alex

    2011-07-01

    The English system of health resource allocation has been described as the apotheosis of the area-level approach to setting health care capitations. However, recent policy developments have changed the scale at which commissioning decisions are made (and budgets allocated) with important implications for resource allocation. Doubts concerning the legitimacy of applying area-based formulae used to distribute resources between Primary Care Trusts (PCTs) to the much smaller scale required by Practice Based Commissioning (PBC) led the English Department of Health (DH) to introduce a new approach to setting health care budgets. To this end, practice-level allocations for acute services are now calculated using a diagnosis-based capitation model of the kind used in the United States and several other systems of competitive social health insurance. The new Coalition Government has proposed that these budgets are directly allocated to GP 'consortia', the new commissioning bodies in the NHS. This paper questions whether this is an appropriate development for a health system in which the major objective of resource allocation is to promote equal opportunity of access for equal needs. The chief reservation raised is that of circularity and the perpetuation of resource bias, the concern being that an existing social, demographic and geographical bias in the use of health care resources will be reinforced through the use of historic utilisation data. Demonstrating that there are legitimate reasons to suspect that this will be the case, the paper poses the question whether health systems internationally should more openly address the key limitations of empirical methods that select risk adjusters on the basis of existing patterns of health service utilisation. PMID:21093953

  15. A Multicomponent Latent Trait Model for Diagnosis

    ERIC Educational Resources Information Center

    Embretson, Susan E.; Yang, Xiangdong

    2013-01-01

    This paper presents a noncompensatory latent trait model, the multicomponent latent trait model for diagnosis (MLTM-D), for cognitive diagnosis. In MLTM-D, a hierarchical relationship between components and attributes is specified to be applicable to permit diagnosis at two levels. MLTM-D is a generalization of the multicomponent latent trait…

  16. Fault Diagnosis approach based on a model-based reasoner and a functional designer for a wind turbine. An approach towards self-maintenance

    NASA Astrophysics Data System (ADS)

    Echavarria, E.; Tomiyama, T.; van Bussel, G. J. W.

    2007-07-01

    The objective of this on-going research is to develop a design methodology to increase the availability for offshore wind farms, by means of an intelligent maintenance system capable of responding to faults by reconfiguring the system or subsystems, without increasing service visits, complexity, or costs. The idea is to make use of the existing functional redundancies within the system and sub-systems to keep the wind turbine operational, even at a reduced capacity if necessary. Re-configuration is intended to be a built-in capability to be used as a repair strategy, based on these existing functionalities provided by the components. The possible solutions can range from using information from adjacent wind turbines, such as wind speed and direction, to setting up different operational modes, for instance re-wiring, re-connecting, changing parameters or control strategy. The methodology described in this paper is based on qualitative physics and consists of a fault diagnosis system based on a model-based reasoner (MBR), and on a functional redundancy designer (FRD). Both design tools make use of a function-behaviour-state (FBS) model. A design methodology based on the re-configuration concept to achieve self-maintained wind turbines is an interesting and promising approach to reduce stoppage rate, failure events, maintenance visits, and to maintain energy output possibly at reduced rate until the next scheduled maintenance.

  17. System monitoring and diagnosis with qualitative models

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1991-01-01

    A substantial foundation of tools for model-based reasoning with incomplete knowledge was developed: QSIM (a qualitative simulation program) and its extensions for qualitative simulation; Q2, Q3 and their successors for quantitative reasoning on a qualitative framework; and the CC (component-connection) and QPC (Qualitative Process Theory) model compilers for building QSIM QDE (qualitative differential equation) models starting from different ontological assumptions. Other model-compilers for QDE's, e.g., using bond graphs or compartmental models, have been developed elsewhere. These model-building tools will support automatic construction of qualitative models from physical specifications, and further research into selection of appropriate modeling viewpoints. For monitoring and diagnosis, plausible hypotheses are unified against observations to strengthen or refute the predicted behaviors. In MIMIC (Model Integration via Mesh Interpolation Coefficients), multiple hypothesized models of the system are tracked in parallel in order to reduce the 'missing model' problem. Each model begins as a qualitative model, and is unified with a priori quantitative knowledge and with the stream of incoming observational data. When the model/data unification yields a contradiction, the model is refuted. When there is no contradiction, the predictions of the model are progressively strengthened, for use in procedure planning and differential diagnosis. Only under a qualitative level of description can a finite set of models guarantee the complete coverage necessary for this performance. The results of this research are presented in several publications. Abstracts of these published papers are presented along with abtracts of papers representing work that was synergistic with the NASA grant but funded otherwise. These 28 papers include but are not limited to: 'Combined qualitative and numerical simulation with Q3'; 'Comparative analysis and qualitative integral representations

  18. Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling.

    PubMed

    Li, Fan; Upadhyaya, Belle R; Coffey, Lonnie A

    2009-04-01

    This paper presents an incipient fault diagnosis approach based on the Group Method of Data Handling (GMDH) technique. The GMDH algorithm provides a generic framework for characterizing the interrelationships among a set of process variables of fossil power plant sub-systems and is employed to generate estimates of important variables in a data-driven fashion. In this paper, ridge regression techniques are incorporated into the ordinary least squares (OLS) estimator to solve regression coefficients at each layer of the GMDH network. The fault diagnosis method is applied to feedwater heater leak detection with data from an operating coal-fired plant. The results demonstrate the proposed method is capable of providing an early warning to operators when a process fault or an equipment fault occurs in a fossil power plant. PMID:19084227

  19. A PC based fault diagnosis expert system

    NASA Technical Reports Server (NTRS)

    Marsh, Christopher A.

    1990-01-01

    The Integrated Status Assessment (ISA) prototype expert system performs system level fault diagnosis using rules and models created by the user. The ISA evolved from concepts to a stand-alone demonstration prototype using OPS5 on a LISP Machine. The LISP based prototype was rewritten in C and the C Language Integrated Production System (CLIPS) to run on a Personal Computer (PC) and a graphics workstation. The ISA prototype has been used to demonstrate fault diagnosis functions of Space Station Freedom's Operation Management System (OMS). This paper describes the development of the ISA prototype from early concepts to the current PC/workstation version used today and describes future areas of development for the prototype.

  20. Development of an on-line diagnosis system for rotor vibration via model-based intelligent inference

    PubMed

    Bai; Hsiao; Tsai; Lin

    2000-01-01

    An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance. PMID:10641641

  1. Patient specific modeling of palpation-based prostate cancer diagnosis: effects of pelvic cavity anatomy and intrabladder pressure.

    PubMed

    Palacio-Torralba, Javier; Jiménez Aguilar, Elizabeth; Good, Daniel W; Hammer, Steven; McNeill, S Alan; Stewart, Grant D; Reuben, Robert L; Chen, Yuhang

    2016-01-01

    Computational modeling has become a successful tool for scientific advances including understanding the behavior of biological and biomedical systems as well as improving clinical practice. In most cases, only general models are used without taking into account patient-specific features. However, patient specificity has proven to be crucial in guiding clinical practice because of disastrous consequences that can arise should the model be inaccurate. This paper proposes a framework for the computational modeling applied to the example of the male pelvic cavity for the purpose of prostate cancer diagnostics using palpation. The effects of patient specific structural features on palpation response are studied in three selected patients with very different pathophysiological conditions whose pelvic cavities are reconstructed from MRI scans. In particular, the role of intrabladder pressure in the outcome of digital rectal examination is investigated with the objective of providing guidelines to practitioners to enhance the effectiveness of diagnosis. Furthermore, the presence of the pelvic bone in the model is assessed to determine the pathophysiological conditions in which it has to be modeled. The conclusions and suggestions of this work have potential use not only in clinical practice and also for biomechanical modeling where structural patient-specificity needs to be considered. © 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd. PMID:26190813

  2. A theoretical model for the development of a diagnosis-based clinical decision rule for the management of patients with spinal pain

    PubMed Central

    Murphy, Donald R; Hurwitz, Eric L

    2007-01-01

    Background Spinal pain is a common problem, and disability related to spinal pain has great consequence in terms of human suffering, medical costs and costs to society. The traditional approach to the non-surgical management of patients with spinal pain, as well as to research in spinal pain, has been such that the type of treatment any given patient receives is determined more by what type of practitioner he or she sees, rather than by diagnosis. Furthermore, determination of treatment depends more on the type of practitioner than by the needs of the patient. Much needed is an approach to clinical management and research that allows clinicians to base treatment decisions on a reliable and valid diagnostic strategy leading to treatment choices that result in demonstrable outcomes in terms of pain relief and functional improvement. The challenges of diagnosis in patients with spinal pain, however, are that spinal pain is often multifactorial, the factors involved are wide ranging, and for most of these factors there exist no definitive objective tests. Discussion The theoretical model of a diagnosis-based clinical decision rule has been developed that may provide clinicians with an approach to non-surgical spine pain patients that allows for specific treatment decisions based on a specific diagnosis. This is not a classification scheme, but a thought process that attempts to identify most important features present in each individual patient. Presented here is a description of the proposed approach, in which reliable and valid assessment procedures are used to arrive at a working diagnosis which considers the disparate factors contributing to spinal pain. Treatment decisions are based on the diagnosis and the outcome of treatment can be measured. Summary In this paper, the theoretical model of a proposed diagnosis-based clinical decision rule is presented. In a subsequent manuscript, the current evidence for the approach will be systematically reviewed, and we will

  3. Developing a semantic web model for medical differential diagnosis recommendation.

    PubMed

    Mohammed, Osama; Benlamri, Rachid

    2014-10-01

    In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results. PMID:25178271

  4. An Operational Model of Motor Skill Diagnosis.

    ERIC Educational Resources Information Center

    Pinheiro, Victor E. D.; Simon, Herbert A.

    1992-01-01

    The ability to diagnose motor skills is important for physical educators. The paper discusses processes critical in motor skill diagnosis, proposing an operational model of motor skill development diagnosis for teacher educators and practitioners. The model provides a foundation upon which to build instructional strategies for developing…

  5. Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals

    NASA Astrophysics Data System (ADS)

    Silveira, Landulfo; Silveira, Fabrício Luiz; Bodanese, Benito; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu T.

    2012-07-01

    Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

  6. An architecture for the development of real-time fault diagnosis systems using model-based reasoning

    NASA Technical Reports Server (NTRS)

    Hall, Gardiner A.; Schuetzle, James; Lavallee, David; Gupta, Uday

    1992-01-01

    Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

  7. Final Report ''Model-Based Approach to Soft-Sensing and Diagnosis for Control of a Continuous Digester''

    SciTech Connect

    Francis J. Doyle III

    2003-06-01

    The goal of the work was to develop and demonstrate computing based modeling and control methodologies that will facilitate integrated operations on the continuous pulp digester. The technical work required to achieve these goals included development of efficient methods for soft-sensing using fundamental models, integration of fault monitoring and control algorithms, and the development of computationally feasible formulations of model predictive control for profile management in the digester. The developed operational methodology for digester grade transition control was benchmarked against an industrial design in cooperation with our collaborators at Weyerhaeuser and Westvaco.

  8. Early diagnosis of Irkut virus infection using magnetic bead-based serum peptide profiling by MALDI-TOF MS in a mouse model.

    PubMed

    Li, Nan; Liu, Ye; Hao, Zhuo; Zhang, Shoufeng; Hu, Rongliang; Li, Jiping

    2014-01-01

    Early diagnosis is important for the prompt post-exposure prophylaxis of lyssavirus infections. To diagnose Irkut virus (IRKV) infection during incubation in mice, a novel method using magnetic bead-based serum peptide profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been established. For this test, serum peptides were concentrated by adsorption to and elution from the magnetic bead-based weak cation ion exchanger. Mass spectrograms obtained by MALDI-TOF MS were analyzed using ClinProTools bioinformatics software. Construction of the diagnostic model was performed using serum samples from mice infected with IRKV and rabies virus (RABV) BD06, Flury-LEP, and SRV9 (as controls). The method accurately diagnosed sera 2, 4 and 8 days after IRKV and RABV infections. The sensitivity, specificity, and total accuracy of diagnosis were 86.7%, 95.2%, and 92.9%, respectively. However, IRKV could not be differentiated from RABV 1 day after infection. The results of the present study indicate that serum peptide profiling by MALDI-TOF MS is a promising technique for the early clinical diagnosis of lyssavirus infections and needs to be further tested in humans and farm animals. PMID:24670473

  9. Relative and Absolute Fit Evaluation in Cognitive Diagnosis Modeling

    ERIC Educational Resources Information Center

    Chen, Jinsong; de la Torre, Jimmy; Zhang, Zao

    2013-01-01

    As with any psychometric models, the validity of inferences from cognitive diagnosis models (CDMs) determines the extent to which these models can be useful. For inferences from CDMs to be valid, it is crucial that the fit of the model to the data is ascertained. Based on a simulation study, this study investigated the sensitivity of various fit…

  10. Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Palmer, Gregory M.; Zhu, Changfang; Breslin, Tara M.; Xu, Fushen; Gilchrist, Kennedy W.; Ramanujam, Nirmala

    2006-02-01

    The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.

  11. A Doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis.

    PubMed

    Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W

    2013-01-01

    The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully. PMID:24253191

  12. A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

    2015-01-01

    Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

  13. Establishment of Simple and Routine Methods in Early Diagnosis of Gentamicin-Induced Kidney Injury Based on a Rat Model

    PubMed Central

    Kang, Youxi; Zhang, Huiqin; Yu, Hai

    2016-01-01

    The changes in biomarkers of gentamycin- (GM-) induced kidney injury have been studied by using simple and routine methods and also assessed the efficacy and utility of these routine biomarkers in early diagnosis. Eighty Sprague-Dawley (SD) rats were randomly divided into 4 groups: three experimental groups treated with different GM dosages (4, 20, and 100 mg·kg−1) and a control group. The experimental groups were given intramuscular GM injections once daily for 14 days, and the control group was given intramuscular sterile water. Blood and urine samples were collected on treatment days 1, 3, 7, and 14 to test for total protein (TP), albumin (ALB), blood urea nitrogen (BUN), creatinine (CRE), uric acid (UA), pH, specific gravity (SG), proteins (PRO), and cells in urinary sediment. Histopathology and kidney coefficient were performed on excised kidney specimens. The result indicated that serum CRE, BUN, and TP, urine PRO, and urinary hyaline casts and low-transitional epithelium showed an immediate and highly sensitive response to kidney injury, and the combined diagnosis with the above methods could be used in early diagnosis. Particularly, the process of the test was simple and quick, no special equipment, so it is more suit for primary medical institution.

  14. Hierarchical Item Response Models for Cognitive Diagnosis

    ERIC Educational Resources Information Center

    Hansen, Mark Patrick

    2013-01-01

    Cognitive diagnosis models (see, e.g., Rupp, Templin, & Henson, 2010) have received increasing attention within educational and psychological measurement. The popularity of these models may be largely due to their perceived ability to provide useful information concerning both examinees (classifying them according to their attribute profiles)…

  15. Calibrating perceived understanding and competency in probability concepts: A diagnosis of learning difficulties based on Rasch probabilistic model

    NASA Astrophysics Data System (ADS)

    Mahmud, Zamalia; Porter, Anne; Salikin, Masniyati; Ghani, Nor Azura Md

    2015-12-01

    Students' understanding of probability concepts have been investigated from various different perspectives. Competency on the other hand is often measured separately in the form of test structure. This study was set out to show that perceived understanding and competency can be calibrated and assessed together using Rasch measurement tools. Forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW have volunteered to participate in the study. Rasch measurement which is based on a probabilistic model is used to calibrate the responses from two survey instruments and investigate the interactions between them. Data were captured from the e-learning platform Moodle where students provided their responses through an online quiz. The study shows that majority of the students perceived little understanding about conditional and independent events prior to learning about it but tend to demonstrate a slightly higher competency level afterward. Based on the Rasch map, there is indication of some increase in learning and knowledge about some probability concepts at the end of the two weeks lessons on probability concepts.

  16. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  17. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  18. The Fuzzy Model for Diagnosis of Animal Disease

    NASA Astrophysics Data System (ADS)

    Jianhua, Xiao; Luyi, Shi; Yu, Zhang; Li, Gao; Honggang, Fan; Haikun, Ma; Hongbin, Wang

    The knowledge of animal disease diagnosis was fuzzy; the fuzzy model can imitate the character of clinical diagnosis for veterinary. The fuzzy model of disease, the methods for class the disease group of differential diagnosis and the fuzzy diagnosis model were discussed in this paper.

  19. The Concept of Sasang Health Index and Constitution-Based Health Assessment: An Integrative Model with Computerized Four Diagnosis Methods

    PubMed Central

    Kim, Jaeuk U.; Ku, Boncho; Kim, Young-Min; Do, Jun-Hyeong; Jang, Eunsu; Jeon, Young Ju; Kim, Keun Ho; Kim, Jong Yeol

    2013-01-01

    Sasang constitutional medicine (SCM) shares its philosophy with that of personalized medicine: it provides constitution-specific treatment and healthcare individualized for each patient. In this work, we propose the concept of the Sasang Health Index (SHI) as an attempt to assess the individualized health status in the framework of SCM. From the target population of females in their fifties and older, we recruited 298 subjects and collected their physiological data, including complexion, radial pulse, and voice, and their questionnaire responses. The health status of each subject was evaluated by two Korean medical doctors independently, and the SHI model was obtained by combining all the integrative features of the phenotype data using a regression technique. As a result, most subjects belonged to either the healthy, subhealthy, or slightly diseased group, and the intraclass correlation coefficient between the two doctors' health scoring reached 0.95. We obtained an SHI model for each constitution type with adjusted R-squares of 0.50, 0.56, and 0.30, for the TE, SE, and SY constitution types, respectively. In the proposed SHI model, the significant characteristics used in the health assessment consisted of constitution-specific features in accordance with the classic literature and features common to all the constitution types. PMID:23843888

  20. Efficient Hilbert transform-based alternative to Tofts physiological models for representing MRI dynamic contrast-enhanced images in computer-aided diagnosis of prostate cancer

    NASA Astrophysics Data System (ADS)

    Boehm, Kevin M.; Wang, Shijun; Burtt, Karen E.; Turkbey, Baris; Weisenthal, Samuel; Pinto, Peter; Choyke, Peter; Wood, Bradford J.; Petrick, Nicholas; Sahiner, Berkman; Summers, Ronald M.

    2015-03-01

    In computer-aided diagnosis (CAD) systems for prostate cancer, dynamic contrast enhanced (DCE) magnetic resonance imaging is useful for distinguishing cancerous and benign tissue. The Tofts physiological model is a commonly used representation of the DCE image data, but the parameters require extensive computation. Hence, we developed an alternative representation based on the Hilbert transform of the DCE images. The time maximum of the Hilbert transform, a binary metric of early enhancement, and a pre-DCE value was assigned to each voxel and appended to a standard feature set derived from T2-weighted images and apparent diffusion coefficient maps. A cohort of 40 patients was used for training the classifier, and 20 patients were used for testing. The AUC was calculated by pooling the voxel-wise prediction values and comparing with the ground truth. The resulting AUC of 0.92 (95% CI [0.87 0.97]) is not significantly different from an AUC calculated using Tofts physiological models of 0.92 (95% CI [0.87 0.97]), as validated by a Wilcoxon signed rank test on each patient's AUC (p = 0.19). The time required for calculation and feature extraction is 11.39 seconds (95% CI [10.95 11.82]) per patient using the Hilbert-based feature set, two orders of magnitude faster than the 1319 seconds (95% CI [1233 1404]) required for the Tofts parameter-based feature set (p<0.001). Hence, the features proposed herein appear useful for CAD systems integrated into clinical workflows where efficiency is important.

  1. Diagnosis and Treatment of Reading Disabilities Based on the Component Model of Reading: An Alternative to the Discrepancy Model of LD

    ERIC Educational Resources Information Center

    Aaron, P. G.; Joshi, R. Malatesha; Gooden, Regina; Bentum, Kwesi E.

    2008-01-01

    Currently, learning disabilities (LD) are diagnosed on the basis of the discrepancy between students' IQ and reading achievement scores. Students diagnosed with LD often receive remedial instruction in resource rooms. The available evidence suggests that the educational policy based on this discrepancy model has not yielded satisfactory results.…

  2. Surgical Intervention for Masticatory Muscle Tendon-Aponeurosis Hyperplasia Based on the Diagnosis Using the Four-Dimensional Muscle Model

    PubMed Central

    Nakaoka, Kazutoshi; Hamada, Yoshiki; Nakatani, Hayaki; Shigeta, Yuko; Hirai, Shinya; Ikawa, Tomoko; Mishima, Akira; Ogawa, Takumi

    2015-01-01

    Objectives: The surgical target of Masticatory muscle tendon-aponeurosis hyperplasia (MMTAH) is the masseter or temporal muscle. In our clinic, the 4-dimentional muscle model (4DMM) has been used to decide if we should approach to the masseter or temporal muscle. The aim of this study is validate the clinical usefulness of 4DMM on the basis of the surgical results. Methods: The 4DMM was constructed from the digital data of 3D-CT and 4-dimentional mandibular movements of the patients. It made us to able to visually observe the expansion rate of masticatory muscles at maximum mouth opening comparing to their length at closed mouth position. Fifteen patients were applied the 4DMM before the surgical treatment and 2 healthy volunteers were enrolled as control group. Results: The expansion rate of temporal muscle at the maximum mouth opening in the patient group was significantly less than that in the control group (P < 0.05). On the other hand, the masseter muscles of all patients were expanded as same as the control group. Therefore the main cause of limitation of mouth-opening was suggested to be a contracture of the temporal muscle. Consequently, we performed successful bilateral coronoidectomy with no surgical intervention to the masseter muscles in all patients. Conclusion: The present 4DMM would be valuable modality to decide the target muscle of surgical treatment for patients with MMTAH. In this pathology, contracture of the temporal muscle seems to be main cause of limited mouth opening. PMID:26352365

  3. Evaluation of Model Fit in Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin

    2016-01-01

    Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…

  4. DNA-based diagnosis of lymphatic filariasis.

    PubMed

    Nuchprayoon, Surang

    2009-09-01

    Lymphatic filariasis (LF) is still a major public health problem. The disease is ranked by the World Health Organization (WHO) as the second leading cause of permanent and long-term disability, and has been targeted for elimination by 2020. Effective diagnosis LF is required for treatment of infected individuals, for epidemiological assessment and for monitoring of the control program. Conventional diagnosis of LF depends on detection of microfilariae (Mf) in blood specimens, which has low sensitivity and specificity. Detection of specific circulating filarial antigens is regarded by WHO as the 'gold standard' for diagnosis of LF. However, the limitations of the antigen tests are cost and inconsistent availability. Although anti-filarial IgG4 antibody levels are associated with active LF infections, however, cross-reactivity with other filarial parasites is common. Not as sensitive as antigen tests, DNA-based techniques have been developed to diagnose and differentiate filarial parasites in humans, animal reservoir hosts, and mosquito vectors. These include DNA hybridization, polymerase chain reaction (PCR) amplification using specific primers (eg Ssp I repeat, pWb12 repeat, pWb-35 repeat, and LDR repeat for Wuchereria bancrofti and Hha I repeat, glutathione peroxidase gene, mitochondrial DNA for Brugia malayi), and universal primers, multiplex-PCR, PCR-restriction fragment length polymorphism (PCR-RFLP), PCR-enzyme linked immunosorbent assay (PCR-ELISA), as well as quantitative PCR. Furthermore, because bancroftian filariasis is endemic on the Thai-Myanmar border, the potential now exists for a re-emergence of bancroftian filariasis in Thailand, and random amplified polymorphic DNA (RAPD) analysis has proved effective to differentiate Thai and Myanmar strains of W. bancrofti. PMID:19842372

  5. Fitting Data to Model: Structural Equation Modeling Diagnosis Using Two Scatter Plots

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Hayashi, Kentaro

    2010-01-01

    This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts…

  6. Sensor-based fault diagnosis in a flight expert system

    NASA Technical Reports Server (NTRS)

    Ali, M.; Scharnhorst, D. A.

    1985-01-01

    A prototype of a knowledge-based flight expert system (FLES) has been developed to assist airplane pilots in monitoring, analyzing, and diagnosing faults and to provide support in reducing the pilot's own mistakes. A sensor simulation model has been developed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Advanced Concept System (ACS), a future-generation airplane, and partly on the Boeing 737, an existing airplane. The architecture of FLES contains several subsystems. One of the major subsystems performs fault diagnosis in the electrical system of the ACS. This paper describes the mechanism and functionality of the automatic diagnosis performed in this expert system.

  7. Wireless laptop-based phonocardiograph and diagnosis

    PubMed Central

    2015-01-01

    Auscultation is used to evaluate heart health, and can indicate when it’s needed to refer a patient to a cardiologist. Advanced phonocardiograph (PCG) signal processing algorithms are developed to assist the physician in the initial diagnosis but they are primarily designed and demonstrated with research quality equipment. Therefore, there is a need to demonstrate the applicability of those techniques with consumer grade instrument. Furthermore, routine monitoring would benefit from a wireless PCG sensor that allows continuous monitoring of cardiac signals of patients in physical activity, e.g., treadmill or weight exercise. In this work, a low-cost portable and wireless healthcare monitoring system based on PCG signal is implemented to validate and evaluate the most advanced algorithms. Off-the-shelf electronics and a notebook PC are used with MATLAB codes to record and analyze PCG signals which are collected with a notebook computer in tethered and wireless mode. Physiological parameters based on the S1 and S2 signals and MATLAB codes are demonstrated. While the prototype is based on MATLAB, the later is not an absolute requirement. PMID:26339555

  8. Variogram-based fault diagnosis in an interconnected tank system.

    PubMed

    Kouadri, Abdelmalek; Aitouche, Mohanad Amokrane; Zelmat, Mimoun

    2012-05-01

    We consider in this paper the fault diagnosis problem of a three tank system DTS-200 pilot plant. The presented approach is based on the analysis of the variogram, which is a graphical variance representation that characterizes the distribution of a measured dataset, and is used to extract the sensor fault parameters. These parameters are obtained by determining the best mathematical model that fits the empirical data. Nonlinear regression techniques are used to estimate the model coefficients. Experimental study is provided to illustrate the potential applicability of this method in process monitoring. PMID:22369877

  9. Reasoning and modeling systems in diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Mathur, Amit; Cavanaugh, Kevin F.; Pattipati, Krishna R.; Willett, Peter K.; Galie, Thomas R.

    2001-07-01

    Diagnosis and prognosis are processes of assessment of a system's health - past, present and future - based on observed data and available knowledge about the system. Due to the nature of the observed data and the available knowledge, the diagnostic and prognostic methods are often a combination of statistical inference and machine learning methods. The development (or selection) of appropriate methods requires appropriate formulation of the learning and inference problems that support the goals of diagnosis and prognosis. An important aspect of the formulation is modeling - relating the real system to its mathematical abstraction. The models, depending on the application and how well it is understood, can be either empirical or scientific (physics based). The expression of the model, too, tends to be statistical (probabilistic) to account for uncertainties and randomness. This paper explores the impact of diagnostic and prognostic goals on modeling and reasoning system requirements, with the purpose of developing a common software framework that can be applied to a large class of systems. In particular, the role of failure-dependency modeling in the overall decision problem is discussed. The applicability of Qualtech Systems' modeling and diagnostic software tools to the presented framework for both the development and implementation of diagnostics and prognostics is assessed. Finally, a potential application concept for advancing the reliability of Navy shipboard Condition Based Maintenance (CBM) systems and processes is discussed.

  10. Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Roychoudhury, Indranil

    2010-01-01

    We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

  11. Measurement of Psychological Disorders Using Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Templin, Jonathan L.; Henson, Robert A.

    2006-01-01

    Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article…

  12. FDSAC-SPICE: fault diagnosis software for analog circuit based on SPICE simulation

    NASA Astrophysics Data System (ADS)

    Cao, Yiqin; Cen, Zhao-Hui; Wei, Jiao-Long

    2009-12-01

    This paper presents a novel fault diagnosis software (called FDSAC-SPICE) based on SPICE simulator for analog circuits. Four important techniques in AFDS-SPICE, including visual user-interface(VUI), component modeling and fault modeling (CMFM), fault injection and fault simulation (FIFS), fault dictionary and fault diagnosis (FDFD), greatly increase design-for-test and diagnosis efficiency of analog circuit by building a fault modeling-injection-simulationdiagnosis environment to get prior fault knowledge of target circuit. AFDS-SPICE also generates accurate fault coverage statistics that are tied to the circuit specifications. With employing a dictionary diagnosis method based on node-signalcharacters and regular BPNN algorithm, more accurate and effective diagnosis results are available for analog circuit with tolerance.

  13. Great Expectations: Expectation Based Reasoning in Medical Diagnosis

    PubMed Central

    Fisher, Paul R.; Miller, Perry L.; Swett, Henry A.

    1988-01-01

    Several different approaches to knowledge representation for medical expert systems have been explored. We suggest that a modified version of the script formalism, which we term “expectation-based reasoning”, may offer an additional knowledge representation for medical information, addressing certain shortcomings of previous approaches. This representation can drive expert system analysis for diagnosis and workup advice. The script formalism structures the knowledge base around a set of temporally sequenced event frames, each containing a list of default expectations. This model, we believe, allows straightforward knowledge generation from a domain expert, since it may closely parallel a central aspect of human clinical decision-making: that of projecting assumptions for a “hypothesize-and-test” inference mechanism. A prototype expectation-based expert system, OSCAR, is under development to explore this approach.

  14. Computer modeling of lung cancer diagnosis-to-treatment process

    PubMed Central

    Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U.; Yu, Xinhua; Faris, Nick

    2015-01-01

    We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed. PMID:26380181

  15. [Endocrine diagnosis in puberty--pathophysiologic bases].

    PubMed

    Girard, J

    1994-05-01

    Puberty is characterized by activation of the maturing gonads and by the thus started increased secretion of sexual steroids. Consequences are the appearance of secondary signs of puberty sensu strictori, i. e. the development of breasts in girls, the increase of testicle volume in boys, often followed by growing pubic hair, axillary hair, menarche or laryngeal growth (puberty vocal change) respectively. The most important accompanying symptom is the spurt of growth starting around 12 to 18 months after the onset of the development of the secondary pubertal signs. From the time sequence of the development and the possible delays, valuable diagnostic hints can be gained, giving rise to a more precise analysis of the hormonal phenomena of adolescence. In cases of pubertas tarda a primary malfunction must be differentiated from secondary hypogonadotropic functional defect. The syndromes should be classified correctly according to their etiology. The most frequent diagnosis is that of a simply delayed puberty. Acne, hypertrichosis, hirsutism are concomitant phenomena of puberty development which can indicate a hormonal imbalance (differential diagnosis AGS, ovarian hyperandrogeny). The swelling of breasts in boys (gynecomastia) is a common transitory phenomenon in male adolescence (DD, tumor of the gonads or Klinefelter syndrome). Interesting considerations of differential diagnosis apply also to the assessment of the enlargement of the thyroid gland in puberty, which affects more often girls than boys. PMID:8016754

  16. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2011-12-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  17. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2012-01-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  18. Multivariate Predictive Model for Dyslexia Diagnosis

    ERIC Educational Resources Information Center

    Le Jan, Guylaine; Le Bouquin-Jeannes, Regine; Costet, Nathalie; Troles, Nolwenn; Scalart, Pascal; Pichancourt, Dominique; Faucon, Gerard; Gombert, Jean-Emile

    2011-01-01

    Dyslexia is a specific disorder of language development that mainly affects reading. Etiological researches have led to multiple hypotheses which induced various diagnosis methods and rehabilitation treatments so that many different tests are used by practitioners to identify dyslexia symptoms. Our purpose is to determine a subset of the most…

  19. Knowledge-based fault diagnosis system for refuse collection vehicle

    NASA Astrophysics Data System (ADS)

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-01

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  20. Knowledge-based fault diagnosis system for refuse collection vehicle

    SciTech Connect

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-15

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  1. Secure Medical Diagnosis Using Rule Based Mining

    NASA Astrophysics Data System (ADS)

    Saleem Durai, M. A.; Sriman Narayana Iyengar, N. Ch.

    Security is the governing dynamics of all walks of life. Here we propose a secured medical diagnosis system. Certain specific rules are specified implicitly by the designer of the expert system and then symptoms for the diseases are obtained from the users and by using the pre defined confidence and support values we extract a threshold value which is used to conclude on a particular disease and the stage using Rule Mining. "THINK" CAPTCHA mechanism is used to distinguish between the human and the robots thereby eliminating the robots and preventing them from creating fake accounts and spam's. A novel image encryption mechanism is designed using genetic algorithm to encrypt the medical images thereby storing and sending the image data in a secured manner.

  2. Probabilistic Modeling of Imaging, Genetics and Diagnosis.

    PubMed

    Batmanghelich, Nematollah K; Dalca, Adrian; Quon, Gerald; Sabuncu, Mert; Golland, Polina

    2016-07-01

    We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimer's Disease Neuroimaging Initiative data. PMID:26886973

  3. Diagnosis of Photochemical Ozone Production Rates and Limiting Factors based on Observation-based Modeling Approach over East Asia: Impact of Radical Chemistry Mechanism and Ozone-Control Implications

    NASA Astrophysics Data System (ADS)

    Kanaya, Y.

    2015-12-01

    Growth of tropospheric ozone, causing health and climate impacts, is concerned over East Asia, because emissions of precursors have dramatically increased. Photochemical production rates of ozone and limiting factors, primarily studied for urban locations, have been poorly assessed within a perspective of regional-scale air pollution over East Asia. We performed comprehensive observations of ozone precursors at several locations with regional representativeness and made such assessment based on the observation-based modeling approach. Here, diagnosis at Fukue Island (32.75°N, 128.68°E) remotely located in western Japan (May 2009) is highlighted, where the highest 10% of hourly ozone concentrations reached 72‒118 ppb during May influenced by Asian continental outflow. The average in-situ ozone production rate was estimated to be 6.8 ppb per day, suggesting that in-travel production was still active, while larger buildup must have occurred beforehand. Information on the chemical status of the air mass arriving in Japan is important, because it affects how further ozone production occurs after precursor addition from Japanese domestic emissions. The main limiting factor of ozone production was usually NOx, suggesting that domestic NOx emission control is important in reducing further ozone production and the incidence of warning issuance (>120 ppb). VOCs also increased the ozone production rate, and occasionally (14% of time) became dominant. This analysis implies that the VOC reduction legislation recently enacted should be effective. The uncertainty in the radical chemistry mechanism governing ozone production had a non-negligible impact, but the main conclusion relevant to policy was not altered. When chain termination was augmented by HO2-H2O + NO/NO2 reactions and by heterogeneous loss of HO2 on aerosol particle surfaces, the daily ozone production rate decreased by <24%, and the fraction of hours when the VOC-limited condition occurred varied from 14% to 13

  4. Pattern-based fault diagnosis using neural networks

    NASA Technical Reports Server (NTRS)

    Dietz, W. E.; Kiech, E. L.; Ali, M.

    1988-01-01

    An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine fault diagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.

  5. An Event-Based Approach to Distributed Diagnosis of Continuous Systems

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

    2010-01-01

    Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

  6. Epiretinal membrane: optical coherence tomography-based diagnosis and classification.

    PubMed

    Stevenson, William; Prospero Ponce, Claudia M; Agarwal, Daniel R; Gelman, Rachel; Christoforidis, John B

    2016-01-01

    Epiretinal membrane (ERM) is a disorder of the vitreomacular interface characterized by symptoms of decreased visual acuity and metamorphopsia. The diagnosis and classification of ERM has traditionally been based on clinical examination findings. However, modern optical coherence tomography (OCT) has proven to be more sensitive than clinical examination for the diagnosis of ERM. Furthermore, OCT-derived findings, such as central foveal thickness and inner segment ellipsoid band integrity, have shown clinical relevance in the setting of ERM. To date, no OCT-based ERM classification scheme has been widely accepted for use in clinical practice and investigation. Herein, we review the pathogenesis, diagnosis, and classification of ERMs and propose an OCT-based ERM classification system. PMID:27099458

  7. Epiretinal membrane: optical coherence tomography-based diagnosis and classification

    PubMed Central

    Stevenson, William; Prospero Ponce, Claudia M; Agarwal, Daniel R; Gelman, Rachel; Christoforidis, John B

    2016-01-01

    Epiretinal membrane (ERM) is a disorder of the vitreomacular interface characterized by symptoms of decreased visual acuity and metamorphopsia. The diagnosis and classification of ERM has traditionally been based on clinical examination findings. However, modern optical coherence tomography (OCT) has proven to be more sensitive than clinical examination for the diagnosis of ERM. Furthermore, OCT-derived findings, such as central foveal thickness and inner segment ellipsoid band integrity, have shown clinical relevance in the setting of ERM. To date, no OCT-based ERM classification scheme has been widely accepted for use in clinical practice and investigation. Herein, we review the pathogenesis, diagnosis, and classification of ERMs and propose an OCT-based ERM classification system. PMID:27099458

  8. A Model Expert System For Machine Failure Diagnosis (MED)

    NASA Astrophysics Data System (ADS)

    Liqun, Yin

    1987-05-01

    MED is a model expert system for machine failure diagnosis. MED can help the repairer quickly determine milling machine electrical failure. The key points in MED are a simple method to deal with the "subsequent visit" problem in machine failure diagnosis, a weighted list to interfere in the control of AGENDA to imitate an expert's continuous thinking process and to keep away erratic questioning and problem running away caused by probabilistic reasoning, the structuralized AGENDA, the characteristics of machine failure diagnosis and people's thinking pattern in faulure diagnosis. The structuralized AGENDA gives an idea to supply a more powerful as well as flexible control strategy in best-first search by using AGENDA. The "subsequent visit" problem is a very complicated task to solve, it will be convenient to deal with it by using a simple method to keep from consuming too much time in urgent situations. Weighted list also gives a method to improve control in inference of expert system. The characteristics of machine failure diagnosis and people's thinking pattern are both important for building a machine failure diagnosis expert system. When being told failure phenomena, MED can determine failure causes through dialogue. MED is written in LISP and run in UNIVAC 1100/10 and IBM PC/XT computers. The average diagnosis time per failure is 11 seconds to CPU, 2 minites to terminal operation, and 11 minites to a skilful repairer.

  9. Chip-Based Sensors for Disease Diagnosis

    NASA Astrophysics Data System (ADS)

    Fang, Zhichao

    Nucleic acid analysis is one of the most important disease diagnostic approaches in medical practice, and has been commonly used in cancer biomarker detection, bacterial speciation and many other fields in laboratory. Currently, the application of powerful research methods for genetic analysis, including the polymerase chain reaction (PCR), DNA sequencing, and gene expression profiling using fluorescence microarrays, are not widely used in hospitals and extended-care units due to high-cost, long detection times, and extensive sample preparation. Bioassays, especially chip-based electrochemical sensors, may be suitable for the next generation of rapid, sensitive, and multiplexed detection tools. Herein, we report three different microelectrode platforms with capabilities enabled by nano- and microtechnology: nanoelectrode ensembles (NEEs), nanostructured microelectrodes (NMEs), and hierarchical nanostructured microelectrodes (HNMEs), all of which are able to directly detect unpurified RNA in clinical samples without enzymatic amplification. Biomarkers that are cancer and infectious disease relevant to clinical medicine were chosen to be the targets. Markers were successfully detected with clinically-relevant sensitivity. Using peptide nucleic acids (PNAs) as probes and an electrocatalytic reporter system, NEEs were able to detect prostate cancer-related gene fusions in tumor tissue samples with 100 ng of RNA. The development of NMEs improved the sensitivity of the assay further to 10 aM of DNA target, and multiplexed detection of RNA sequences of different prostate cancer-related gene fusion types was achieved on the chip-based NMEs platform. An HNMEs chip integrated with a bacterial lysis device was able to detect as few as 25 cfu bacteria in 30 minutes and monitor the detection in real time. Bacterial detection could also be performed in neat urine samples. The development of these versatile clinical diagnostic tools could be extended to the detection of various

  10. Higher-Order Latent Trait Models for Cognitive Diagnosis

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Douglas, Jeffrey A.

    2004-01-01

    Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative…

  11. Fractal dimension based corneal fungal infection diagnosis

    NASA Astrophysics Data System (ADS)

    Balasubramanian, Madhusudhanan; Perkins, A. Louise; Beuerman, Roger W.; Iyengar, S. Sitharama

    2006-08-01

    We present a fractal measure based pattern classification algorithm for automatic feature extraction and identification of fungus associated with an infection of the cornea of the eye. A white-light confocal microscope image of suspected fungus exhibited locally linear and branching structures. The pixel intensity variation across the width of a fungal element was gaussian. Linear features were extracted using a set of 2D directional matched gaussian-filters. Portions of fungus profiles that were not in the same focal plane appeared relatively blurred. We use gaussian filters of standard deviation slightly larger than the width of a fungus to reduce discontinuities. Cell nuclei of cornea and nerves also exhibited locally linear structure. Cell nuclei were excluded by their relatively shorter lengths. Nerves in the cornea exhibited less branching compared with the fungus. Fractal dimensions of the locally linear features were computed using a box-counting method. A set of corneal images with fungal infection was used to generate class-conditional fractal measure distributions of fungus and nerves. The a priori class-conditional densities were built using an adaptive-mixtures method to reflect the true nature of the feature distributions and improve the classification accuracy. A maximum-likelihood classifier was used to classify the linear features extracted from test corneal images as 'normal' or 'with fungal infiltrates', using the a priori fractal measure distributions. We demonstrate the algorithm on the corneal images with culture-positive fungal infiltrates. The algorithm is fully automatic and will help diagnose fungal keratitis by generating a diagnostic mask of locations of the fungal infiltrates.

  12. Design and Implementation of Harmful Algal Bloom Diagnosis System Based on J2EE Platform

    NASA Astrophysics Data System (ADS)

    Guo, Chunfeng; Zheng, Haiyong; Ji, Guangrong; Lv, Liang

    According to the shortcomings which are time consuming and laborious of the traditional HAB (Harmful Algal Bloom) diagnosis by the experienced experts using microscope, all kinds of methods and technologies to identify HAB emerged such as microscopic images, molecular biology, characteristics of pigments analysis, fluorescence spectra, inherent optical properties, etc. This paper proposes the design and implementation of a web-based diagnosis system integrating the popular methods for HAB identification. This system is designed with J2EE platform based on MVC (Model-View-Controller) model as well as technologies such as JSP, Servlets, EJB and JDBC.

  13. Skin image illumination modeling and chromophore identification for melanoma diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Zhao; Zerubia, Josiane

    2015-05-01

    The presence of illumination variation in dermatological images has a negative impact on the automatic detection and analysis of cutaneous lesions. This paper proposes a new illumination modeling and chromophore identification method to correct lighting variation in skin lesion images, as well as to extract melanin and hemoglobin concentrations of human skin, based on an adaptive bilateral decomposition and a weighted polynomial curve fitting, with the knowledge of a multi-layered skin model. Different from state-of-the-art approaches based on the Lambert law, the proposed method, considering both specular reflection and diffuse reflection of the skin, enables us to address highlight and strong shading effects usually existing in skin color images captured in an uncontrolled environment. The derived melanin and hemoglobin indices, directly relating to the pathological tissue conditions, tend to be less influenced by external imaging factors and are more efficient in describing pigmentation distributions. Experiments show that the proposed method gave better visual results and superior lesion segmentation, when compared to two other illumination correction algorithms, both designed specifically for dermatological images. For computer-aided diagnosis of melanoma, sensitivity achieves 85.52% when using our chromophore descriptors, which is 8~20% higher than those derived from other color descriptors. This demonstrates the benefit of the proposed method for automatic skin disease analysis.

  14. Efficient diagnosis of multiprocessor systems under probabilistic models

    NASA Technical Reports Server (NTRS)

    Blough, Douglas M.; Sullivan, Gregory F.; Masson, Gerald M.

    1989-01-01

    The problem of fault diagnosis in multiprocessor systems is considered under a probabilistic fault model. The focus is on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. Lower and upper bounds on the number of tests required for regular systems are also presented. A class of regular systems which includes hypercubes is shown to be correctly diagnosable with high probability. In all cases, the number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis techniques.

  15. Polycrystalline diamond based detector for Z-pinch plasma diagnosis

    SciTech Connect

    Liu Linyue; Zhao Jizhen; Chen Liang; Ouyang Xiaoping; Wang Lan

    2010-08-15

    A detector setup based on polycrystalline chemical-vapor-deposition diamond film is developed with great characteristics: low dark current (lower than 60 pA within 3 V/{mu}m), fast pulsed response time (rise time: 2-3 ns), flat spectral response (3-5 keV), easy acquisition, low cost, and relative large sensitive area. The characterizing data on Qiangguang-I accelerator show that this detector can satisfy the practical requirements in Z-pinch plasma diagnosis very well, which offers a promising prototype for the x-ray detection in Z-pinch diagnosis.

  16. Diagnosis of helicopter gearboxes using structure-based networks

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

    1995-01-01

    A connectionist network is introduced for fault diagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.

  17. Distributed bearing fault diagnosis based on vibration analysis

    NASA Astrophysics Data System (ADS)

    Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

    2016-01-01

    Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

  18. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    PubMed Central

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-01-01

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

  19. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

    PubMed

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-01-01

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

  20. Object-oriented fault tree models applied to system diagnosis

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.

  1. Computer-based diagnosis of illness in historical persons.

    PubMed

    Peters, T J

    2013-01-01

    Retrospective diagnosis of illness in historical figures is a popular but somewhat unreliable pastime due to the lack of detailed information and reliable reports about clinical features and disease progression. Modern computer-based diagnostic programmes have been used to supplement historical documents and accounts, offering new and more objective approaches to the retrospective investigations of the medical conditions of historical persons. In the case of King George III, modern technology has been used to strengthen the findings of previous reports rejecting the popular diagnosis of variegate porphyria in the King, his grandson Augustus d'Esté and his antecedent King James VI and I. Alternative diagnoses based on these programmes are indicated. The Operational Criteria in Studies of Psychotic Illness (OPCRIT) programme and the Young mania scale have been applied to the features described for George III and suggest a diagnosis of bipolar disorder. The neuro-diagnostic programme SimulConsult was applied to Augustus d'Esté and suggests a diagnosis of neuromyelitis optica rather than acute porphyria with secondarily multiple sclerosis, as proposed by others. James VI and I's complex medical history and the clinical features of his behavioural traits were also subjected to SimulConsult analysis; acute porphyria was rejected and the unexpected diagnosis of attenuated (mild) Lesch-Nyhan disease offered. A brief review of these approaches along with full reference listings to the methodology including validation are provided. Textual analysis of the written and verbal outputs of historical figures indicate possible future developments in the diagnosis of medical disorders in historical figures. PMID:23734360

  2. Self-calibrating models for dynamic monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1996-01-01

    A method for automatically building qualitative and semi-quantitative models of dynamic systems, and using them for monitoring and fault diagnosis, is developed and demonstrated. The qualitative approach and semi-quantitative method are applied to monitoring observation streams, and to design of non-linear control systems.

  3. Vibration signal models for fault diagnosis of planet bearings

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

    2016-05-01

    Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.

  4. Imaging-based diagnosis of acute renal allograft rejection

    PubMed Central

    Thölking, Gerold; Schuette-Nuetgen, Katharina; Kentrup, Dominik; Pawelski, Helga; Reuter, Stefan

    2016-01-01

    Kidney transplantation is the best available treatment for patients with end stage renal disease. Despite the introduction of effective immunosuppressant drugs, episodes of acute allograft rejection still endanger graft survival. Since efficient treatment of acute rejection is available, rapid diagnosis of this reversible graft injury is essential. For diagnosis of rejection, invasive core needle biopsy of the graft is the “gold-standard”. However, biopsy carries the risk of significant graft injury and is not immediately feasible in patients taking anticoagulants. Therefore, a non-invasive tool assessing the whole organ for specific and fast detection of acute allograft rejection is desirable. We herein review current imaging-based state of the art approaches for non-invasive diagnostics of acute renal transplant rejection. We especially focus on new positron emission tomography-based as well as targeted ultrasound-based methods. PMID:27011915

  5. Classification models based on the level of metals in hair and nails of laryngeal cancer patients: diagnosis support or rather speculation?

    PubMed

    Golasik, Magdalena; Jawień, Wojciech; Przybyłowicz, Agnieszka; Szyfter, Witold; Herman, Małgorzata; Golusiński, Wojciech; Florek, Ewa; Piekoszewski, Wojciech

    2015-03-01

    The etiology of cancer is complex, and the disturbances in toxic and essential metals homeostasis are among many of the factors that lead to the development of malignancy. The aim of this study is to investigate the relationship between cancer risk and element status as well as cancer risk and external factors, such as diet, smoking and drinking habits, in order to support diagnosis of cancer. The samples of hair and nails obtained from patients with larynx cancer and healthy subjects were analyzed. Essential elements (Ca, Cr, Mg, Zn, Cu, Mn, and Fe), besides toxic metals (Cd, Co, and Pb), were determined using inductively coupled plasma atomic emission spectrometry (ICP-OES) and mass spectrometry (ICP-MS) techniques. The concentration of essential elements was from 1.5- (Zn) to 4.7-fold (Fe) higher in hair and from 2.4- to 3.3-fold higher in the nails of the control group compared to the patients, while the opposite trend was observed for the heavy metals. The differences between two groups in the level of metals (except for Zn) were statistically significant (p < 0.05). The association of cancer with metals and other factors was evaluated using various statistical methods, for which the best predictions were obtained using logistic regression, artificial neural networks and canonical discriminant analysis. The classifiers constructed using the data from a survey of diet and lifestyle, and analysis of elements in hair and nails, can be useful tools for estimating cancer risk and early screening of the disease. PMID:25616222

  6. Broken wires diagnosis method numerical simulation based on smart cable structure

    NASA Astrophysics Data System (ADS)

    Li, Sheng; Zhou, Min; Yang, Yan

    2014-12-01

    The smart cable with embedded distributed fiber optical Bragg grating (FBG) sensors was chosen as the object to study a new diagnosis method about broken wires of the bridge cable. The diagnosis strategy based on cable force and stress distribution state of steel wires was put forward. By establishing the bridge-cable and cable-steel wires model, the broken wires sample database was simulated numerically. A method of the characterization cable state pattern which can both represent the degree and location of broken wires inside a cable was put forward. The training and predicting results of the sample database by the back propagation (BP) neural network showed that the proposed broken wires diagnosis method was feasible and expanded the broken wires diagnosis research area by using the smart cable which was used to be only representing cable force.

  7. An Intelligent Learning Diagnosis System for Web-Based Thematic Learning Platform

    ERIC Educational Resources Information Center

    Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun

    2007-01-01

    This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to…

  8. A Cough-Based Algorithm for Automatic Diagnosis of Pertussis.

    PubMed

    Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

    2016-01-01

    Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523

  9. A hybrid MEMS-based microfluidic system for cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Ortiz, Pedro; Keegan, Neil; Spoors, Julia; Hedley, John; Harris, Alun; Burdess, Jim; Burnett, Richard; Velten, Thomas; Biehl, Margit; Knoll, Thorsten; Haberer, Werner; Solomon, Matthew; Campitelli, Andrew; McNeil, Calum

    2008-12-01

    A microfluidic system for cancer diagnosis based around a core MEMS biosensor technology is presented in this paper. The principle of the MEMS biosensor is introduced and the functionalisation strategy for cancer marker recognition is described. In addition, the successful packaging and integration of functional MEMS biosensor devices are reported herein. This ongoing work represents one of the first hybrid systems to integrate a PCB packaged silicon MEMS device into a disposable microfluidic cartridge.

  10. Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

    NASA Astrophysics Data System (ADS)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

    2008-03-01

    Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

  11. Automated medical diagnosis with fuzzy stochastic models: monitoring chronic diseases.

    PubMed

    Jeanpierre, Laurent; Charpillet, François

    2004-01-01

    As the world population ages, the patients per physician ratio keeps on increasing. This is even more important in the domain of chronic pathologies where people are usually monitored for years and need regular consultations. To address this problem, we propose an automated system to monitor a patient population, detecting anomalies in instantaneous data and in their temporal evolution, so that it could alert physicians. By handling the population of healthy patients autonomously and by drawing the physicians' attention to the patients-at-risk, the system allows physicians to spend comparatively more time with patients who need their services. In such a system, the interaction between the patients, the diagnosis module, and the physicians is very important. We have based this system on a combination of stochastic models, fuzzy filters, and strong medical semantics. We particularly focused on a particular tele-medicine application: the Diatelic Project. Its objective is to monitor chronic kidney-insufficient patients and to detect hydration troubles. During two years, physicians from the ALTIR have conducted a prospective randomized study of the system. This experiment clearly shows that the proposed system is really beneficial to the patients' health. PMID:15520535

  12. Development of fault diagnosis system for transformer based on multi-class support vector machines

    NASA Astrophysics Data System (ADS)

    Cao, Jian; Qian, Suxiang; Hu, Hongsheng; Yan, Gongbiao

    2007-12-01

    The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and having high generalization ability. It is strong to solve the problem with small sample, nonlinear and high dimension. The fundamental theory of DGA (Dissolved Gas Analysis, DGA) and fault characteristic of transformer is firstly researched in this paper, and then the disadvantages of traditional method of transformer fault diagnosis are analyzed, finally, a new fault diagnosis method using multi-class support vector machines (M-SVMs) based on DGA theory for transformer is put forward. Then the fault diagnosis model based on M-SVMs for transformer is established. At the same time, the fault diagnosis system based on M-SVMs for transformer is developed. The system can realize the acquisition of the dissolving gas in the transformer oil and data timely and low cost transmission by GPRS (General Packet Radio Service, GPRS). And it can identify out the transformer running state according to the acquisition data. The test results show that the method proposed has an excellent performance on correct ratio. And it can overcome the disadvantage of the traditional three-ratio method which lacks of fault coding and no fault types in the existent coding. Combining the wireless communication technology with the monitoring technology, the designed and developed system can greatly improve the real-time and continuity for the transformer' condition monitoring and fault diagnosis.

  13. A knowledge-based system for diagnosis of mastitis problems at the herd level. 1. Concepts.

    PubMed

    Hogeveen, H; Noordhuizen-Stassen, E N; Tepp, D M; Kremer, W D; van Vliet, J H; Brand, A

    1995-07-01

    Much specialized knowledge is involved in the diagnosis of a mastitis problem at the herd level. Because of their problem-solving capacities, knowledge-based systems can be very useful to support the diagnosis of mastitis problems in the herd. Conditional causal models with multiple layers are used as a representation scheme for the development of a knowledge-based system for diagnosing mastitis problems. Construction of models requires extensive cooperation between the knowledge engineer and the domain expert. The first layer consists of three overview models: the general overview conditional causal model, the contagious overview conditional causal model, and the environmental overview conditional causal model, giving a causal description of the pathways through which mastitis problems can occur. The conditional causal model for primary udder defense and the conditional causal model for host defense are attached to the overview models at the second layer, and the conditional causal model for deep primary udder defense is attached to the conditional causal model for the primary udder defense at the third layer. Based on quantitative user input, the system determines the qualitative values of the nodes that are used for reasoning. The developed models showed that conditional causal models are a good method for modeling the mechanisms involved in a mastitis problem. The system needs to be extended in order to be useful in practical circumstances. PMID:7593836

  14. Deep learning based syndrome diagnosis of chronic gastritis.

    PubMed

    Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

    2014-01-01

    In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:24734118

  15. Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

    PubMed Central

    Liu, Guo-Ping; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

    2014-01-01

    In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:24734118

  16. A hierarchical fuzzy rule-based approach to aphasia diagnosis.

    PubMed

    Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

    2007-10-01

    Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy. PMID:17293167

  17. PACS-Based Computer-Aided Detection and Diagnosis

    NASA Astrophysics Data System (ADS)

    Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

    The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

  18. BCC skin cancer diagnosis based on texture analysis techniques

    NASA Astrophysics Data System (ADS)

    Chuang, Shao-Hui; Sun, Xiaoyan; Chang, Wen-Yu; Chen, Gwo-Shing; Huang, Adam; Li, Jiang; McKenzie, Frederic D.

    2011-03-01

    In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% and 95%, respectively.

  19. Condition Monitoring and Fault Diagnosis of Wet-Shift Clutch Transmission Based on Multi-technology

    NASA Astrophysics Data System (ADS)

    Chen, Man; Wang, Liyong; Ma, Biao

    Based on the construction feature and operating principle of the wet-shift clutch transmission, the condition monitoring and fault diagnosis for the transmission of the tracklayer with wet-shift clutch were implemented with using the oil analysis technology, function parameter test method and vibration analysis technology. The new fault diagnosis methods were proposed, which are to build the gray modeling with the oil analysis data, and to test the function parameter of the clutch press, the rotate speed of each gear, the oil press of the steer system and lubrication system and the hydraulic torque converter. It's validated that the representative function signals were chosen to execute the condition monitoring analysis, when the fault symptoms were found, and the oil analysis data were used to apply the gray modeling to forecast the fault occurs time can satisfy the demand of the condition monitoring and fault diagnosis for the transmission regular work.

  20. Temporal reasoning for diagnosis in a causal probabilistic knowledge base.

    PubMed

    Long, W

    1996-07-01

    We have added temporal reasoning to the Heart Disease Program (HDP) to take advantage of the temporal constraints inherent in cardiovascular reasoning. Some processes take place over minutes while others take place over months or years and a strictly probabilistic formalism can generate hypotheses that are impossible given the temporal relationships involved. The HDP has temporal constraints on the causal relations specified in the knowledge base and temporal properties on the patient input provided by the user. These are used in two ways. First, they are used to constrain the generation of the pre-computed causal pathways through the model that speed the generation of hypotheses. Second, they are used to generate time intervals for the instantiated nodes in the hypotheses, which are matched and adjusted as nodes are added to each evolving hypothesis. This domain offers a number of challenges for temporal reasoning. Since the nature of diagnostic reasoning is inferring a causal explanation from the evidence, many of the temporal intervals have few constraints and the reasoning has to make maximum use of those that exist. Thus, the HDP uses a temporal interval representation that includes the earliest and latest beginning and ending specified by the constraints. Some of the disease states can be corrected but some of the manifestations may remain. For example, a valve disease such as aortic stenosis produces hypertrophy that remains long after the valve has been replaced. This requires multiple time intervals to account for the existing findings. This paper discusses the issues and solutions that have been developed for temporal reasoning integrated with a pseudo-Bayesian probabilistic network in this challenging domain for diagnosis. PMID:8830922

  1. Mutation-based prenatal diagnosis of Herlitz junctional epidermolysis bullosa.

    PubMed

    Christiano, A M; Pulkkinen, L; McGrath, J A; Uitto, J

    1997-04-01

    Epidermolysis bullosa (EB) is a group of heritable diseases which manifest with blistering and erosions of the skin and mucous membranes. Due of life-threatening complications and significant long-term morbidity associated with the severe, neonatal lethal (Herlitz) form of junctional EB (H-JEB), there has been a demand for prenatal diagnosis from families at risk for recurrence. Previously, the only reliable method of prenatal diagnosis of EB was a fetal skin biopsy performed at 16-20 weeks' gestation and analysed by electron microscopy. Recently, the genes LAMA3, LAMB3, and LAMC2, encoding the polypeptide subunits of laminin 5, an anchoring filament protein, have been shown to contain mutations in H-JEB. In this study, direct detection of pathogenetic mutations in the laminin 5 genes was used to perform polymerase chain reaction (PCR)-based prenatal testing. DNA was obtained by chorionic villus sampling (CVS) at 10-15 weeks or amniocentesis at 12-19 weeks' gestation in 15 families at risk for recurrence of JEB. In 13 cases, the fetus was predicted to be either genetically normal or a clinically unaffected carrier of a mutation in one allele. These predictions have been validated in all cases by the birth of a healthy child. In two cases, an affected fetus was predicted, and the diagnosis was confirmed by subsequent fetal skin biopsy. These results demonstrate that DNA-based prenatal testing offers an early, expedient, and accurate method of prenatal diagnosis or an exclusion of Herlitz JEB. PMID:9160387

  2. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

    PubMed

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  3. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    PubMed Central

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  4. Diagnosis and office-based treatment of urinary incontinence in adults. Part one: diagnosis and testing

    PubMed Central

    Heidelbaugh, Joel J.; Jimbo, Masahito

    2013-01-01

    Urinary incontinence is a common problem in both men and women. This review article addresses its prevalence, risk factors, cost, the various types of incontinence, as well as how to diagnose them. The US Preventive Services Task Force, the Cochrane Database of Systematic Reviews, and PubMed were reviewed for articles focusing on urinary incontinence. Incontinence is a common problem with a high societal cost. It is frequently underreported by patients so it is appropriate for primary-care providers to screen all women and older men during visits. A thorough history and physical examination combined with easy office-based tests can often yield a clear diagnosis and rule out other transient illnesses contributing to the incontinence. Specialist referral is occasionally needed in specific situations before embarking on a treatment plan. PMID:23904857

  5. Knowledge based acquisition of rules for medical diagnosis

    SciTech Connect

    Drastal, G.A.; Kulikowski, C.A.

    1982-01-01

    Medical consultation systems in the expert framework contain rules written under the guidance of expert physicians. The authors present a methodology and preliminary implementation of a system which learns compiled rule chains from positive case examples of a diagnostic class and negative examples of alternative diagnostic classes. Rule acquisition is guided by the constraints of physiological process models represented in the system. Evaluation of the system is proceeding in the area of glaucoma diagnosis, and an example of an experiment in this domain is included. 9 references.

  6. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    PubMed

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. PMID:24981891

  7. Medical Diagnosis System of Breast Cancer Using FCM Based Parallel Neural Networks

    NASA Astrophysics Data System (ADS)

    Hwang, Sang-Hyun; Kim, Dongwon; Kang, Tae-Koo; Park, Gwi-Tae

    In this paper, a new methodology for medical diagnosis based on fuzzy clustering and parallel neural networks is proposed. Intelligent systems have various fields. Breast cancer is one of field to be targeted, which is the most common tumor-related disease among women. Diagnosis of breast cancer is not task for medical expert owing to many attributes of the disease. So we proposed a new method, FCM based parallel neural networks to handle difficult. FCM based parallel neural networks composed of two parts. One is classifying breast cancer data using Fuzzy c-means clustering method (FCM). The other is designing the multiple neural networks using classified data by FCM. The proposed methodology is experimented, evaluated, and compared the performance with other existed models. As a result we can show the effectiveness and precision of the proposed method are better than other previous models.

  8. A Novel Diagnosis Method for a Hall Plates-Based Rotary Encoder with a Magnetic Concentrator

    PubMed Central

    Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

    2014-01-01

    In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly. PMID:25090417

  9. A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

    PubMed

    Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

    2014-01-01

    In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly. PMID:25090417

  10. Self-calibrating models for dynamic monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1994-01-01

    The present goal in qualitative reasoning is to develop methods for automatically building qualitative and semiquantitative models of dynamic systems and to use them for monitoring and fault diagnosis. The qualitative approach to modeling provides a guarantee of coverage while our semiquantitative methods support convergence toward a numerical model as observations are accumulated. We have developed and applied methods for automatic creation of qualitative models, developed two methods for obtaining tractable results on problems that were previously intractable for qualitative simulation, and developed more powerful methods for learning semiquantitative models from observations and deriving semiquantitative predictions from them. With these advances, qualitative reasoning comes significantly closer to realizing its aims as a practical engineering method.

  11. Data mining and model simplicity: A case study in diagnosis

    SciTech Connect

    Provan, G.M.; Singh, M.

    1996-12-31

    We describe the results of performing data mining on a challenging medical diagnosis domain, acute abdominal pain. This domain is well known to be difficult, yielding little more than 60% predictive accuracy for most human and machine diagnosticians. Moreover, many researchers argue that one of the simplest approaches, the naive Bayesian classifier, is optimal. By comparing the performance of the naive Bayesian classifier to its more general cousin, the Bayesian network classifier, and to selective Bayesian classifiers with just 10% of the total attributes, we show that the simplest models perform at least as well as the more complex models. We argue that simple models like the selective naive Bayesian classifier will perform as well as more complicated models for similarly complex domains with relatively small data sets, thereby calling into question the extra expense necessary to induce more complex models.

  12. Teaching medical diagnosis: a rule-based approach.

    PubMed

    Michalowski, W; Rubin, S; Aggarwal, H

    1993-01-01

    This paper discusses the design of a diagnostic process simulator which teaches medical students to think clinically. This was possible to achieve due to the application of a rule-based approach to represent diagnosis and treatments. Whilst using the simulator, as a result of the student's incorrect and correct decisions, the clinical situation changes accordingly. New diagnostic options result in the ability to choose further clinical and laboratory tests. The simulator is being implemented on Sun workstations and Macintosh computers using Prolog programming language. PMID:8139404

  13. Tumors of the cranial base: Diagnosis and treatment

    SciTech Connect

    Sekhar, L.N.; Schramm, V.L.

    1987-01-01

    The first section of this book highlights the differences and similarities in the pathology and biology of the various types of neoplasms of the cranial base. The second section covers improvements in radiological diagnosis with the advent of computed tomography, magnetic resonance imaging and a better knowledge of radiological anatomy. It also examines the significance and proper evaluation of minor symptoms to enable earlier diagnosis, as well as the advances in interventional radiology that have produced the balloon occlusion text and tumor embolization. Section three is on advanced neuroanesthetic techniques and intraoperative neurophysiological monitoring. Section four describes specialized treatment modalities including microsurgical resection with the laser, radiation therapy and chemotherapy. Section five reviews the latest techniques for reconstruction of the cranial base following resection, as well as the preservation and reconstruction of cranial nerves and cerebral blood vessels exposed during the surgery. The final three sections examine the lesions and surgical techniques specific to the different anatomical regions, i.e, the anterior, middle and posterior cranial base.

  14. An Expert Fitness Diagnosis System Based on Elastic Cloud Computing

    PubMed Central

    Tseng, Kevin C.; Wu, Chia-Chuan

    2014-01-01

    This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service. PMID:24723842

  15. Cancer Mortality in People Treated with Antidepressants before Cancer Diagnosis: A Population Based Cohort Study

    PubMed Central

    Sun, Yuelian; Vedsted, Peter; Fenger-Grøn, Morten; Wu, Chun Sen; Bech, Bodil Hammer; Olsen, Jørn; Benros, Michael Eriksen; Vestergaard, Mogens

    2015-01-01

    Background Depression is common after a cancer diagnosis and is associated with an increased mortality, but it is unclear whether depression occurring before the cancer diagnosis affects cancer mortality. We aimed to study cancer mortality of people treated with antidepressants before cancer diagnosis. Methods and Findings We conducted a population based cohort study of all adults diagnosed with cancer between January 2003 and December 2010 in Denmark (N = 201,662). We obtained information on cancer from the Danish Cancer Registry, on the day of death from the Danish Civil Registry, and on redeemed antidepressants from the Danish National Prescription Registry. Current users of antidepressants were defined as those who redeemed the latest prescription of antidepressant 0–4 months before cancer diagnosis (irrespective of earlier prescriptions), and former users as those who redeemed the latest prescription five or more months before cancer diagnosis. We estimated an all-cause one-year mortality rate ratio (MRR) and a conditional five-year MRR for patients who survived the first year after cancer diagnosis and confidence interval (CI) using a Cox proportional hazards regression model. Overall, 33,111 (16.4%) patients redeemed at least one antidepressant prescription in the three years before cancer diagnosis of whom 21,851 (10.8%) were current users at the time of cancer diagnosis. Current antidepressant users had a 32% higher one-year mortality (MRR = 1.32, 95% CI: 1.29–1.35) and a 22% higher conditional five-year mortality (MRR = 1.22, 95% CI: 1.17–1.26) if patients survived the first year after the cancer diagnosis than patients not redeeming antidepressants. The one-year mortality was particularly high for patients who initiated antidepressant treatment within four months before cancer diagnosis (MRR = 1.54, 95% CI: 1.47–1.61). Former users had no increased cancer mortality. Conclusions Initiation of antidepressive treatment prior to cancer diagnosis is

  16. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    PubMed

    Yin, Hong; Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  17. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    PubMed Central

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  18. Autoregressive modelling for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, H.; Trendafilova, I.

    2015-07-01

    In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

  19. A diagnosis system using object-oriented fault tree models

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A fault diagnosis algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented fault trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a fault tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this diagnosis system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the fault tree. The diagnosis system has been implemented in common LISP using Flavors.

  20. Microfluidic chip-based technologies: emerging platforms for cancer diagnosis

    PubMed Central

    2013-01-01

    The development of early and personalized diagnostic protocols is considered the most promising avenue to decrease mortality from cancer and improve outcome. The emerging microfluidic-based analyzing platforms hold high promises to fulfill high-throughput and high-precision screening with reduced equipment cost and low analysis time, as compared to traditional bulky counterparts in bench-top laboratories. This article overviewed the potential applications of microfluidic technologies for detection and monitoring of cancer through nucleic acid and protein biomarker analysis. The implications of the technologies in cancer cytology that can provide functional personalized diagnosis were highlighted. Finally, the future niches for using microfluidic-based systems in tumor screening were briefly discussed. PMID:24070124

  1. Sensor fault diagnosis for fast steering mirror system based on Kalman filter

    NASA Astrophysics Data System (ADS)

    Wang, Hongju; Bao, Qiliang; Yang, Haifeng; Tao, Sunjie

    2015-10-01

    In this paper, to improve the reliability of a two-axis fast steering mirror system with minimum hardware consumption, a fault diagnosis method based on Kalman filter was developed. The dynamics model of the two-axis FSM was established firstly, and then the state-space form of the FSM was adopted. A bank of Kalman filters for fault detection was designed based on the state-space form. The effects of the sensor faults on the innovation sequence were investigated, and a decision approach called weighted sum-squared residual (WSSR) was adopted to isolate the sensor faults. Sensor faults could be detected and isolated when the decision statistics changed. Experimental studies on a prototype system show that the faulty sensor can be isolated timely and accurately. Meanwhile, the mathematical model of FSM system was used to design fault diagnosis scheme in the proposed method, thus the consumption of the hardware and space is decreased.

  2. Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Mengshoel, Ole

    2008-01-01

    Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

  3. Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Damour, Cédric; Benne, Michel; Grondin-Perez, Brigitte; Bessafi, Miloud; Hissel, Daniel; Chabriat, Jean-Pierre

    2015-12-01

    Diagnosis tool for water management is relevant to improve the reliability and lifetime of polymer electrolyte membrane fuel cells (PEMFCs). This paper presents a novel signal-based diagnosis approach, based on Empirical Mode Decomposition (EMD), dedicated to PEMFCs. EMD is an empirical, intuitive, direct and adaptive signal processing method, without pre-determined basis functions. The proposed diagnosis approach relies on the decomposition of FC output voltage to detect and isolate flooding and drying faults. The low computational cost of EMD, the reduced number of required measurements, and the high diagnosis accuracy of flooding and drying faults diagnosis make this approach a promising online diagnosis tool for PEMFC degraded modes management.

  4. NGS-based Molecular diagnosis of 105 eyeGENE® probands with Retinitis Pigmentosa

    PubMed Central

    Ge, Zhongqi; Bowles, Kristen; Goetz, Kerry; Scholl, Hendrik P. N.; Wang, Feng; Wang, Xinjing; Xu, Shan; Wang, Keqing; Wang, Hui; Chen, Rui

    2015-01-01

    The National Ophthalmic Disease Genotyping and Phenotyping Network (eyeGENE®) was established in an effort to facilitate basic and clinical research of human inherited eye disease. In order to provide high quality genetic testing to eyeGENE®’s enrolled patients which potentially aids clinical diagnosis and disease treatment, we carried out a pilot study and performed Next-generation sequencing (NGS) based molecular diagnosis for 105 Retinitis Pigmentosa (RP) patients randomly selected from the network. A custom capture panel was designed, which incorporated 195 known retinal disease genes, including 61 known RP genes. As a result, disease-causing mutations were identified in 52 out of 105 probands (solving rate of 49.5%). A total of 82 mutations were identified, and 48 of them were novel. Interestingly, for three probands the molecular diagnosis was inconsistent with the initial clinical diagnosis, while for five probands the molecular information suggested a different inheritance model other than that assigned by the physician. In conclusion, our study demonstrated that NGS target sequencing is efficient and sufficiently precise for molecular diagnosis of a highly heterogeneous patient cohort from eyeGENE®. PMID:26667666

  5. Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

    NASA Astrophysics Data System (ADS)

    Zhang, Han

    Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

  6. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

    PubMed Central

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

  7. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    PubMed

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

  8. Wavelet-Based Real-Time Diagnosis of Complex Systems

    NASA Technical Reports Server (NTRS)

    Gulati, Sandeep; Mackey, Ryan

    2003-01-01

    A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning.

  9. Self-diagnosis of smart structures based on dynamical properties

    NASA Astrophysics Data System (ADS)

    Fritzen, C.-P.; Kraemer, P.

    2009-08-01

    When we talk about "smart structures" we can think about different properties and capabilities which make a structure "intelligent" in a certain sense. Originally, the expression "smart" was used in the context that a structure can react and adapt to certain environmental conditions, such as change of shape, compensation of deformations, active vibration damping, etc. Over the last year, the expression "smart" has been extended to the field of structural health monitoring (SHM), where sensor networks, actuators and computational capabilities are used to enable a structure to perform a self-diagnosis with the goal that this structure can release early warnings about a critical health state, locate and classify damage or even to forecast the remaining life-time. This paper intends to give an overview and point out recent developments of vibration-based methods for SHM. All these methods have in common that a structural change due to a damage results in a more or less significant change of the dynamic behavior. For the diagnosis an inverse problem has to be solved. We discuss the use of modal information as well as the direct use of forced and ambient vibrations in the time and frequency domain. Examples from civil and aerospace engineering as well as off-shore wind energy plants show the applicability of these methods.

  10. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    DOE PAGESBeta

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

  11. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    SciTech Connect

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issue is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.

  12. Physiology-based diagnosis algorithm for arteriovenous fistula stenosis detection.

    PubMed

    Yeih, Dong-Feng; Wang, Yuh-Shyang; Huang, Yi-Chun; Chen, Ming-Fong; Lu, Shey-Shi

    2014-01-01

    In this paper, a diagnosis algorithm for arteriovenous fistula (AVF) stenosis is developed based on auscultatory features, signal processing, and machine learning. The AVF sound signals are recorded by electronic stethoscopes at pre-defined positions before and after percutaneous transluminal angioplasty (PTA) treatment. Several new signal features of stenosis are identified and quantified, and the physiological explanations for these features are provided. Utilizing support vector machine method, an average of 90% two-fold cross-validation hit-rate can be obtained, with angiography as the gold standard. This offers a non-invasive easy-to-use diagnostic method for medical staff or even patients themselves for early detection of AVF stenosis. PMID:25571021

  13. Diagnosis of Dengue Infection Using Conventional and Biosensor Based Techniques.

    PubMed

    Parkash, Om; Shueb, Rafidah Hanim

    2015-10-01

    Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed. PMID:26492265

  14. Diagnosis of Dengue Infection Using Conventional and Biosensor Based Techniques

    PubMed Central

    Parkash, Om; Hanim Shueb, Rafidah

    2015-01-01

    Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed. PMID:26492265

  15. Computer-based assessment for facioscapulohumeral dystrophy diagnosis.

    PubMed

    Chambers, O; Milenković, J; Pražnikar, A; Tasič, J F

    2015-06-01

    The paper presents a computer-based assessment for facioscapulohumeral dystrophy (FSHD) diagnosis through characterisation of the fat and oedema percentages in the muscle region. A novel multi-slice method for the muscle-region segmentation in the T1-weighted magnetic resonance images is proposed using principles of the live-wire technique to find the path representing the muscle-region border. For this purpose, an exponential cost function is used that incorporates the edge information obtained after applying the edge-enhancement algorithm formerly designed for the fingerprint enhancement. The difference between the automatic segmentation and manual segmentation performed by a medical specialists is characterised using the Zijdenbos similarity index, indicating a high accuracy of the proposed method. Finally, the fat and oedema are quantified from the muscle region in the T1-weighted and T2-STIR magnetic resonance images, respectively, using the fuzzy c-mean clustering approach for 10 FSHD patients. PMID:25910520

  16. Computational complexity issues in operative diagnosis of graph-based systems

    SciTech Connect

    Rao, N.S.V. )

    1993-04-01

    Systems that can be modeled as graphs, such that nodes represent the components and the edges represent the fault propagation between the components, are considered. Some components are equipped with alarms that ring in response to faulty conditions. In these systems, two types of problems are studied: (a) fault diagnosis, and (b) alarm placement. The fault diagnosis problems deal with computing the set of all potential failure sources that correspond to a set of ringing alarms A[sub R]. First, the single faults, where exactly one component can become faulty at any time, are considered. Systems are classified into zero-time and nonzero-time systems based on fault propagation times, and the latter is further classified based on the knowledge of propagation times. For each of these classes algorithms are presented for single fault diagnosis. The problem of detecting multiple faults is shown to be NP-complete. An alarm placement problem, that requires a single fault to be uniquely diagnosed, is examined; various versions of this problem are shown to be NP-complete. The single fault diagnosis algorithms have been implemented and tested.

  17. Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

    PubMed

    Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe

    2011-12-01

    The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method. PMID:21954208

  18. A Clinical Model for the Diagnosis and Management of Patients with Cervical Spine Syndromes

    PubMed Central

    Murphy, Donald R.

    2004-01-01

    Background: Disorders of the cervical spine are common and often disabling. The etiology of these disorders is often multifactorial and a comprehensive approach to both diagnosis and management is essential to successful resolution. Objective: This article provides an overview of a clinical model of the diagnosis and management of patients with disorders related to the cervical spine. This model is based in part on the scientific literature, clinical experience, and communication with other practitioners over the course of the past 20 years. Discussion: The clinical model presented here involves taking a systematic approach to diagnosis, and management. The diagnostic process is one that asks three essential questions. The answers to these questions then guides the management process, allowing the physician to apply specific methods that address the many factors that can be involved in each individual patient. This clinical model allows the physician to individualize the management strategy while utilizing principles that can be applied to all patients. At times, the management strategy must be multidisciplinary, and cooperation with other physicians and therapists is often necessary for effective patient care. This model is currently being used by the author in practice, as well as forming the basis upon which further research can be conducted to refine or, if necessary, abandon any of its aspects, as the evidence dictates. It is the purpose of this paper to present this clinical model and the clinical and scientific evidence, or lack thereof, of its components. PMID:17987214

  19. Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Jiang, Huiming; Chen, Jin; Dong, Guangming; Liu, Tao; Chen, Gang

    2015-02-01

    Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV-NSVR. Combined with selected SV-NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification. Then the SV-NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings.

  20. Microchip-based Devices for Molecular Diagnosis of Genetic Diseases.

    PubMed

    Cheng; Fortina; Surrey; Kricka; Wilding

    1996-09-01

    Microchips, constructed with a variety of microfabrication technologies (photolithography, micropatterning, microjet printing, light-directed chemical synthesis, laser stereochemical etching, and microcontact printing) are being applied to molecular biology. The new microchip-based analytical devices promise to solve the analytical problems faced by many molecular biologists (eg, contamination, low throughput, and high cost). They may revolutionize molecular biology and its application in clinical medicine, forensic science, and environmental monitoring. A typical biochemical analysis involves three main steps: (1) sample preparation, (2) biochemical reaction, and (3) detection (either separation or hybridization may be involved) accompanied by data acquisition and interpretation. The construction of a miniturized analyzer will therefore necessarily entail the miniaturization and integration of all three of these processes. The literature related to the miniaturization of these three processes indicates that the greatest emphasis so far is on the investigation and development of methods for the detection of nucleic acid, followed by the optimization of a biochemical reaction, such as the polymerase chain reaction. The first step involving sample preparation has received little attention. In this review the state of the art of, microchip-based, miniaturized analytical processes (eg, sample preparation, biochemical reaction, and detection of products) are outlined and the applications of microchip-based devices in the molecular diagnosis of genetic diseases are discussed. PMID:10462559

  1. Diagnosis of Parkinsonian disorders using a channelized Hotelling observer model: Proof of principle

    SciTech Connect

    Bal, H.; Bal, G.; Acton, P. D.

    2007-10-15

    Imaging dopamine transporters using PET and SPECT probes is a powerful technique for the early diagnosis of Parkinsonian disorders. In order to perform automated accurate diagnosis of these diseases, a channelized Hotelling observer (CHO) based model was developed and evaluated using the SPECT tracer [Tc-99m]TRODAT-1. Computer simulations were performed using a digitized striatal phantom to characterize early stages of the disease (20 lesion-present cases with varying lesion size and contrast). Projection data, modeling the effects of attenuation and geometric response function, were obtained for each case. Statistical noise levels corresponding to those observed clinically were added to the projection data to obtain 100 noise realizations for each case. All the projection data were reconstructed, and a subset of the transaxial slices containing the striatum was summed and used for further analysis. CHO models, using the Laguerre-Gaussian functions as channels, were designed for two cases: (1) By training the model using individual lesion-present samples and (2) by training the model using pooled lesion-present samples. A decision threshold obtained for each CHO model was used to classify the study population (n=40). It was observed that individual lesion trained CHO models gave high diagnostic accuracy for lesions that were larger than those used to train the model and vice-versa. On the other hand, the pooled CHO model was found to give a high diagnostic accuracy for all the lesion cases (average diagnostic accuracy=0.95{+-}0.07; p<0.0001 Fisher's exact test). Based on our results, we conclude that a CHO model has the potential to provide early and accurate diagnosis of Parkinsonian disorders, thereby improving patient management.

  2. A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer

    PubMed Central

    Si, Hongzong; Liu, Shihai; Li, Xianchao; Gao, Caihong; Cui, Lianhua; Li, Chuan; Yang, Xue; Yao, Xiaojun

    2015-01-01

    Background Lung cancer is an important and common cancer that constitutes a major public health problem, but early detection of small cell lung cancer can significantly improve the survival rate of cancer patients. A number of serum biomarkers have been used in the diagnosis of lung cancers; however, they exhibit low sensitivity and specificity. Methods We used biochemical methods to measure blood levels of lactate dehydrogenase (LDH), C-reactive protein (CRP), Na+, Cl-, carcino-embryonic antigen (CEA), and neuron specific enolase (NSE) in 145 small cell lung cancer (SCLC) patients and 155 non-small cell lung cancer and 155 normal controls. A gene expression programming (GEP) model and Receiver Operating Characteristic (ROC) curves incorporating these biomarkers was developed for the auxiliary diagnosis of SCLC. Results After appropriate modification of the parameters, the GEP model was initially set up based on a training set of 115 SCLC patients and 125 normal controls for GEP model generation. Then the GEP was applied to the remaining 60 subjects (the test set) for model validation. GEP successfully discriminated 281 out of 300 cases, showing a correct classification rate for lung cancer patients of 93.75% (225/240) and 93.33% (56/60) for the training and test sets, respectively. Another GEP model incorporating four biomarkers, including CEA, NSE, LDH, and CRP, exhibited slightly lower detection sensitivity than the GEP model, including six biomarkers. We repeat the models on artificial neural network (ANN), and our results showed that the accuracy of GEP models were higher than that in ANN. GEP model incorporating six serum biomarkers performed by NSCLC patients and normal controls showed low accuracy than SCLC patients and was enough to prove that the GEP model is suitable for the SCLC patients. Conclusion We have developed a GEP model with high sensitivity and specificity for the auxiliary diagnosis of SCLC. This GEP model has the potential for the wide use

  3. Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Templin, Jonathan L.; Henson, Robert A.; Templin, Sara E.; Roussos, Louis

    2008-01-01

    Several types of parameterizations of attribute correlations in cognitive diagnosis models use the reduced reparameterized unified model. The general approach presumes an unconstrained correlation matrix with K(K - 1)/2 parameters, whereas the higher order approach postulates K parameters, imposing a unidimensional structure on the correlation…

  4. Computer-aided diagnosis workstation and database system for chest diagnosis based on multihelical CT images

    NASA Astrophysics Data System (ADS)

    Sato, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Moriyama, Noriyuki; Ohmatsu, Hironobu; Kakinuma, Ryutaro; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

    2004-04-01

    Lung cancer is the most common cause, accounting for about 20% of all cancer deaths for males in Japan. Myocardial infarction is also known as a most fearful adult disease. Recently, multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for screening examination. This screening examination requires a considerable number of images to be read. It is this time-consuming step that makes the use of multi-helical CT for mass screening. To overcome this problem, our group has developed a computer-aided diagnosis algorithm to automatically detect suspicious regions of lung cancer and coronary calcifications in chest CT images, so far. And in this time, our group has developed a newly computer-aided diagnosis workstation and database. These consist in three. First, it is an image processing system to automatically detect suspicious bronchial regions, pulmonary artery regions, plumonary vein regions and myocardial infarction regions at high speed. Second, they are two 1600 x 1200 matrix black and white liquid crystal monitor. Third, it is a terminal of image storage. These are connected mutually on the network. This makes it much easier to read images, since the 3D image of suspicious regions and shadow of suspicious regions can be displayed simultaneously on two 1600 x 1200 matrix liquid crystal monitor. The experimental results indicate that a newly computer-aided diagnosis workstation and database system can be effectively used in clinical practice to increase the speed and accuracy of routine diagnosis.

  5. Application of a diagnosis-based clinical decision guide in patients with neck pain

    PubMed Central

    2011-01-01

    Background Neck pain (NP) is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with NP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of NP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 95 patients. Signs of visceral disease or potentially serious illness were found in 1%. Centralization signs were found in 27%, segmental pain provocation signs were found in 69% and radicular signs were found in 19%. Clinically relevant myofascial signs were found in 22%. Dynamic instability was found in 40%, oculomotor dysfunction in 11.6%, fear beliefs in 31.6%, central pain hypersensitivity in 4%, passive coping in 5% and depression in 2%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, oculomotor dysfunction, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability, validity and efficacy of treatment based on the DBCDG. PMID:21871119

  6. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1996-01-01

    Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

  7. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    PubMed

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis. PMID:25993810

  8. Human immunodeficiency virus testing for patient-based and population-based diagnosis.

    PubMed

    Albritton, W L; Vittinghoff, E; Padian, N S

    1996-10-01

    Laboratory testing for human immunodeficiency virus (HIV) has been introduced for individual patient-based diagnosis as well as high-risk and low-risk population-based screening. The choice of test, confirmatory algorithm, and interpretative criteria used depend on the clinical setting. In the context of general population-based testing, factors affecting test performance will have to be considered carefully in the development of testing policy. PMID:8843247

  9. Applied Neuroimaging to the Diagnosis of Alzheimer's Disease: A Multicriteria Model

    NASA Astrophysics Data System (ADS)

    Tamanini, Isabelle; de Castro, Ana Karoline; Pinheiro, Plácido Rogério; Pinheiro, Mirian Calíope Dantas

    In the last few years, Alzheimer's disease has been the most frequent cause of dementia and it is responsible, alone or in association with other diseases, for 50% of the cases in western countries. Dementias are syndromes characterized by a decline in memory and other neuropsychological changes, especially occurring in elderly people and increasing exponentially along the aging process. The main focus of this work is to develop a multicriteria model for aiding in decision making on the diagnosis of Alzheimer's disease by using the Aranau Tool, structured on the Verbal Decision Analysis. In this work, the modeling and evaluation processes were conducted with the aid of a medical expert, bibliographic sources and questionnaires. The questionnaires taken into account were based mainly on patients' neuroimaging tests, and we analyzed wheter or not there were problems in the patients' brain that could be relevant to the diagnosis of Alzheimer's disease.

  10. Specific contrast ultrasound using sterically stabilized microbubbles for early diagnosis of thromboembolic disease in a rabbit model

    PubMed Central

    Vlašín, Michal; Lukáč, Robert; Kauerová, Zuzana; Kohout, Pavel; Mašek, Josef; Bartheldyová, Eliška; Koudelka, Štěpán; Korvasová, Zina; Plocková, Jana; Hronová, Nikola; Turánek, Jaroslav

    2014-01-01

    Specific contrast ultrasound is widely applied in diagnostic procedures on humans but remains underused in veterinary medicine. The objective of this study was to evaluate the use of microbubble-based contrast for rapid ultrasonographic diagnosis of thrombosis in small animals, using male New Zealand white rabbits (average weight about 3.5 kg) as a model. It was hypothesized that the use of microbubble-based contrast agents will result in a faster and more precise diagnosis in our model of thrombosis. A pro-coagulant environment had been previously established by combining endothelial denudation and external vessel wall damage. Visualization of thrombi was achieved by application of contrast microbubbles [sterically stabilized, phospholipid-based microbubbles filled with sulfur hexafluoride (SF6) gas] and ultrasonography. As a result, rapid and clear diagnosis of thrombi in aorta abdominalis was achieved within 10 to 30 s (mean: 17.3 s) by applying microbubbles as an ultrasound contrast medium. In the control group, diagnosis was not possible or took 90 to 180 s. Therefore, sterically stabilized microbubbles were found to be a suitable contrast agent for the rapid diagnosis of thrombi in an experimental model in rabbits. This contrast agent could be of practical importance in small animal practice for rapid diagnosis of thrombosis. PMID:24688175

  11. Shape-based diagnosis of the aortic valve

    NASA Astrophysics Data System (ADS)

    Ionasec, Razvan Ioan; Tsymbal, Alexey; Vitanovski, Dime; Georgescu, Bogdan; Zhou, S. Kevin; Navab, Nassir; Comaniciu, Dorin

    2009-02-01

    Disorders of the aortic valve represent a common cardiovascular disease and an important public-health problem worldwide. Pathological valves are currently determined from 2D images through elaborate qualitative evalu- ations and complex measurements, potentially inaccurate and tedious to acquire. This paper presents a novel diagnostic method, which identies diseased valves based on 3D geometrical models constructed from volumetric data. A parametric model, which includes relevant anatomic landmarks as well as the aortic root and lea ets, represents the morphology of the aortic valve. Recently developed robust segmentation methods are applied to estimate the patient specic model parameters from end-diastolic cardiac CT volumes. A discriminative distance function, learned from equivalence constraints in the product space of shape coordinates, determines the corresponding pathology class based on the shape information encoded by the model. Experiments on a heterogeneous set of 63 patients aected by various diseases demonstrated the performance of our method with 94% correctly classied valves.

  12. Crack diagnosis of metallic profiles based on structural damage indicators

    NASA Astrophysics Data System (ADS)

    Preisler, A.; Steenbock, C.; Schröder, K.-U.

    2015-07-01

    Structural Health Monitoring (SHM) faces several challenges before large-scale industrial application. First of all damage diagnosis has to be reliable. Therefore, common SHM approaches use highly advanced sensor techniques to monitor the whole structure on all possible failures. This results in an enormous amount of data gathered during service. The general effort can be drastically reduced, if the knowledge achieved during the sizing process is used. During sizing, potential failure modes and critical locations, so called hot spots, are already evaluated. A very sensitive SHM system can be developed, when the monitoring effort shifts from the damage to its impact on the structural behaviour and the so called damage indicators. These are the two main components of the SmartSHM approach, which reduces the monitoring effort significantly. Not only the amount of data is minimized, but also reliability and robustness are ensured by the SmartSHM approach. This contribution demonstrates the SmartSHM approach by a cracked four point bending beam. To show general applicability a parametric study considering different profiles (bar, box, I, C, T, L, Z), crack positions and lengths has been performed. Questions of sensitivity and minimum size of the sensor network are discussed based on the results of the parametric study.

  13. Recent advances in biosensor based diagnosis of urinary tract infection.

    PubMed

    Kumar, M S; Ghosh, S; Nayak, S; Das, A P

    2016-06-15

    Urinary tract infections (UTIs) are potentially life threatening infections that are associated with high rates of incidence, recurrence and mortality. UTIs are characterized by several chronic infections which may lead to lethal consequences if left undiagnosed and untreated. The uropathogens are consistent across the globe. The most prevalent uropathogenic gram negative bacteria are Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa, Klebsiella pneumonia. Early detection and precise diagnosis of these infections will play a pivotal role in health care, pharmacological and biomedical sectors. A number of detection methods are available but their performances are not upto the mark. Therefore a more rapid, selective and highly sensitive technique for the detection and quantification of uropathogen levels in extremely minute concentrations need of the time. This review brings all the major concerns of UTI at one's doorstep such as clinical costs and incidence rate, several diagnostic approaches along with their advantages and disadvantages. Paying attention to detection approaches with emphasizing biosensor based recent developments in the quest for new diagnostics for UTI and the need for more sophisticated techniques in terms of selectivity and sensitivity is discussed. PMID:26890825

  14. Biophotonics in diagnosis and modeling of tissue pathologies

    NASA Astrophysics Data System (ADS)

    Serafetinides, A. A.; Makropoulou, M.; Drakaki, E.

    2008-12-01

    Biophotonics techniques are applied to several fields in medicine and biology. The laser based techniques, such as the laser induced fluorescence (LIF) spectroscopy and the optical coherence tomography (OCT), are of particular importance in dermatology, where the laser radiation could be directly applied to the tissue target (e.g. skin). In addition, OCT resolves architectural tissue properties that might be useful as tumour discrimination parameters for skin as well as for ocular non-invasive visualization. Skin and ocular tissues are complex multilayered and inhomogeneous organs with spatially varying optical properties. This fact complicates the quantitative analysis of the fluorescence and/or light scattering spectra, even from the same tissue sample. To overcome this problem, mathematical simulation is applied for the investigation of the human tissue optical properties, in the visible/infrared range of the spectrum, resulting in a better discrimination of several tissue pathologies. In this work, we present i) a general view on biophotonics applications in diagnosis of human diseases, ii) some specific results on laser spectroscopy techniques, as LIF measurements, applied in arterial and skin pathologies and iii) some experimental and theoretical results on ocular OCT measurements. Regarding the LIF spectroscopy, we examined the autofluorescence properties of several human skin samples, excised from humans undergoing biopsy examination. A nitrogen laser was used as an excitation source, emitting at 337 nm (ultraviolet excitation). Histopathology examination of the samples was also performed, after the laser spectroscopy measurements and the results from the spectroscopic and medical analysis were compared, to differentiate malignancies, e.g. basal cell carcinoma tissue (BCC), from normal skin tissue. Regarding the OCT technique, we correlated human data, obtained from patients undergoing OCT examination, with Monte Carlo simulated cornea and retina tissues

  15. Fault diagnosis for stator winding bar hollow strand blockage of turbogenerators based on data fusion

    NASA Astrophysics Data System (ADS)

    Wang, Xianpei; Dai, Zheng Y.; Liu, Zhenxing; Chen, Yalin

    2003-09-01

    Stator Winding Bar Hollow Strand Blockage (SWBHSB) is one of the main faults for large turbo-generators with water and hydrogen cooling system. It will lead to increasing water temperature at the bar exit which may cause hidden troubles for turbo-generator's security. According to a three-layer-structural model of data fusion, this paper presents a fault diagnosis method for turbo-generators based on data fusion technology. Firstly, a bp network on pixel level fusion is set up, in which several temperature parameters at the bar exit are accurately computed. Then in feature level fusion, the fingerprints are distilled from the result of pixel level fusion. Finally, decision level fusion gives a fault diagnosis for the measuring channels and thermometric components. This method can effectively avoid problems such as misinformation and fake report.

  16. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    PubMed

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-01-01

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

  17. Evidenced-based review of clinical studies on endodontic diagnosis.

    PubMed

    2009-08-01

    The practice of endodontics requires excellence in diagnostic skills. The importance of this topic has been underscored by a recent 2008 AAE-sponsored symposium on endodontic diagnosis, which will be highlighted in a special issue of the Journal of Endodontics. In this minireview, we focus on recent clinical studies that emphasize different aspects related to the diagnosis of disorders of the pulp-dentin complex. PMID:19631854

  18. Fault diagnosis in spur gears based on genetic algorithm and random forest

    NASA Astrophysics Data System (ADS)

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  19. Microwave-based medical diagnosis using particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Modiri, Arezoo

    This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level

  20. Comparison of Predictive Models for the Early Diagnosis of Diabetes

    PubMed Central

    Jahani, Meysam

    2016-01-01

    Objectives This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). Results The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. Conclusions The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes. PMID:27200219

  1. A Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning to Support Cancer Diagnosis: A Pilot Study.

    PubMed

    Saraiva, Renata M; Bezerra, João; Perkusich, Mirko; Almeida, Hyggo; Siebra, Clauirton

    2015-01-01

    Recently there has been an increasing interest in applying information technology to support the diagnosis of diseases such as cancer. In this paper, we present a hybrid approach using case-based reasoning (CBR) and rule-based reasoning (RBR) to support cancer diagnosis. We used symptoms, signs, and personal information from patients as inputs to our model. To form specialized diagnoses, we used rules to define the input factors' importance according to the patient's characteristics. The model's output presents the probability of the patient having a type of cancer. To carry out this research, we had the approval of the ethics committee at Napoleão Laureano Hospital, in João Pessoa, Brazil. To define our model's cases, we collected real patient data at Napoleão Laureano Hospital. To define our model's rules and weights, we researched specialized literature and interviewed health professional. To validate our model, we used K-fold cross validation with the data collected at Napoleão Laureano Hospital. The results showed that our approach is an effective CBR system to diagnose cancer. PMID:26262174

  2. Assessment of Differential Item Functioning under Cognitive Diagnosis Models: The DINA Model Example

    ERIC Educational Resources Information Center

    Li, Xiaomin; Wang, Wen-Chung

    2015-01-01

    The assessment of differential item functioning (DIF) is routinely conducted to ensure test fairness and validity. Although many DIF assessment methods have been developed in the context of classical test theory and item response theory, they are not applicable for cognitive diagnosis models (CDMs), as the underlying latent attributes of CDMs are…

  3. Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)

    1994-01-01

    When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be

  4. Model-based reasoning: Troubleshooting

    NASA Astrophysics Data System (ADS)

    Davis, Randall; Hamscher, Walter C.

    1988-07-01

    To determine why something has stopped working, its useful to know how it was supposed to work in the first place. That simple observation underlies some of the considerable interest generated in recent years on the topic of model-based reasoning, particularly its application to diagnosis and troubleshooting. This paper surveys the current state of the art, reviewing areas that are well understood and exploring areas that present challenging research topics. It views the fundamental paradigm as the interaction of prediction and observation, and explores it by examining three fundamental subproblems: generating hypotheses by reasoning from a symptom to a collection of components whose misbehavior may plausibly have caused that symptom; testing each hypothesis to see whether it can account for all available observations of device behavior; then discriminating among the ones that survive testing. We analyze each of these independently at the knowledge level i.e., attempting to understand what reasoning capabilities arise from the different varieties of knowledge available to the program. We find that while a wide range of apparently diverse model-based systems have been built for diagnosis and troubleshooting, they are for the most part variations on the central theme outlined here. Their diversity lies primarily in the varying amounts of kinds of knowledge they bring to bear at each stage of the process; the underlying paradigm is fundamentally the same.

  5. DERMA: A Melanoma Diagnosis Platform Based on Collaborative Multilabel Analog Reasoning

    PubMed Central

    Golobardes, Elisabet; Corral, Guiomar; Puig, Susana; Malvehy, Josep

    2014-01-01

    The number of melanoma cancer-related death has increased over the last few years due to the new solar habits. Early diagnosis has become the best prevention method. This work presents a melanoma diagnosis architecture based on the collaboration of several multilabel case-based reasoning subsystems called DERMA. The system has to face up several challenges that include data characterization, pattern matching, reliable diagnosis, and self-explanation capabilities. Experiments using subsystems specialized in confocal and dermoscopy images have provided promising results for helping experts to assess melanoma diagnosis. PMID:24578629

  6. 1H NMR- based metabolomics approaches as non- invasive tools for diagnosis of endometriosis

    PubMed Central

    Ghazi, Negar; Arjmand, Mohammad; Akbari, Ziba; Mellati, Ali Owsat; Saheb-Kashaf, Hamid; Zamani, Zahra

    2016-01-01

    Background: So far, non-invasive diagnostic approaches such as ultrasound, magnetic resonance imaging, or blood tests do not have sufficient diagnostic power for endometriosis disease. Lack of a non-invasive diagnostic test contributes to the long delay between onset of symptoms and diagnosis of endometriosis. Objective: The present study focuses on the identification of predictive biomarkers in serum by pattern recognition techniques and uses partial least square discriminant analysis, multi-layer feed forward artificial neural networks (ANNs) and quadratic discriminant analysis (QDA) modeling tools for the early diagnosis of endometriosis in a minimally invasive manner by 1H- NMR based metabolomics. Materials and Methods: This prospective cohort study was done in Pasteur Institute, Iran in June 2013. Serum samples of 31 infertile women with endometriosis (stage II and III) who confirmed by diagnostic laparoscopy and 15 normal women were collected and analyzed by nuclear magnetic resonance spectroscopy. The model was built by using partial least square discriminant analysis, QDA, and ANNs to determine classifier metabolites for early prediction risk of disease. Results: The levels of 2- methoxyestron, 2-methoxy estradiol, dehydroepiandrostion androstendione, aldosterone, and deoxy corticosterone were enhanced significantly in infertile group. While cholesterol and primary bile acids levels were decreased. QDA model showed significant difference between two study groups. Positive and negative predict value levels obtained about 71% and 78%, respectively. ANNs provided also criteria for detection of endometriosis. Conclusion: The QDA and ANNs modeling can be used as computational tools in noninvasive diagnose of endometriosis. However, the model designed by QDA methods is more efficient compared to ANNs in diagnosis of endometriosis patients. PMID:27141542

  7. Continuous wavelet transform-based feature selection applied to near-infrared spectral diagnosis of cancer.

    PubMed

    Chen, Hui; Lin, Zan; Mo, Lin; Wu, Hegang; Wu, Tong; Tan, Chao

    2015-12-01

    Spectrum is inherently local in nature since it can be thought of as a signal being composed of various frequency components. Wavelet transform (WT) is a powerful tool that partitions a signal into components with different frequency. The property of multi-resolution enables WT a very effective and natural tool for analyzing spectrum-like signal. In this study, a continuous wavelet transform (CWT)-based variable selection procedure was proposed to search for a set of informative wavelet coefficients for constructing a near-infrared (NIR) spectral diagnosis model of cancer. The CWT provided a fine multi-resolution feature space for selecting best predictors. A measure of discriminating power (DP) was defined to evaluate the coefficients. Partial least squares-discriminant analysis (PLS-DA) was used as the classification algorithm. A NIR spectral dataset associated to cancer diagnosis was used for experiment. The optimal results obtained correspond to the wavelet of db2. It revealed that on condition of having better performance on the training set, the optimal PLS-DA model using only 40 wavelet coefficients in 10 scales achieved the same performance as the one using all the variables in the original space on the test set: an overall accuracy of 93.8%, sensitivity of 92.5% and specificity of 96.3%. It confirms that the CWT-based feature selection coupled with PLS-DA is feasible and effective for constructing models of diagnostic cancer by NIR spectroscopy. PMID:26143320

  8. Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

    PubMed Central

    Emir, Birol; Masters, Elizabeth T; Mardekian, Jack; Clair, Andrew; Kuhn, Max; Silverman, Stuart L

    2015-01-01

    Background Diagnosis of fibromyalgia (FM), a chronic musculoskeletal condition characterized by widespread pain and a constellation of symptoms, remains challenging and is often delayed. Methods Random forest modeling of electronic medical records was used to identify variables that may facilitate earlier FM identification and diagnosis. Subjects aged ≥18 years with two or more listings of the International Classification of Diseases, Ninth Revision, (ICD-9) code for FM (ICD-9 729.1) ≥30 days apart during the 2012 calendar year were defined as cases among subjects associated with an integrated delivery network and who had one or more health care provider encounter in the Humedica database in calendar years 2011 and 2012. Controls were without the FM ICD-9 codes. Seventy-two demographic, clinical, and health care resource utilization variables were entered into a random forest model with downsampling to account for cohort imbalances (<1% subjects had FM). Importance of the top ten variables was ranked based on normalization to 100% for the variable with the largest loss in predicting performance by its omission from the model. Since random forest is a complex prediction method, a set of simple rules was derived to help understand what factors drive individual predictions. Results The ten variables identified by the model were: number of visits where laboratory/non-imaging diagnostic tests were ordered; number of outpatient visits excluding office visits; age; number of office visits; number of opioid prescriptions; number of medications prescribed; number of pain medications excluding opioids; number of medications administered/ordered; number of emergency room visits; and number of musculoskeletal conditions. A receiver operating characteristic curve confirmed the model’s predictive accuracy using an independent test set (area under the curve, 0.810). To enhance interpretability, nine rules were developed that could be used with good predictive probability of

  9. A Note on Comparing Examinee Classification Methods for Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Huebner, Alan; Wang, Chun

    2011-01-01

    Cognitive diagnosis models have received much attention in the recent psychometric literature because of their potential to provide examinees with information regarding multiple fine-grained discretely defined skills, or attributes. This article discusses the issue of methods of examinee classification for cognitive diagnosis models, which are…

  10. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    NASA Astrophysics Data System (ADS)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  11. Clinical diagnosis support system based on case based fuzzy cognitive maps and semantic web.

    PubMed

    Douali, Nassim; De Roo, Jos; Jaulent, Marie-Christine

    2012-01-01

    Incorrect or improper diagnostic tests uses have important implications for health outcomes and costs. Clinical Decision Support Systems purports to optimize the use of diagnostic tests in clinical practice. The computerized medical reasoning should not only focus on existing medical knowledge but also on physician's previous experiences and new knowledge. Such medical knowledge is vague and defines uncertain relationships between facts and diagnosis, in this paper, Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps. They allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve diagnosis decision. We have developed a framework for interacting with patient's data and formalizing knowledge from Guidelines in the domain of Urinary Tract Infection. The conducted study allowed us to test cognitive approaches for implementing Guidelines with Semantic Web tools. The advantage of this approach is to enable the sharing and reuse of knowledge from Guidelines, physicians experiences and simplify maintenance. PMID:22874199

  12. Inherited platelet disorders: toward DNA-based diagnosis

    PubMed Central

    Lentaigne, Claire; Freson, Kathleen; Laffan, Michael A.; Turro, Ernest

    2016-01-01

    Variations in platelet number, volume, and function are largely genetically controlled, and many loci associated with platelet traits have been identified by genome-wide association studies (GWASs).1 The genome also contains a large number of rare variants, of which a tiny fraction underlies the inherited diseases of humans. Research over the last 3 decades has led to the discovery of 51 genes harboring variants responsible for inherited platelet disorders (IPDs). However, the majority of patients with an IPD still do not receive a molecular diagnosis. Alongside the scientific interest, molecular or genetic diagnosis is important for patients. There is increasing recognition that a number of IPDs are associated with severe pathologies, including an increased risk of malignancy, and a definitive diagnosis can inform prognosis and care. In this review, we give an overview of these disorders grouped according to their effect on platelet biology and their clinical characteristics. We also discuss the challenge of identifying candidate genes and causal variants therein, how IPDs have been historically diagnosed, and how this is changing with the introduction of high-throughput sequencing. Finally, we describe how integration of large genomic, epigenomic, and phenotypic datasets, including whole genome sequencing data, GWASs, epigenomic profiling, protein–protein interaction networks, and standardized clinical phenotype coding, will drive the discovery of novel mechanisms of disease in the near future to improve patient diagnosis and management. PMID:27095789

  13. Inherited platelet disorders: toward DNA-based diagnosis.

    PubMed

    Lentaigne, Claire; Freson, Kathleen; Laffan, Michael A; Turro, Ernest; Ouwehand, Willem H

    2016-06-01

    Variations in platelet number, volume, and function are largely genetically controlled, and many loci associated with platelet traits have been identified by genome-wide association studies (GWASs).(1) The genome also contains a large number of rare variants, of which a tiny fraction underlies the inherited diseases of humans. Research over the last 3 decades has led to the discovery of 51 genes harboring variants responsible for inherited platelet disorders (IPDs). However, the majority of patients with an IPD still do not receive a molecular diagnosis. Alongside the scientific interest, molecular or genetic diagnosis is important for patients. There is increasing recognition that a number of IPDs are associated with severe pathologies, including an increased risk of malignancy, and a definitive diagnosis can inform prognosis and care. In this review, we give an overview of these disorders grouped according to their effect on platelet biology and their clinical characteristics. We also discuss the challenge of identifying candidate genes and causal variants therein, how IPDs have been historically diagnosed, and how this is changing with the introduction of high-throughput sequencing. Finally, we describe how integration of large genomic, epigenomic, and phenotypic datasets, including whole genome sequencing data, GWASs, epigenomic profiling, protein-protein interaction networks, and standardized clinical phenotype coding, will drive the discovery of novel mechanisms of disease in the near future to improve patient diagnosis and management. PMID:27095789

  14. Developing a Diagnosis Aiding Ontology Based on Hysteroscopy Image Processing

    NASA Astrophysics Data System (ADS)

    Poulos, Marios; Korfiatis, Nikolaos

    In this paper we describe an ontology design process which will introduce the steps and mechanisms required in order to create and develop an ontology which will be able to represent and describe the contents and attributes of hysteroscopy images, as well as their relationships, thus providing a useful ground for the development of tools related with medical diagnosis from physicians.

  15. Bearing fault diagnosis based on spectrum images of vibration signals

    NASA Astrophysics Data System (ADS)

    Li, Wei; Qiu, Mingquan; Zhu, Zhencai; Wu, Bo; Zhou, Gongbo

    2016-03-01

    Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and it’s receiving more and more attention. The conventional fault diagnosis methods usually extract features from the waveforms or spectrums of vibration signals in order to correctly classify faults. In this paper, a novel feature in the form of images is presented, namely analysis of the spectrum images of vibration signals. The spectrum images are simply obtained by doing fast Fourier transformation. Such images are processed with two-dimensional principal component analysis (2DPCA) to reduce the dimensions, and then a minimum distance method is applied to classify the faults of bearings. The effectiveness of the proposed method is verified with experimental data.

  16. Fiber probes based optical techniques for biomedical diagnosis

    NASA Astrophysics Data System (ADS)

    Arce-Diego, José L.; Fanjul-Vélez, Félix

    2007-06-01

    Although fiber optics have been applied in optical communication and sensor systems for several years in a very successful way, their first application was developed in medicine in the early 20's. Manufacturing and developing of optical fibers for biomedical purposes have required a lot of research efforts in order to achieve a non-invasive, in-vivo, and real-time diagnosis of different diseases in human or animal tissues. In general, optical fiber probes are designed as a function of the optical measurement technique. In this work, a brief description of the main optical techniques for optical characterization of biological tissues is presented. The recent advances in optical fiber probes for biomedical diagnosis in clinical analysis and optical biopsy in relation with the different spectroscopic or tomographic optical techniques are described.

  17. Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

    PubMed Central

    2010-01-01

    Background Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem. Methods Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets. Results A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained

  18. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    PubMed Central

    Cerrada, Mariela; Sánchez, René Vinicio; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-01-01

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%. PMID:26393603

  19. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal.

    PubMed

    Cerrada, Mariela; Vinicio Sánchez, René; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-01-01

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%. PMID:26393603

  20. Base Flow Model Validation

    NASA Technical Reports Server (NTRS)

    Sinha, Neeraj; Brinckman, Kevin; Jansen, Bernard; Seiner, John

    2011-01-01

    A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.

  1. Modeling the Phenotypic Architecture of Autism Symptoms from Time of Diagnosis to Age 6

    ERIC Educational Resources Information Center

    Georgiades, Stelios; Boyle, Michael; Szatmari, Peter; Hanna, Steven; Duku, Eric; Zwaigenbaum, Lonnie; Bryson, Susan; Fombonne, Eric; Volden, Joanne; Mirenda, Pat; Smith, Isabel; Roberts, Wendy; Vaillancourt, Tracy; Waddell, Charlotte; Bennett, Teresa; Elsabbagh, Mayada; Thompson, Ann

    2014-01-01

    The latent class structure of autism symptoms from the time of diagnosis to age 6 years was examined in a sample of 280 children with autism spectrum disorder. Factor mixture modeling was performed on 26 algorithm items from the Autism Diagnostic Interview-Revised at diagnosis (Time 1) and again at age 6 (Time 2). At Time 1, a…

  2. A multi-fault diagnosis method for sensor systems based on principle component analysis.

    PubMed

    Zhu, Daqi; Bai, Jie; Yang, Simon X

    2010-01-01

    A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. PMID:22315537

  3. Imaging-Based Diagnosis of Wernicke Encephalopathy: A Case Report

    PubMed Central

    Delavar Kasmaei, Hosein; Baratloo, Alireza; Soleymani, Maryam; Nasiri, Zahra

    2014-01-01

    Introduction: Wernicke encephalopathy (WE) is a medical emergency characterized by ataxia, confusion, nystagmus and ophthalmoplegia resulting from thiamin deficiency. Alcoholism is the common cause for this disease. Case Presentation: A 41 year old man was brought to our emergency department (ED) complaining of confusion. One week earlier he had started to experience severe nausea and vomiting followed by diplopia, dysarthria and also dysphagia. One day later he had experienced gait disturbance and progressive ataxia accompanied with confusion, apathy and disorientation. He had no history of alcoholism, drug abuse or previous surgery but had history of untreated Crohn disease. Just before arrival to our emergency department, he had been hospitalized in another center for about a week but all investigations had failed to provide a conclusive diagnosis. Upon admission to our ED, he was dysarthric and replied with inappropriate answers. On physical examination, bilateral horizontal nystagmus in lateral gaze, left abducens nerve palsy and upward gaze palsy were seen. Gag reflex was absent and plantar reflexes were upwards bilaterally. After reviewing all the previously performed management measures, MRI was performed and was consistent with the diagnosis of WE. Treatment with thiamine led to partial resolution of his upward gaze palsy and nystagmus on the first day. At the end of the third day of treatment, except for gate ataxia, all other symptoms completely resolved and he was fully conscious. After the fifth day his gait became normal and after one week he was discharged in good general condition. Discussion: After reviewing the current literature, it seems that brain MRI can be helpful in the diagnosis of WE in patients with the classic clinical trial in the absence of clear risk factors. PMID:25717447

  4. Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Douglas, Jeffrey A.

    2008-01-01

    This paper studies three models for cognitive diagnosis, each illustrated with an application to fraction subtraction data. The objective of each of these models is to classify examinees according to their mastery of skills assumed to be required for fraction subtraction. We consider the DINA model, the NIDA model, and a new model that extends the…

  5. Actuator fault tolerant multi-controller scheme using set separation based diagnosis

    NASA Astrophysics Data System (ADS)

    Seron, María M.; De Doná, José A.

    2010-11-01

    We present a fault tolerant control strategy based on a new principle for actuator fault diagnosis. The scheme employs a standard bank of observers which match the different fault situations that can occur in the plant. Each of these observers has an associated estimation error with distinctive dynamics when an estimator matches the current fault situation of the plant. Based on the information from each observer, a fault detection and isolation (FDI) module is able to reconfigure the control loop by selecting the appropriate control law from a bank of controllers, each of them designed to stabilise and achieve reference tracking for one of the given fault models. The main contribution of this article is to propose a new FDI principle which exploits the separation of sets that characterise healthy system operation from sets that characterise transitions from healthy to faulty behaviour. The new principle allows to provide pre-checkable conditions for guaranteed fault tolerance of the overall multi-controller scheme.

  6. Knowledge-based computer system to aid in the histopathological diagnosis of breast disease.

    PubMed Central

    Heathfield, H; Bose, D; Kirkham, N

    1991-01-01

    A knowledge-based computer system, designed to assist pathologists in the histological diagnosis of breast disease, is described. This system represents knowledge in the form of "disease profiles" and uses a novel inference model based on the mathematical technique of hypergraphs. Its design overcomes many of the limitations of existing expert system technologies when applied to breast disease. In particular, the system can quickly focus on a differential problem and thus reduce the amount of data necessary to reach a conclusion. The system was tested on two sets of samples, consisting of 14 retrospective cases and five hypothetical cases of breast disease. Its recommendations were judged "correct" by the evaluating pathologist in 15 cases. This study shows the feasibility of providing "decision support" in histopathology. PMID:2066430

  7. Deep-reasoning fault diagnosis - An aid and a model

    NASA Technical Reports Server (NTRS)

    Yoon, Wan Chul; Hammer, John M.

    1988-01-01

    The design and evaluation are presented for the knowledge-based assistance of a human operator who must diagnose a novel fault in a dynamic, physical system. A computer aid based on a qualitative model of the system was built to help the operators overcome some of their cognitive limitations. This aid differs from most expert systems in that it operates at several levels of interaction that are believed to be more suitable for deep reasoning. Four aiding approaches, each of which provided unique information to the operator, were evaluated. The aiding features were designed to help the human's casual reasoning about the system in predicting normal system behavior (N aiding), integrating observations into actual system behavior (O aiding), finding discrepancies between the two (O-N aiding), or finding discrepancies between observed behavior and hypothetical behavior (O-HN aiding). Human diagnostic performance was found to improve by almost a factor of two with O aiding and O-N aiding.

  8. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  9. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

    PubMed

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics). PMID:27475574

  10. Development of a novel multiplex beads-based assay for autoantibody detection for colorectal cancer diagnosis.

    PubMed

    Villar-Vázquez, Roi; Padilla, Guillermo; Fernández-Aceñero, María Jesús; Suárez, Adolfo; Fuente, Eduardo; Pastor, Carlos; Calero, Miguel; Barderas, Rodrigo; Casal, J Ignacio

    2016-04-01

    Humoral response in cancer patients can be used for early cancer detection. By screening high-density protein microarrays with sera from colorectal cancer (CRC) patients and controls, we identified 16 tumor-associated antigens (TAAs) exhibiting high diagnostic value. This high number of TAAs requires the development of multiplex assays combining different antigens for a faster and more accurate prediction of CRC. Here, we have developed and optimized a bead-based assay using nine selected TAAs and two controls to provide a multiplex test for early CRC diagnosis. We screened a collection of 307 CRC patients' and control sera with the beads assay to identify and validate the best TAA combination for CRC detection. The multiplex bead-based assay exhibited a similar diagnostic performance to detect the humoral response in comparison to multiple ELISA analyses. After multivariate analysis, a panel composed of GTF2B, EDIL3, HCK, PIM1, STK4, and p53, together with gender and age, was identified as the best combination of TAAs for CRC diagnosis, achieving an AUC of 89.7%, with 66% sensitivity at 90.0% fixed specificity. The model was validated using bootstrapping analysis. In summary, we have developed a novel multiplex bead assay that after validation with a larger independent cohort of sera could be utilized in a high-throughput manner for population screening to facilitate the detection of early CRC patients. PMID:26915739

  11. Beam Diagnosis and Lattice Modeling of the Fermilab Booster

    SciTech Connect

    Huang, Xiaobiao

    2005-09-01

    A realistic lattice model is a fundamental basis for the operation of a synchrotron. In this study various beam-based measurements, including orbit response matrix (ORM) and BPM turn-by-turn data are used to verify and calibrate the lattice model of the Fermilab Booster. In the ORM study, despite the strong correlation between the gradient parameters of adjacent magnets which prevents a full determination of the model parameters, an equivalent lattice model is obtained by imposing appropriate constraints. The fitted gradient errors of the focusing magnets are within the design tolerance and the results point to the orbit offsets in the sextupole field as the source of gradient errors. A new method, the independent component analysis (ICA) is introduced to analyze multiple BPM turn-by-turn data taken simultaneously around a synchrotron. This method makes use of the redundancy of the data and the time correlation of the source signals to isolate various components, such as betatron motion and synchrotron motion, from raw BPM data. By extracting clean coherent betatron motion from noisy data and separates out the betatron normal modes when there is linear coupling, the ICA method provides a convenient means to measure the beta functions and betatron phase advances. It also separates synchrotron motion from the BPM samples for dispersion function measurement. The ICA method has the capability to separate other perturbation signals and is robust over the contamination of bad BPMs. The application of the ICA method to the Booster has enabled the measurement of the linear lattice functions which are used to verify the existing lattice model. The transverse impedance and chromaticity are measured from turn-by-turn data using high precision tune measurements. Synchrotron motion is also observed in the BPM data. The emittance growth of the Booster is also studied by data taken with ion profile monitor (IPM). Sources of emittance growth are examined and an approach to cure

  12. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    NASA Astrophysics Data System (ADS)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran

    2016-05-01

    Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.

  13. Fault diagnosis

    NASA Technical Reports Server (NTRS)

    Abbott, Kathy

    1990-01-01

    The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

  14. A GC/MS-based metabolomic approach for reliable diagnosis of phenylketonuria.

    PubMed

    Xiong, Xiyue; Sheng, Xiaoqi; Liu, Dan; Zeng, Ting; Peng, Ying; Wang, Yichao

    2015-11-01

    Although the phenylalanine/tyrosine ratio in blood has been the gold standard for diagnosis of phenylketonuria (PKU), the disadvantages of invasive sample collection and false positive error limited the application of this discriminator in the diagnosis of PKU to some extent. The aim of this study was to develop a new standard with high sensitivity and specificity in a less invasive manner for diagnosing PKU. In this study, an improved oximation-silylation method together with GC/MS was utilized to obtain the urinary metabolomic information in 47 PKU patients compared with 47 non-PKU controls. Compared with conventional oximation-silylation methods, the present approach possesses the advantages of shorter reaction time and higher reaction efficiency at a considerably lower temperature, which is beneficial to the derivatization of some thermally unstable compounds, such as phenylpyruvic acid. Ninety-seven peaks in the chromatograms were identified as endogenous metabolites by the National Institute of Standards and Technology (NIST) mass spectra library, including amino acids, organic acids, carbohydrates, amides, and fatty acids. After normalization of data using creatinine as internal standard, 19 differentially expressed compounds with p values of <0.05 were selected by independent-sample t test for the separation of the PKU group and the control group. A principal component analysis (PCA) model constructed by these differentially expressed compounds showed that the PKU group can be discriminated from the control group. Receiver-operating characteristic (ROC) analysis with area under the curve (AUC), specificity, and sensitivity of each PKU marker obtained from these differentially expressed compounds was used to evaluate the possibility of using these markers for diagnosing PKU. The largest value of AUC (0.987) with high specificity (0.936) and sensitivity (1.000) was obtained by the ROC curve of phenylacetic acid at its cutoff value (17.244 mmol/mol creatinine

  15. Irreversible entropy model for damage diagnosis in resistors

    SciTech Connect

    Cuadras, Angel Crisóstomo, Javier; Ovejas, Victoria J.; Quilez, Marcos

    2015-10-28

    We propose a method to characterize electrical resistor damage based on entropy measurements. Irreversible entropy and the rate at which it is generated are more convenient parameters than resistance for describing damage because they are essentially positive in virtue of the second law of thermodynamics, whereas resistance may increase or decrease depending on the degradation mechanism. Commercial resistors were tested in order to characterize the damage induced by power surges. Resistors were biased with constant and pulsed voltage signals, leading to power dissipation in the range of 4–8 W, which is well above the 0.25 W nominal power to initiate failure. Entropy was inferred from the added power and temperature evolution. A model is proposed to understand the relationship among resistance, entropy, and damage. The power surge dissipates into heat (Joule effect) and damages the resistor. The results show a correlation between entropy generation rate and resistor failure. We conclude that damage can be conveniently assessed from irreversible entropy generation. Our results for resistors can be easily extrapolated to other systems or machines that can be modeled based on their resistance.

  16. Knowledge-based approach to fault diagnosis and control in distributed process environments

    NASA Astrophysics Data System (ADS)

    Chung, Kwangsue; Tou, Julius T.

    1991-03-01

    This paper presents a new design approach to knowledge-based decision support systems for fault diagnosis and control for quality assurance and productivity improvement in automated manufacturing environments. Based on the observed manifestations, the knowledge-based diagnostic system hypothesizes a set of the most plausible disorders by mimicking the reasoning process of a human diagnostician. The data integration technique is designed to generate error-free hierarchical category files. A novel approach to diagnostic problem solving has been proposed by integrating the PADIKS (Pattern-Directed Knowledge-Based System) concept and the symbolic model of diagnostic reasoning based on the categorical causal model. The combination of symbolic causal reasoning and pattern-directed reasoning produces a highly efficient diagnostic procedure and generates a more realistic expert behavior. In addition, three distinctive constraints are designed to further reduce the computational complexity and to eliminate non-plausible hypotheses involved in the multiple disorders problem. The proposed diagnostic mechanism, which consists of three different levels of reasoning operations, significantly reduces the computational complexity in the diagnostic problem with uncertainty by systematically shrinking the hypotheses space. This approach is applied to the test and inspection data collected from a PCB manufacturing operation.

  17. Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability.

    PubMed

    Kwong, Raymond H; Yonge-Mallo, David L

    2015-07-01

    Most model-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system, or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In a previous paper, we addressed the problem of diagnosing faults given an incomplete model of the discrete-event system. We presented the learning diagnoser which not only diagnoses faults, but also attempts to learn missing model information through parsimonious hypothesis generation. In this paper, we study the properties of learnability and diagnosability. Learnability deals with the issue of whether the missing model information can be learned, while diagnosability corresponds to the ability to detect and isolate a fault after it has occurred. We provide conditions under which the learning diagnoser can learn missing model information. We define the notions of weak and strong diagnosability and also give conditions under which they hold. PMID:25204002

  18. Paper-based diagnostic devices for clinical paraquat poisoning diagnosis.

    PubMed

    Kuan, Chen-Meng; Lin, Szu-Ting; Yen, Tzung-Hai; Wang, Yu-Lin; Cheng, Chao-Min

    2016-05-01

    This article unveils the development of a paper-based analytical device designed to rapidly detect and clinically diagnose paraquat (PQ) poisoning. Using wax printing technology, we fabricated a PQ detection device by pattering hydrophobic boundaries on paper. This PQ detection device employs a colorimetric sodium dithionite assay or an ascorbic acid assay to indicate the PQ level in a buffer system or in a human serum system in 10 min. In this test, colorimetric changes, blue in color, were observable with the naked eye. By curve fitting models of sodium dithionite and ascorbic acid assays in normal human serum, we evaluated serum PQ levels for five PQ-poisoned patients before hemoperfusion (HP) treatment and one PQ-poisoned patient after HP treatment. As evidenced by similar detection outcomes, the analytical performance of our device can compete with that of the highest clinical standard, i.e., spectrophotometry, with less complicated sample preparation and with more rapid results. Accordingly, we believe that our rapid PQ detection can benefit physicians determining timely treatment strategies for PQ-poisoned patients once they are taken to hospitals, and that this approach will increase survival rates. PMID:27462379

  19. Surface Plasmon Resonance for Cell-Based Clinical Diagnosis

    PubMed Central

    Yanase, Yuhki; Hiragun, Takaaki; Ishii, Kaori; Kawaguchi, Tomoko; Yanase, Tetsuji; Kawai, Mikio; Sakamoto, Kenji; Hide, Michihiro

    2014-01-01

    Non-invasive real-time observations and the evaluation of living cell conditions and functions are increasingly demanded in life sciences. Surface plasmon resonance (SPR) sensors detect the refractive index (RI) changes on the surface of sensor chips in label-free and on a real-time basis. Using SPR sensors, we and other groups have developed techniques to evaluate living cells' reactions in response to stimuli without any labeling in a real-time manner. The SPR imaging (SPRI) system for living cells may visualize single cell reactions and has the potential to expand application of SPR cell sensing for clinical diagnosis, such as multi-array cell diagnostic systems and detection of malignant cells among normal cells in combination with rapid cell isolation techniques. PMID:24618778

  20. Intelligent-based Structural Damage Detection Model

    SciTech Connect

    Lee, Eric Wai Ming; Yu, K.F.

    2010-05-21

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  1. Computer-aided diagnosis workstation for chest diagnosis based on multihelical CT images

    NASA Astrophysics Data System (ADS)

    Sato, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

    2005-04-01

    Mass screening based on helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router. This electronic medical recording system and prototype internet system were developed so as not to loosen the communication among staffs of hospital. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

  2. Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

    NASA Astrophysics Data System (ADS)

    Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

    2007-03-01

    Multislice CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multislice CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. Moreover, we have provided diagnostic assistance methods to medical screening specialists by using a lung cancer screening algorithm built into mobile helical CT scanner for the lung cancer mass screening done in the region without the hospital. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

  3. CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency

    PubMed Central

    2015-01-01

    Background The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. Objective We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called “CIMIDx”, based on representative association rules that support the diagnosis of medical images (mammograms). Methods The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype’s classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user’s perspective. Results We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information

  4. A novel ELISA-based diagnosis of acquired von Willebrand disease with increased VWF proteolysis.

    PubMed

    Rauch, Antoine; Caron, Claudine; Vincent, Flavien; Jeanpierre, Emmanuelle; Ternisien, Catherine; Boisseau, Pierre; Zawadzki, Christophe; Fressinaud, Edith; Borel-Derlon, Annie; Hermoire, Sylvie; Paris, Camille; Lavenu-Bombled, Cécile; Veyradier, Agnès; Ung, Alexandre; Vincentelli, André; van Belle, Eric; Lenting, Peter J; Goudemand, Jenny; Susen, Sophie

    2016-05-01

    Von Willebrand disease-type 2A (VWD-2A) and acquired von Willebrand syndrome (AVWS) due to aortic stenosis (AS) or left ventricular assist device (LVAD) are associated with an increased proteolysis of von Willebrand factor (VWF). Analysis of VWF multimeric profile is the most sensitive way to assess such increased VWF-proteolysis. However, several technical aspects hamper a large diffusion among routine diagnosis laboratories. This makes early diagnosis and early appropriate care of increased proteolysis challenging. In this context of unmet medical need, we developed a new ELISA aiming a quick, easy and reliable assessment of VWF-proteolysis. This ELISA was assessed successively in a LVAD-model, healthy subjects (n=39), acquired TTP-patients (n=4), VWD-patients (including VWD-2A(IIA), n=22; VWD-2B, n=26; VWD-2A(IIE), n=21; and VWD-1C, n=8) and in AVWS-patients (AS, n=9; LVAD, n=9; and MGUS, n=8). A standard of VWF-proteolysis was specifically developed. Extent of VWF-proteolysis was expressed as relative percentage and as VWF proteolysis/VWF:Ag ratio. A speed-dependent increase in VWF-proteolysis was assessed in the LVAD model whereas no proteolysis was observed in TTP-patients. In VWD-patients, VWF-proteolysis was significantly increased in VWD-2A(IIA) and VWD-2B and significantly decreased in VWD-2A(IIE) versus controls (p< 0.0001). In AVWS-patients, VWF-proteolysis was significantly increased in AS- and LVAD-patients compared to controls (p< 0.0001) and not detectable in MGUS-patients. A significant increase in VWF-proteolysis was detected as soon as three hours after LVAD implantation (p< 0.01). In conclusion, we describe a new ELISA allowing a rapid and accurate diagnosis of VWF-proteolysis validated in three different clinical situations. This assay represents a helpful alternative to electrophoresis-based assay in the diagnosis and management of AVWS with increased VWF-proteolysis. PMID:26791163

  5. Boolean modeling and fault diagnosis in oxidative stress response

    PubMed Central

    2012-01-01

    Background Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean network model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean network model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. Results In this paper, we have developed a Boolean network model for the oxidative stress response. This model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI3k/Akt pathway. Conclusions It is our hope that some of the additional predictions here, such as those pertaining to the

  6. Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis

    PubMed Central

    Wei, Liyang; Nishikawa, Roberts M.

    2009-01-01

    In this paper we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms was modeled after expert observers. In this work we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adaptively incorporating local proximity information into a classifier, it can help to improve its classification accuracy, thereby leading to an improved “second opinion” to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve. PMID:20161326

  7. A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.

    PubMed

    Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R

    2016-01-01

    A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures. PMID:27438234

  8. Simplified and optimized multispectral imaging for 5-ALA-based fluorescence diagnosis of malignant lesions.

    PubMed

    Minamikawa, Takeo; Matsuo, Hisataka; Kato, Yoshiyuki; Harada, Yoshinori; Otsuji, Eigo; Yanagisawa, Akio; Tanaka, Hideo; Takamatsu, Tetsuro

    2016-01-01

    5-aminolevulinic acid (5-ALA)-based fluorescence diagnosis is now clinically applied for accurate and ultrarapid diagnosis of malignant lesions such as lymph node metastasis during surgery. 5-ALA-based diagnosis evaluates fluorescence intensity of a fluorescent metabolite of 5-ALA, protoporphyrin IX (PPIX); however, the fluorescence of PPIX is often affected by autofluorescence of tissue chromophores, such as collagen and flavins. In this study, we demonstrated PPIX fluorescence estimation with autofluorescence elimination for 5-ALA-based fluorescence diagnosis of malignant lesions by simplified and optimized multispectral imaging. We computationally optimized observation wavelength regions for the estimation of PPIX fluorescence in terms of minimizing prediction error of PPIX fluorescence intensity in the presence of typical chromophores, collagen and flavins. By using the fluorescence intensities of the optimized wavelength regions, we verified quantitative detection of PPIX fluorescence by using chemical mixtures of PPIX, flavins, and collagen. Furthermore, we demonstrated detection capability by using metastatic and non-metastatic lymph nodes of colorectal cancer patients. These results suggest the potential and usefulness of the background-free estimation method of PPIX fluorescence for 5-ALA-based fluorescence diagnosis of malignant lesions, and we expect this method to be beneficial for intraoperative and rapid cancer diagnosis. PMID:27149301

  9. Simplified and optimized multispectral imaging for 5-ALA-based fluorescence diagnosis of malignant lesions

    PubMed Central

    Minamikawa, Takeo; Matsuo, Hisataka; Kato, Yoshiyuki; Harada, Yoshinori; Otsuji, Eigo; Yanagisawa, Akio; Tanaka, Hideo; Takamatsu, Tetsuro

    2016-01-01

    5-aminolevulinic acid (5-ALA)-based fluorescence diagnosis is now clinically applied for accurate and ultrarapid diagnosis of malignant lesions such as lymph node metastasis during surgery. 5-ALA-based diagnosis evaluates fluorescence intensity of a fluorescent metabolite of 5-ALA, protoporphyrin IX (PPIX); however, the fluorescence of PPIX is often affected by autofluorescence of tissue chromophores, such as collagen and flavins. In this study, we demonstrated PPIX fluorescence estimation with autofluorescence elimination for 5-ALA-based fluorescence diagnosis of malignant lesions by simplified and optimized multispectral imaging. We computationally optimized observation wavelength regions for the estimation of PPIX fluorescence in terms of minimizing prediction error of PPIX fluorescence intensity in the presence of typical chromophores, collagen and flavins. By using the fluorescence intensities of the optimized wavelength regions, we verified quantitative detection of PPIX fluorescence by using chemical mixtures of PPIX, flavins, and collagen. Furthermore, we demonstrated detection capability by using metastatic and non-metastatic lymph nodes of colorectal cancer patients. These results suggest the potential and usefulness of the background-free estimation method of PPIX fluorescence for 5-ALA-based fluorescence diagnosis of malignant lesions, and we expect this method to be beneficial for intraoperative and rapid cancer diagnosis. PMID:27149301

  10. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cong, Feiyun; Chen, Jin; Dong, Guangming; Pecht, Michael

    2013-04-01

    Rolling element bearing faults are among the main causes of breakdown in rotating machines. In this paper, a rolling bearing fault model is proposed based on the dynamic load analysis of a rotor-bearing system. The rotor impact factor is taken into consideration in the rolling bearing fault signal model. The defect load on the surface of the bearing is divided into two parts, the alternate load and the determinate load. The vibration response of the proposed fault signal model is investigated and the fault signal calculating equation is derived through dynamic and kinematic analysis. Outer race and inner race fault simulations are realized in the paper. The simulation process includes consideration of several parameters, such as the gravity of the rotor-bearing system, the imbalance of the rotor, and the location of the defect on the surface. The simulation results show that different amplitude contributions of the alternate load and determinate load will cause different envelope spectrum expressions. The rotating frequency sidebands will occur in the envelope spectrum in addition to the fault characteristic frequency. This appearance of sidebands will increase the difficulty of fault recognition in intelligent fault diagnosis. The experiments given in the paper have successfully verified the proposed signal model simulation results. The test rig design of the rotor bearing system simulated several operating conditions: (1) rotor bearing only; (2) rotor bearing with loader added; (3) rotor bearing with loader and rotor disk; and (4) bearing fault simulation without rotor influence. The results of the experiments have verified that the proposed rolling bearing signal model is important to the rolling bearing fault diagnosis of rotor-bearing systems.

  11. Imaging-based diagnosis of autosomal dominant polycystic kidney disease.

    PubMed

    Pei, York; Hwang, Young-Hwan; Conklin, John; Sundsbak, Jamie L; Heyer, Christina M; Chan, Winnie; Wang, Kairong; He, Ning; Rattansingh, Anand; Atri, Mostafa; Harris, Peter C; Haider, Masoom A

    2015-03-01

    The clinical use of conventional ultrasonography (US) in autosomal dominant polycystic kidney disease (ADPKD) is currently limited by reduced diagnostic sensitivity, especially in at-risk subjects younger than 30 years of age. In this single-center prospective study, we compared the diagnostic performance of MRI with that of high-resolution (HR) US in 126 subjects ages 16-40 years born with a 50% risk of ADPKD who underwent both these renal imaging studies and comprehensive PKD1 and PKD2 mutation screening. Concurrently, 45 healthy control subjects without a family history of ADPKD completed the same imaging protocol. We analyzed 110 at-risk subjects whose disease status was unequivocally defined by molecular testing and 45 unaffected healthy control subjects. Using a total of >10 cysts as a test criterion in subjects younger than 30 years of age, we found that MRI provided both a sensitivity and specificity of 100%. Comparison of our results from HR US with those from a previous study of conventional US using the test criterion of a total of three or more cysts found a higher diagnostic sensitivity (approximately 97% versus approximately 82%) with a slightly decreased specificity (approximately 98% versus 100%) in this study. Similar results were obtained in test subjects between the ages of 30 and 40 years old. These results suggest that MRI is highly sensitive and specific for diagnosis of ADPKD. HR US has the potential to rival the diagnostic performance of MRI but is both center- and operator-dependent. PMID:25074509

  12. Model based manipulator control

    NASA Technical Reports Server (NTRS)

    Petrosky, Lyman J.; Oppenheim, Irving J.

    1989-01-01

    The feasibility of using model based control (MBC) for robotic manipulators was investigated. A double inverted pendulum system was constructed as the experimental system for a general study of dynamically stable manipulation. The original interest in dynamically stable systems was driven by the objective of high vertical reach (balancing), and the planning of inertially favorable trajectories for force and payload demands. The model-based control approach is described and the results of experimental tests are summarized. Results directly demonstrate that MBC can provide stable control at all speeds of operation and support operations requiring dynamic stability such as balancing. The application of MBC to systems with flexible links is also discussed.

  13. Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

    NASA Astrophysics Data System (ADS)

    Wang, Yingmin; Zhang, Fujun; Cui, Tao; Zhou, Jinlong

    2016-03-01

    Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015-0.017 5 and sample error is controlled within 0-0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis; the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

  14. Development of a component centered fault monitoring and diagnosis knowledge based system for space power system

    NASA Technical Reports Server (NTRS)

    Lee, S. C.; Lollar, Louis F.

    1988-01-01

    The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.

  15. Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis.

    PubMed

    Liu, Mingxia; Zhang, Daoqiang; Adeli, Ehsan; Shen, Dinggang

    2016-07-01

    Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods. PMID:26540666

  16. Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

    NASA Astrophysics Data System (ADS)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

    2009-02-01

    Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and

  17. Detection, modeling and matching of pleural thickenings from CT data towards an early diagnosis of malignant pleural mesothelioma

    NASA Astrophysics Data System (ADS)

    Chaisaowong, Kraisorn; Kraus, Thomas

    2014-03-01

    Pleural thickenings can be caused by asbestos exposure and may evolve into malignant pleural mesothelioma. While an early diagnosis plays the key role to an early treatment, and therefore helping to reduce morbidity, the growth rate of a pleural thickening can be in turn essential evidence to an early diagnosis of the pleural mesothelioma. The detection of pleural thickenings is today done by a visual inspection of CT data, which is time-consuming and underlies the physician's subjective judgment. Computer-assisted diagnosis systems to automatically assess pleural mesothelioma have been reported worldwide. But in this paper, an image analysis pipeline to automatically detect pleural thickenings and measure their volume is described. We first delineate automatically the pleural contour in the CT images. An adaptive surface-base smoothing technique is then applied to the pleural contours to identify all potential thickenings. A following tissue-specific topology-oriented detection based on a probabilistic Hounsfield Unit model of pleural plaques specify then the genuine pleural thickenings among them. The assessment of the detected pleural thickenings is based on the volumetry of the 3D model, created by mesh construction algorithm followed by Laplace-Beltrami eigenfunction expansion surface smoothing technique. Finally, the spatiotemporal matching of pleural thickenings from consecutive CT data is carried out based on the semi-automatic lung registration towards the assessment of its growth rate. With these methods, a new computer-assisted diagnosis system is presented in order to assure a precise and reproducible assessment of pleural thickenings towards the diagnosis of the pleural mesothelioma in its early stage.

  18. Incipient interturn fault diagnosis in induction machines using an analytic wavelet-based optimized Bayesian inference.

    PubMed

    Seshadrinath, Jeevanand; Singh, Bhim; Panigrahi, Bijaya Ketan

    2014-05-01

    Interturn fault diagnosis of induction machines has been discussed using various neural network-based techniques. The main challenge in such methods is the computational complexity due to the huge size of the network, and in pruning a large number of parameters. In this paper, a nearly shift insensitive complex wavelet-based probabilistic neural network (PNN) model, which has only a single parameter to be optimized, is proposed for interturn fault detection. The algorithm constitutes two parts and runs in an iterative way. In the first part, the PNN structure determination has been discussed, which finds out the optimum size of the network using an orthogonal least squares regression algorithm, thereby reducing its size. In the second part, a Bayesian classifier fusion has been recommended as an effective solution for deciding the machine condition. The testing accuracy, sensitivity, and specificity values are highest for the product rule-based fusion scheme, which is obtained under load, supply, and frequency variations. The point of overfitting of PNN is determined, which reduces the size, without compromising the performance. Moreover, a comparative evaluation with traditional discrete wavelet transform-based method is demonstrated for performance evaluation and to appreciate the obtained results. PMID:24808044

  19. Modeling and diagnosis of structural systems through sparse dynamic graphical models

    NASA Astrophysics Data System (ADS)

    Bornn, Luke; Farrar, Charles R.; Higdon, David; Murphy, Kevin P.

    2016-06-01

    Since their introduction into the structural health monitoring field, time-domain statistical models have been applied with considerable success. Current approaches still have several flaws, however, as they typically ignore the structure of the system, using individual sensor data for modeling and diagnosis. This paper introduces a Bayesian framework containing much of the previous work with autoregressive models as a special case. In addition, the framework allows for natural inclusion of structural knowledge through the form of prior distributions on the model parameters. Acknowledging the need for computational efficiency, we extend the framework through the use of decomposable graphical models, exploiting sparsity in the system to give models that are simple to fit and understand. This sparsity can be specified from knowledge of the system, from the data itself, or through a combination of the two. Using both simulated and real data, we demonstrate the capability of the model to capture the dynamics of the system and to provide clear indications of structural change and damage. We also demonstrate how learning the sparsity in the system gives insight into the structure's physical properties.

  20. ODP based UPT model

    NASA Astrophysics Data System (ADS)

    Berre, A. J.; Handegard, T.; Loevnes, K.; Skjellaug, B.; Aagedal, J. O.

    1994-01-01

    The report documents the experiments with object oriented modelling of Universal Personal Telecommunication (UPT) in a telecommunication environment based on the basic principles of open distributed processing (ODP). Through the object-oriented analysis and design technique Object Modelling Technique (OMT) the service is modelled as a collection of software objects distributed across multiple network nodes. A software platform provides the mechanisms for application objects to interact. The platform builds on the basic facilities in the native computing and communication environments, but hides the heterogeneity of these environments and provides distribution transparency at the application programmer's interface. The report closes with some thoughts about applying the paradigm of ODP to intelligent networks (IN), and the experience with OMT as a modelling technique for real time distributed applications.

  1. Application of 4G wireless network-based system for remote diagnosis and nursing of stomal complications

    PubMed Central

    Xu, Xiulian; Cao, Yingjuan; Luan, Xiaorong

    2014-01-01

    Background: This study aims to apply 4G wireless network in the remote diagnosis of stoma complications for the first time. Background: Remote diagnosis and nursing care for a variety of illnesses are urgently needed in clinical settings. Objectives: Combining with relevant clinical manifestations, an Android phone-based intelligent diagnosis system was designed to construct a universe, easy access to exploitation and human-computer interaction database and exploitation environment for applications and programs. Methods: “Production rule” and forward reasoning method were utilized to design arborescence structures and logic reasoner associated with stoma complications. Stoma physicians were responsible for delivering evaluation scores on patients’ health status using analytic hierarchy process. The emphasis of this study is to exploit an “Android phone-based system for remote diagnosis of stoma”, which is of certain universe usage. Results: Such system was tested in the Medicine Information Center of Qilu Hospital of Shandong University and initially applied in the city of De Zhou, Shandong province, China. Conclusions: These results collectively demonstrated that the system is easy to carry, of high utility and free from the limitations of wire network environment, etc. It provides clinical evidence for establishing a novel type model for the exchange between patients and physicians. PMID:25550986

  2. Experimental Evaluation of a Structure-Based Connectionist Network for Fault Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, V. B.; Danai, K.; Lewicki, D. G.

    1998-01-01

    This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

  3. Improving the Performance of the Structure-Based Connectionist Network for Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.

    1996-01-01

    A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

  4. A model of human decisionmaking in a fault diagnosis task

    NASA Technical Reports Server (NTRS)

    Rouse, W. B.

    1978-01-01

    Utilizing elementary concepts from the theory of fuzzy sets as well as several nonfuzzy heuristics, a model is presented of human decisionmaking in the task of troubleshooting graphically displayed networks. The performance of the model is compared to the results of two previously reported experimental studies. The ability of the model to represent human decisionmaking as a function of network size, forced-pacing, and computer aiding is considered.

  5. Prediction of colorectal cancer diagnosis based on circulating plasma proteins.

    PubMed

    Surinova, Silvia; Choi, Meena; Tao, Sha; Schüffler, Peter J; Chang, Ching-Yun; Clough, Timothy; Vysloužil, Kamil; Khoylou, Marta; Srovnal, Josef; Liu, Yansheng; Matondo, Mariette; Hüttenhain, Ruth; Weisser, Hendrik; Buhmann, Joachim M; Hajdúch, Marián; Brenner, Hermann; Vitek, Olga; Aebersold, Ruedi

    2015-09-01

    Non-invasive detection of colorectal cancer with blood-based markers is a critical clinical need. Here we describe a phased mass spectrometry-based approach for the discovery, screening, and validation of circulating protein biomarkers with diagnostic value. Initially, we profiled human primary tumor tissue epithelia and characterized about 300 secreted and cell surface candidate glycoproteins. These candidates were then screened in patient systemic circulation to identify detectable candidates in blood plasma. An 88-plex targeting method was established to systematically monitor these proteins in two large and independent cohorts of plasma samples, which generated quantitative clinical datasets at an unprecedented scale. The data were deployed to develop and evaluate a five-protein biomarker signature for colorectal cancer detection. PMID:26253081

  6. A novel local learning based approach with application to breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Xu, Songhua; Tourassi, Georgia

    2012-03-01

    In this paper, we introduce a new local learning based approach and apply it for the well-studied problem of breast cancer diagnosis using BIRADS-based mammographic features. To learn from our clinical dataset the latent relationship between these features and the breast biopsy result, our method first dynamically partitions the whole sample population into multiple sub-population groups through stochastically searching the sample population clustering space. Each encountered clustering scheme in our online searching process is then used to create a certain sample population partition plan. For every resultant sub-population group identified according to a partition plan, our method then trains a dedicated local learner to capture the underlying data relationship. In our study, we adopt the linear logistic regression model as our local learning method's base learner. Such a choice is made both due to the well-understood linear nature of the problem, which is compellingly revealed by a rich body of prior studies, and the computational efficiency of linear logistic regression--the latter feature allows our local learning method to more effectively perform its search in the sample population clustering space. Using a database of 850 biopsy-proven cases, we compared the performance of our method with a large collection of publicly available state-of-the-art machine learning methods and successfully demonstrated its performance advantage with statistical significance.

  7. Model-Based Systems

    NASA Technical Reports Server (NTRS)

    Frisch, Harold P.

    2007-01-01

    Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.

  8. Diagnosis of pneumothorax using a microwave-based detector

    NASA Astrophysics Data System (ADS)

    Ling, Geoffrey S. F.; Riechers, Ronald G., Sr.; Pasala, Krishna M.; Blanchard, Jeremy; Nozaki, Masako; Ramage, Anthony; Jackson, William; Rosner, Michael; Garcia-Pinto, Patricia; Yun, Catherine; Butler, Nathan; Riechers, Ronald G., Jr.; Williams, Daniel; Zeidman, Seth M.; Rhee, Peter; Ecklund, James M.; Fitzpatrick, Thomas; Lockhart, Stephen

    2001-08-01

    A novel method for identifying pneumothorax is presented. This method is based on a novel device that uses electromagnetic waves in the microwave radio frequency (RF) region and a modified algorithm previously used for the estimation of the angle of arrival of radar signals. In this study, we employ this radio frequency triage tool (RAFT) to the clinical condition of pneumothorax, which is a collapsed lung. In anesthetized pigs, RAFT can detect changes in the RF signature from a lung that is 20 percent or greater collapsed. These results are compared to chest x-ray. Both studies are equivalent in their ability to detect pneumothorax in pigs.

  9. Models of human problem solving - Detection, diagnosis, and compensation for system failures

    NASA Technical Reports Server (NTRS)

    Rouse, W. B.

    1983-01-01

    The role of the human operator as a problem solver in man-machine systems such as vehicles, process plants, transportation networks, etc. is considered. Problem solving is discussed in terms of detection, diagnosis, and compensation. A wide variety of models of these phases of problem solving are reviewed and specifications for an overall model outlined.

  10. A Binary Programming Approach to Automated Test Assembly for Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Finkelman, Matthew D.; Kim, Wonsuk; Roussos, Louis; Verschoor, Angela

    2010-01-01

    Automated test assembly (ATA) has been an area of prolific psychometric research. Although ATA methodology is well developed for unidimensional models, its application alongside cognitive diagnosis models (CDMs) is a burgeoning topic. Two suggested procedures for combining ATA and CDMs are to maximize the cognitive diagnostic index and to use a…

  11. Toward Biological Diagnosis System Based on Digital Versatile Disc Technology

    NASA Astrophysics Data System (ADS)

    Arai, Tomofumi; Gopinath, Subash C. B.; Mizuno, Hiroshi; Kumar, Penmetcha K. R.; Rockstuhl, Carsten; Awazu, Koichi; Tominaga, Junji

    2007-06-01

    A novel biosensor utilizing an interference of light reflected at the interfaces of a multilayer structure is proposed. This biosensor detects analytes by monitoring the changes in reflection intensity due to their adsorption to the sensor surface, on which functional biomolecules are immobilized to specifically bind to the analytes. The proposed biosensing instrument is based on a commercial digital versatile disc (DVD) system, which allows the instrument to be small and inexpensive. For the preliminary examination, SiO2 thin films with a well-defined thickness were deposited on the sensor surface. The reflection intensity varied almost linearly depending on the thickness of the SiO2 films in a thickness range of 2-10 nm. Subsequently, it was demonstrated that biotin-streptavidin binding events were clearly detectable on a rotating disc substrate at a constant linear velocity of 4.0 m/s. We named this interference-based biosensor BioDVD, which is expected to be useful for high-throughput multi-analyte bioassays.

  12. Modeling the Diagnosis and Treatment of Pulmonary Embolism

    ERIC Educational Resources Information Center

    Pliskin, Nava; And Others

    1978-01-01

    The problem of acute pulmonary embolism is employed to illustrate that medical decision analysis is possible despite some of the difficulties encountered in previous application. The usefulness of computerized decision models is discussed. (LBH)

  13. Bead-based microfluidic immunoassay for diagnosis of Johne's disease

    SciTech Connect

    Wadhwa, Ashutosh; Foote, Robert; Shaw, Robert W; Eda, Shigetoshi

    2012-01-01

    Microfluidics technology offers a platform for development of point-of-care diagnostic devices for various infectious diseases. In this study, we examined whether serodiagnosis of Johne s disease (JD) can be conducted in a bead-based microfluidic assay system. Magnetic micro-beads were coated with antigens of the causative agent of JD, Mycobacterium avium subsp. paratuberculosis. The antigen-coated beads were incubated with serum samples of JD-positive or negative serum samples and then with a fluorescently-labeled secondary antibody (SAB). To confirm binding of serum antibodies to the antigen, the beads were subjected to flow cytometric analysis. Different conditions (dilutions of serum and SAB, types of SAB, and types of magnetic beads) were optimized for a great degree of differentiation between the JD-negative and JD-positive samples. Using the optimized conditions, we tested a well-classified set of 155 serum samples from JD negative and JD-positive cattle by using the bead-based flow cytometric assay. Of 105 JD-positive samples, 63 samples (60%) showed higher antibody binding levels than a cut-off value determined by using antibody binding levels of JD-negative samples. In contrast, only 43-49 JD-positive samples showed higher antibody binding levels than the cut-off value when the samples were tested by commercially-available immunoassays. Microfluidic assays were performed by magnetically immobilizing a number of beads within a microchannel of a glass microchip and detecting antibody on the collected beads by laser-induced fluorescence. Antigen-coated magnetic beads treated with bovine serum sample and fluorescently-labeled SAB were loaded into a microchannel to measure the fluorescence (reflecting level of antibody binding) on the beads in the microfluidic system. When the results of five bovine serum samples obtained with the system were compared to those obtained with the flow cytometer, a high level of correlation (linear regression, r2 = 0.994) was

  14. Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

    NASA Astrophysics Data System (ADS)

    Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

    2006-03-01

    Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

  15. Case-based reasoning as a computer aid to diagnosis

    NASA Astrophysics Data System (ADS)

    Floyd, Carey E., Jr.; Lo, Joseph Y.; Tourassi, Georgia D.

    1999-05-01

    A Case-Based Reasoning (CBR) system has been developed to predict the outcome of excisional biopsy from mammographic findings. CBR is implemented by comparing the current case to all previous cases and examining the outcomes for those previous cases that match the current case. Patients from breast screening who have suspicious findings on their diagnostic mammogram, are candidates for biopsy. The false positive rate for the decision to biopsy is currently between 66% and 90%. The CBR system is designed to support the decision to biopsy. The mammograms are read by clinicians using a standard reporting lexicon (BI- RADSTM). These findings are compared to a database of findings from cases with known outcomes (from biopsy). The fraction of similar cases that were malignant is returned. The clinician can then consider this result when making the decision regarding biopsy. The system was evaluated using round-robin sampling scheme and performed with a Receiver Operating Characteristic area of 0.77.

  16. Phantoms and computational models in therapy, diagnosis and protection

    SciTech Connect

    Not Available

    1992-01-01

    The development of realistic body phantoms and computational models is strongly dependent on the availability of comprehensive human anatomical data. This information is often missing, incomplete or not easily available. Therefore, emphasis is given in the Report to organ and body masses and geometries. The influence of age, sex and ethnic origins in human anatomy is considered. Suggestions are given on how suitable anatomical data can be either extracted from published information or obtained from measurements on the local population. Existing types of phantoms and computational models used with photons, electrons, protons and neutrons are reviewed in this Report. Specifications of those considered important to the maintenance and development of reliable radiation dosimetry and measurement are given. The information provided includes a description of the phantom or model, together with diagrams or photographs and physical dimensions. The tissues within body sections are identified and the tissue substitutes used or recommended are listed. The uses of the phantom or model in radiation dosimetry and measurement are outlined. The Report deals predominantly with phantom and computational models representing the human anatomy, with a short Section devoted to animal phantoms in radiobiology.

  17. Water Diagnosis in Shrimp Aquaculture based on Neural Network

    NASA Astrophysics Data System (ADS)

    Carbajal Hernández, J. J.; Sánchez Fernández, L. P.

    2007-05-01

    In many countries, the shrimp aquaculture has not advanced computational systems to supervise the artificial habitat of the farms and laboratories. A computational system of this type helps significantly to improve the environmental conditions and to elevate the production and its quality. The main idea of this study is the creation of a system using an artificial neural network (ANN), which can help to recognize patterns of problems and their evolution in shrimp aquaculture, and thus to respond with greater rapidity against the negative effects. Bad control on the shrimp artificial habitat produces organisms with high stress and as consequence losses in their defenses. It generate low nutrition, low reproduction or worse still, they prearrange to acquire lethal diseases. The proposed system helps to control this problem. Environmental variables as pH, temperature, salinity, dissolved oxygen and turbidity have an important effect in the suitable growth of the shrimps and influence in their health. However, the exact mathematical model of this relationship is unspecified; an ANN is useful for establishing a relationship between these variables and to classify a status that describes a problem into the farm. The data classification is made to recognize and to quantify two states within the pool: a) Normal: Everything is well. b) Risk: One, some or all environmental variables are outside of the allowed interval, which generates problems. The neural network will have to recognize the state and to quantify it, in others words, how normal or risky it is, which allows finding trend of the water quality. A study was developed for designing a software tool that allows recognizing the status of the water quality and control problems for the environment into the pond.

  18. Diagnosis of myocardial infarction based on lectin-induced erythrocyte agglutination: a feasibility study

    NASA Astrophysics Data System (ADS)

    Bocsi, József; Nieschke, Kathleen; Mittag, Anja; Reichert, Thomas; Laffers, Wiebke; Marecka, Monika; Pierzchalski, Arkadiusz; Piltz, Joachim; Esche, Hans-Jürgen; Wolf, Günther; Dähnert, Ingo; Baumgartner, Adolf; Tarnok, Attila

    2014-03-01

    Myocardial infarction (MI) is an acute life-threatening disease with a high incidence worldwide. Aim of this study was to test lectin-carbohydrate binding-induced red blood cell (RBC) agglutination as an innovative tool for fast, precise and cost effective diagnosis of MI. Five lectins (Ricinus communis agglutinin (RCA), Phaseolus vulgaris erythroagglutinin (PHA), Datura stramonium agglutinin (DSA), Artocarpus agglutinin (ArA), Triticum agglutinin (TA)) were tested for ability to differentiate between agglutination characteristics in patients with MI (n = 101) or angina pectoris without MI (AP) (n = 34) and healthy volunteers (HV) as control (n =68) . RBC agglutination was analyzed by light absorbance of a stirred RBC suspension in the green to red light spectrum in an agglutimeter (amtec, Leipzig, Germany) for 15 min after lectin addition. Mean cell count in aggregates was estimated from light absorbance by a mathematical model. Each lectin induced RBC agglutination. RCA led to the strongest RBC agglutination (~500 RBCs/aggregate), while the others induced substantially slower agglutination and lead to smaller aggregate sizes (5-150 RBCs/aggregate). For all analyzed lectins the lectin-induced RBC agglutination of MI or AP patients was generally higher than for HV. However, only PHA induced agglutination that clearly distinguished MI from HV. Variance analysis showed that aggregate size after 15 min. agglutination induced by PHA was significantly higher in the MI group (143 RBCs/ aggregate) than in the HV (29 RBC-s/aggregate, p = 0.000). We hypothesize that pathological changes during MI induce modification of the carbohydrate composition on the RBC membrane and thus modify RBC agglutination. Occurrence of carbohydrate-lectin binding sites on RBC membranes provides evidence about MI. Due to significant difference in the rate of agglutination between MI > HV the differentiation between these groups is possible based on PHA-induced RBC-agglutination. This novel assay

  19. Model Based Definition

    NASA Technical Reports Server (NTRS)

    Rowe, Sidney E.

    2010-01-01

    In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.

  20. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    PubMed

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

  1. Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

    PubMed Central

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  2. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    PubMed

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  3. Congenital neutropenia: diagnosis, molecular bases and patient management

    PubMed Central

    2011-01-01

    The term congenital neutropenia encompasses a family of neutropenic disorders, both permanent and intermittent, severe (<0.5 G/l) or mild (between 0.5-1.5 G/l), which may also affect other organ systems such as the pancreas, central nervous system, heart, muscle and skin. Neutropenia can lead to life-threatening pyogenic infections, acute gingivostomatitis and chronic parodontal disease, and each successive infection may leave permanent sequelae. The risk of infection is roughly inversely proportional to the circulating polymorphonuclear neutrophil count and is particularly high at counts below 0.2 G/l. When neutropenia is detected, an attempt should be made to establish the etiology, distinguishing between acquired forms (the most frequent, including post viral neutropenia and auto immune neutropenia) and congenital forms that may either be isolated or part of a complex genetic disease. Except for ethnic neutropenia, which is a frequent but mild congenital form, probably with polygenic inheritance, all other forms of congenital neutropenia are extremely rare and have monogenic inheritance, which may be X-linked or autosomal, recessive or dominant. About half the forms of congenital neutropenia with no extra-hematopoetic manifestations and normal adaptive immunity are due to neutrophil elastase (ELANE) mutations. Some patients have severe permanent neutropenia and frequent infections early in life, while others have mild intermittent neutropenia. Congenital neutropenia may also be associated with a wide range of organ dysfunctions, as for example in Shwachman-Diamond syndrome (associated with pancreatic insufficiency) and glycogen storage disease type Ib (associated with a glycogen storage syndrome). So far, the molecular bases of 12 neutropenic disorders have been identified. Treatment of severe chronic neutropenia should focus on prevention of infections. It includes antimicrobial prophylaxis, generally with trimethoprim-sulfamethoxazole, and also granulocyte

  4. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Dong; Tsui, Kwok-Leung; Zhou, Qiang

    2016-05-01

    Rolling element bearings are commonly used in machines to provide support for rotating shafts. Bearing failures may cause unexpected machine breakdowns and increase economic cost. To prevent machine breakdowns and reduce unnecessary economic loss, bearing faults should be detected as early as possible. Because wavelet transform can be used to highlight impulses caused by localized bearing faults, wavelet transform has been widely investigated and proven to be one of the most effective and efficient methods for bearing fault diagnosis. In this paper, a new Gauss-Hermite integration based Bayesian inference method is proposed to estimate the posterior distribution of wavelet parameters. The innovations of this paper are illustrated as follows. Firstly, a non-linear state space model of wavelet parameters is constructed to describe the relationship between wavelet parameters and hypothetical measurements. Secondly, the joint posterior probability density function of wavelet parameters and hypothetical measurements is assumed to follow a joint Gaussian distribution so as to generate Gaussian perturbations for the state space model. Thirdly, Gauss-Hermite integration is introduced to analytically predict and update moments of the joint Gaussian distribution, from which optimal wavelet parameters are derived. At last, an optimal wavelet filtering is conducted to extract bearing fault features and thus identify localized bearing faults. Two instances are investigated to illustrate how the proposed method works. Two comparisons with the fast kurtogram are used to demonstrate that the proposed method can achieve better visual inspection performances than the fast kurtogram.

  5. Image-based retrieval system and computer-aided diagnosis system for renal cortical scintigraphy images

    NASA Astrophysics Data System (ADS)

    Mumcuoğlu, Erkan; Nar, Fatih; Uğur, Omer; Bozkurt, M. Fani; Aslan, Mehmet

    2008-03-01

    Cortical renal (kidney) scintigraphy images are 2D images (256x256) acquired in three projection angles (posterior, right-posterior-oblique and left-posterior-oblique). These images are used by nuclear medicine specialists to examine the functional morphology of kidney parenchyma. The main visual features examined in reading the images are: size, location, shape and activity distribution (pixel intensity distribution within the boundary of each kidney). Among the above features, activity distribution (in finding scars if any) was found to have the least interobserver reproducibility. Therefore, in this study, we developed an image-based retrieval (IBR) and a computer-based diagnosis (CAD) system, focused on this feature in particular. The developed IBR and CAD algorithms start with automatic segmentation, boundary and landmark detection. Then, shape and activity distribution features are computed. Activity distribution feature is obtained using the acquired image and image set statistics of the normal patients. Active Shape Model (ASM) technique is used for more accurate kidney segmentation. In the training step of ASM, normal patient images are used. Retrieval performance is evaluated by calculating precision and recall. CAD performance is evaluated by specificity and sensitivity. To our knowledge, this paper is the first IBR or CAD system reported in the literature on renal cortical scintigraphy images.

  6. STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis.

    PubMed

    Zheng, Yuanjie; Englander, Sarah; Baloch, Sajjad; Zacharaki, Evangelia I; Fan, Yong; Schnall, Mitchell D; Shen, Dinggang

    2009-07-01

    The authors propose a spatiotemporal enhancement pattern (STEP) for comprehensive characterization of breast tumors in contrast-enhanced MR images. By viewing serial contrast-enhanced MR images as a single spatiotemporal image, they formulate the STEP as a combination of (1) dynamic enhancement and architectural features of a tumor, and (2) the spatial variations of pixelwise temporal enhancements. Although the latter has been widely used by radiologists for diagnostic purposes, it has rarely been employed for computer-aided diagnosis. This article presents two major contributions. First, the STEP features are introduced to capture temporal enhancement and its spatial variations. This is essentially carried out through the Fourier transformation and pharmacokinetic modeling of various temporal enhancement features, followed by the calculation of moment invariants and Gabor texture features. Second, for effectively extracting the STEP features from tumors, we develop a graph-cut based segmentation algorithm that aims at refining coarse manual segmentations of tumors. The STEP features are assessed through their diagnostic performance for differentiating between benign and malignant tumors using a linear classifier (along with a simple ranking-based feature selection) in a leave-one-out cross-validation setting. The experimental results for the proposed features exhibit superior performance, when compared to the existing approaches, with the area under the ROC curve approaching 0.97. PMID:19673218

  7. Discovering associations among diagnosis groups using topic modeling.

    PubMed

    Li, Ding Cheng; Thermeau, Terry; Chute, Christopher; Liu, Hongfang

    2014-01-01

    With the rapid growth of electronic medical records (EMR), there is an increasing need of automatically extract patterns or rules from EMR data with machine learning and data mining technqiues. In this work, we applied unsupervised statistical model, latent Dirichlet allocations (LDA), to cluster patient diagnoics groups from Rochester Epidemiology Projects (REP). The initial results show that LDA holds the potential for broad application in epidemiogloy as well as other biomedical studies due to its unsupervised nature and great interpretive power. PMID:25954576

  8. Early chronic kidney disease: diagnosis, management and models of care.

    PubMed

    Wouters, Olivier J; O'Donoghue, Donal J; Ritchie, James; Kanavos, Panos G; Narva, Andrew S

    2015-08-01

    Chronic kidney disease (CKD) is prevalent in many countries, and the costs associated with the care of patients with end-stage renal disease (ESRD) are estimated to exceed US$1 trillion globally. The clinical and economic rationale for the design of timely and appropriate health system responses to limit the progression of CKD to ESRD is clear. Clinical care might improve if early-stage CKD with risk of progression to ESRD is differentiated from early-stage CKD that is unlikely to advance. The diagnostic tests that are currently used for CKD exhibit key limitations; therefore, additional research is required to increase awareness of the risk factors for CKD progression. Systems modelling can be used to evaluate the impact of different care models on CKD outcomes and costs. The US Indian Health Service has demonstrated that an integrated, system-wide approach can produce notable benefits on cardiovascular and renal health outcomes. Economic and clinical improvements might, therefore, be possible if CKD is reconceptualized as a part of primary care. This Review discusses which early CKD interventions are appropriate, the optimum time to provide clinical care, and the most suitable model of care to adopt. PMID:26055354

  9. Optical Coherence Tomography-based Diagnosis of Polypoidal Choroidal Vasculopathy in Korean Patients

    PubMed Central

    Chang, Young Suk; Kim, Jong Woo; Lee, Tae Gon; Kim, Chul Gu

    2016-01-01

    Purpose To evaluate the efficacy of an optical coherence tomography (OCT)-based diagnosis of polypoidal choroidal vasculopathy (PCV) in Korean patients. Methods This retrospective, observational case series included 263 eyes of 263 patients (147 eyes with PCV and 116 eyes with typical exudative, age-related macular degeneration [AMD]) who had been diagnosed with treatment naïve exudative AMD. Eyes with three or more of the following OCT findings were diagnosed with PCV: multiple retinal pigment epithelial detachment (RPED), a sharp RPED peak, an RPED notch, a hyporeflective lumen representing polyps, and hyperreflective intraretinal hard exudates. The OCT-based diagnosis was compared with the gold-standard indocyanine green angiography-based method. The sensitivity and specificity of the OCT-based diagnosis was also estimated. An additional analysis was performed using a choroidal thickness criterion. Eyes with a subfoveal choroidal thickness greater than 300 µm were also diagnosed with PCV despite having only two OCT features. Results In eyes with PCV, three or more OCT features were observed in 126 of 147 eyes (85.7%), and the incidence of typical exudative AMD was 16 of 116 eyes (13.8%). The sensitivity and specificity of an OCT-based diagnosis were 85.7% and 86.2%, respectively. After applying the choroidal thickness criterion, the sensitivity increased from 85.7% to 89.8%, and the specificity decreased from 86.2% to 84.5%. Conclusions The OCT-based diagnosis of PCV showed a high sensitivity and specificity in Korean patients. The addition of a choroidal thickness criterion improved the sensitivity of the method with a minimal decrease in its specificity. PMID:27247519

  10. Screening and early diagnosis of osteoporosis through X-ray and ultrasound based techniques

    PubMed Central

    Pisani, Paola; Renna, Maria Daniela; Conversano, Francesco; Casciaro, Ernesto; Muratore, Maurizio; Quarta, Eugenio; Paola, Marco Di; Casciaro, Sergio

    2013-01-01

    Effective prevention and management of osteoporosis would require suitable methods for population screenings and early diagnosis. Current clinically-available diagnostic methods are mainly based on the use of either X-rays or ultrasound (US). All X-ray based methods provide a measure of bone mineral density (BMD), but it has been demonstrated that other structural aspects of the bone are important in determining fracture risk, such as mechanical features and elastic properties, which cannot be assessed using densitometric techniques. Among the most commonly used techniques, dual X-ray absorptiometry (DXA) is considered the current “gold standard” for osteoporosis diagnosis and fracture risk prediction. Unfortunately, as other X-ray based techniques, DXA has specific limitations (e.g., use of ionizing radiation, large size of the equipment, high costs, limited availability) that hinder its application for population screenings and primary care diagnosis. This has resulted in an increasing interest in developing reliable pre-screening tools for osteoporosis such as quantitative ultrasound (QUS) scanners, which do not involve ionizing radiation exposure and represent a cheaper solution exploiting portable and widely available devices. Furthermore, the usefulness of QUS techniques in fracture risk prediction has been proven and, with the last developments, they are also becoming a more and more reliable approach for assessing bone quality. However, the US assessment of osteoporosis is currently used only as a pre-screening tool, requiring a subsequent diagnosis confirmation by means of a DXA evaluation. Here we illustrate the state of art in the early diagnosis of this “silent disease” and show up recent advances for its prevention and improved management through early diagnosis. PMID:24349644

  11. Investigation of candidate data structures and search algorithms to support a knowledge based fault diagnosis system

    NASA Technical Reports Server (NTRS)

    Bosworth, Edward L., Jr.

    1987-01-01

    The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.

  12. Early diagnosis of Lemierre's syndrome based on a medical history and physical findings.

    PubMed

    Murata, Yutaka; Wada, Mikio; Kawashima, Atsushi; Kagawa, Keizo

    2013-01-01

    A 37-year-old woman presented with fever and rigor after experiencing respiratory symptoms the previous week that had resolved within a few days. On presentation, her neck was swollen along the right sternocleidomastoid muscle, and chest CT showed pulmonary septic embolisms. Lemierre's syndrome was strongly suspected based on the patient's medical history and physical findings. Further examination revealed venous thrombus, and Fusobacterium necrophorum was later isolated from blood cultures. Antibiotics for anaerobes were administered before a final diagnosis was made, and the patient's symptoms thereafter improved. A rapid diagnosis is essential, since Lemierre's syndrome can be fatal with a diagnostic delay. PMID:23318865

  13. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1998-01-01

    Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).

  14. Model-based reasoning in SSF ECLSS

    NASA Technical Reports Server (NTRS)

    Miller, J. K.; Williams, George P. W., Jr.

    1992-01-01

    The interacting processes and reconfigurable subsystems of the Space Station Freedom Environmental Control and Life Support System (ECLSS) present a tremendous technical challenge to Freedom's crew and ground support. ECLSS operation and problem analysis is time-consuming for crew members and difficult for current computerized control, monitoring, and diagnostic software. These challenges can be at least partially mitigated by the use of advanced techniques such as Model-Based Reasoning (MBR). This paper will provide an overview of MBR as it is being applied to Space Station Freedom ECLSS. It will report on work being done to produce intelligent systems to help design, control, monitor, and diagnose Freedom's ECLSS. Specifically, work on predictive monitoring, diagnosability, and diagnosis, with emphasis on the automated diagnosis of the regenerative water recovery and air revitalization processes will be discussed.

  15. Sequential fuzzy diagnosis method for motor roller bearing in variable operating conditions based on vibration analysis.

    PubMed

    Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi

    2013-01-01

    A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well. PMID:23793021

  16. Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis

    PubMed Central

    Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi

    2013-01-01

    A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well. PMID:23793021

  17. Rapid immunohistochemistry based on alternating current electric field for intraoperative diagnosis of brain tumors.

    PubMed

    Tanino, Mishie; Sasajima, Toshio; Nanjo, Hiroshi; Akesaka, Shiori; Kagaya, Masami; Kimura, Taichi; Ishida, Yusuke; Oda, Masaya; Takahashi, Masataka; Sugawara, Taku; Yoshioka, Toshiaki; Nishihara, Hiroshi; Akagami, Yoichi; Goto, Akiteru; Minamiya, Yoshihiro; Tanaka, Shinya

    2015-01-01

    Rapid immunohistochemistry (R-IHC) can contribute to the intraoperative diagnosis of central nervous system (CNS) tumors. We have recently developed a new IHC method based on an alternating current electric field to facilitate the antigen-antibody reaction. To ensure the requirement of R-IHC for intraoperative diagnosis, 183 cases of CNS tumors were reviewed regarding the accuracy rate of diagnosis without R-IHC. The diagnostic accuracy was 90.7 % (166/183 cases) [corrected] in which definitive diagnoses were not provided in 17 cases because of the failure of glioma grading and differential diagnosis of lymphoma and glioma. To establish the clinicopathological application, R-IHC for frozen specimens was compared with standard IHC for permanent specimens. 33 gliomas were analyzed, and the Ki-67/MIB-1 indices of frozen specimens by R-IHC were consistent with the grade and statistically correlated with those of permanent specimens. Thus, R-IHC provided supportive information to determine the grade of glioma. For discrimination between glioma and lymphoma, R-IHC was able to provide clear results of CD20 and Ki-67/MIB-1 in four frozen specimens of CNS lymphoma as well as standard IHC. We conclude that the R-IHC for frozen specimens can provide important information for intraoperative diagnosis of CNS tumors. PMID:24807101

  18. Design of fault diagnosis system for inertial navigation system based on virtual technology

    NASA Astrophysics Data System (ADS)

    Hu, Baiqing; Wang, Boxiong; Li, An; Zhang, Mingzhao; Qin, Fangjun; Pan, Hua

    2006-11-01

    With regard to the complex structure of the inertial navigation system and the low rate of fault detection with BITE (built-in test equipment), a fault diagnosis system for INS based on virtual technologies (virtual instrument and virtual equipment) is proposed in this paper. The hardware of the system is a PXI computer with highly stable performance and strong extensibility. In addition to the basic functions of digital multimeter, oscilloscope and cymometer, it can also measure the attitude of the ship in real-time, connect and control the measurement instruments with digital interface. The software is designed with the languages of Measurement Studio for VB, JAVA, and CULT3D. Using the extensively applied fault-tree reasoning and fault cases makes fault diagnosis. To suit the system to the diagnosis for various navigation electronic equipments, the modular design concept is adopted for the software programming. Knowledge of the expert system is digitally processed and the parameters of the system's interface and the expert diagnosis knowledge are stored in the database. The application shows that system is stable in operation, easy to use, quick and accurate in fault diagnosis.

  19. AMFESYS: Modelling and diagnosis functions for operations support

    NASA Technical Reports Server (NTRS)

    Wheadon, J.

    1993-01-01

    Packetized telemetry, combined with low station coverage for close-earth satellites, may introduce new problems in presenting to the operator a clear picture of what the spacecraft is doing. A recent ESOC study has gone some way to show, by means of a practical demonstration, how the use of subsystem models combined with artificial intelligence techniques, within a real-time spacecraft control system (SCS), can help to overcome these problems. A spin-off from using these techniques can be an improvement in the reliability of the telemetry (TM) limit-checking function, as well as the telecommand verification function, of the Spacecraft Control systems (SCS). The problem and how it was addressed, including an overview of the 'AMF Expert System' prototype are described, and proposes further work which needs to be done to prove the concept. The Automatic Mirror Furnace is part of the payload of the European Retrievable Carrier (EURECA) spacecraft, which was launched in July 1992.

  20. A knowledge-based system for diagnosis of mastitis problems at the herd level. 2. Machine milking.

    PubMed

    Hogeveen, H; van Vliet, J H; Noordhuizen-Stassen, E N; De Koning, C; Tepp, D M; Brand, A

    1995-07-01

    A knowledge-based system for the diagnosis of mastitis problems at the herd level must search for possible causes, including malfunctioning milking machines or incorrect milking technique. A knowledge-based system on general mechanisms of mastitis infection, using hierarchical conditional causal models, was extended. Model building entailed extensive cooperation between the knowledge engineer and a domain expert. The extended knowledge-based system contains 12 submodels underlying the overview models. Nine submodels were concerned with mastitis problems arising from machine milking. These models are briefly described. The knowledge-based system has been validated by other experts after which the models were adjusted slightly. The final knowledge-based system was validated to data collected at 17 commercial dairy farms with high SCC in the bulk milk. Reports containing the farm data were accompanied by recommendations made by a dairy farm advisor. This validation showed good agreement between the knowledge-based system and the dairy farm advisors. The described knowledge-based system is a good tool for dairy farm advisors to solve herd mastitis problems caused by a malfunctioning milking machine or incorrect milking technique. PMID:7593837

  1. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    PubMed Central

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  2. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    PubMed

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  3. Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, M.; Hu, N. Q.; Qin, G. J.

    2011-07-01

    In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

  4. Rule-based fault diagnosis of hall sensors and fault-tolerant control of PMSM

    NASA Astrophysics Data System (ADS)

    Song, Ziyou; Li, Jianqiu; Ouyang, Minggao; Gu, Jing; Feng, Xuning; Lu, Dongbin

    2013-07-01

    Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

  5. The diagnosis of hyper immunoglobulin e syndrome based on project management.

    PubMed

    Saghafi, Shiva; Pourpak, Zahra; Glocker, Cristina; Nussbaumer, Franziska; Babamahmoodi, Abdolreza; Grimbacher, Bodo; Moin, Mostafa

    2015-04-01

    Hyperimmunoglobulin E Syndrome (HIES) is a complex primary immunodeficiency characterized by both immunologic and non-immunologic manifestations. High serum IgE level, eosinophilia, eczema, recurrent skin and lung infections constitute the immunologic profile of HIES, whereas characteristic facial appearance, scoliosis, retained primary teeth, joint hyperextensibility, bone fractures following minimal trauma and craniosynostosis are the main non-immunologic manifestations. The diagnosis of HIES cannot be made by routine immunologic tests. As the main characteristic laboratory abnormalities of this syndrome are highly elevated serum IgE levels and eosinophilia; both features have a broad spectrum of differential diagnosis. The purpose of this essay was presenting the best way for diagnosis management of HIES. Based on the genetic reports of patients of the Center for Chronic Immunodeficiency (CCI) as a single center experience, and applying project management (PM) in health care research projects, we sought the best way for a rapid diagnosis of HIES. The combination of project management principles with immunologic and genetic knowledge to better define the laboratory and clinical diagnosis lead to an improvement of the management of patients with HIES. These results are shown in one "Decision Tree" which is based on 342 genetic reports of the CCI during the past ten years. It is necessary to facilitate the diagnostic analysis of suspected HIES patients; applying project management in health care research projects provides a better and more accurate diagnosis eventually leading to a better patients' care. This Abstract was presented at 16th Biennial Meeting of the European Society for Immunodeficiencies (ESID 2014), Prague, Czech Republic. PMID:25780878

  6. A Physiological Signal Transmission Model to be Used for Specific Diagnosis of Cochlear Impairments

    NASA Astrophysics Data System (ADS)

    Saremi, Amin; Stenfelt, Stefan

    2011-11-01

    Many of the sophisticated characteristics of human auditory system are attributed to cochlea. Also, most of patients with a hearing loss suffer from impairments that originate from cochlea (sensorineural). Despite this, today's clinical diagnosis methods do not probe the specific origins of such cochlear lesions. The aim of this research is to introduce a physiological signal transmission model to be clinically used as a tool for diagnosis of cochlear losses. This model enables simulation of different bio-mechano-electrical processes which occur in the auditory organ of Corti inside the cochlea. What makes this model different from many available computational models is its loyalty to physiology since the ultimate goal is to model each single physiological phenomenon. This includes passive BM vibration, outer hair cells' performances such as nonlinear mechanoelectrical transduction (MET), active amplifications by somatic motor, as well as vibration to neural conversion at the inner hair cells.

  7. Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

    ERIC Educational Resources Information Center

    Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

    2009-01-01

    Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

  8. Fault diagnosis of direct-drive wind turbine based on support vector machine

    NASA Astrophysics Data System (ADS)

    An, X. L.; Jiang, D. X.; Li, S. H.; Chen, J.

    2011-07-01

    A fault diagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.

  9. Measurement Error: Implications for Diagnosis and Discrepancy Models of Developmental Dyslexia

    ERIC Educational Resources Information Center

    Cotton, Sue M.; Crewther, David P.; Crewther, Sheila G.

    2005-01-01

    The diagnosis of developmental dyslexia (DD) is reliant on a discrepancy between intellectual functioning and reading achievement. Discrepancy-based formulae have frequently been employed to establish the significance of the difference between "intelligence" and "actual" reading achievement. These formulae, however, often fail to take into…

  10. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    PubMed

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. PMID:26626623

  11. The Usefulness of Clinical-Practice-Based Laboratory Data in Facilitating the Diagnosis of Dengue Illness

    PubMed Central

    Liu, Jien-Wei; Lee, Ing-Kit; Wang, Lin; Chen, Rong-Fu; Yang, Kuender D.

    2013-01-01

    Alertness to dengue and making a timely diagnosis is extremely important in the treatment of dengue and containment of dengue epidemics. We evaluated the complementary role of clinical-practice-based laboratory data in facilitating suspicion/diagnosis of dengue. One hundred overall dengue (57 dengue fever [DF] and 43 dengue hemorrhagic fever [DHF]) cases and another 100 nondengue cases (78 viral infections other than dengue, 6 bacterial sepsis, and 16 miscellaneous diseases) were analyzed. We separately compared individual laboratory variables (platelet count [PC] , prothrombin time [PT], activated partial thromboplastin time [APTT], alanine aminotransferase [ALT], and aspartate aminotransferase [AST]) and varied combined variables of DF and/or DHF cases with the corresponding ones of nondengue cases. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) in the diagnosis of DF and/or DHF were measured based on these laboratory variables. While trade-off between sensitivity and specificity, and/or suboptimal PPV/NPV was found at measurements using these variables, prolonged APTT + normal PT + PC < 100 × 109 cells/L had a favorable sensitivity, specificity, PPV, and NPV in diagnosis of DF and/or DHF. In conclusion, these data suggested that prolonged APTT + normal PT + PC < 100 × 109 cells/L is useful in evaluating the likelihood of DF and/or DHF. PMID:24455678

  12. Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features.

    PubMed

    Song, Jae-Won; Lee, Ju-Hong; Choi, Joon-Hyuk; Chun, Seok-Ju

    2013-01-01

    Generally, pathological diagnosis using an electron microscope is time-consuming and likely to result in a subjective judgment, because pathologists perform manual screening of tissue slides at high magnifications. Recently, the advent of digital pathology technology has provided the basis for convenient screening and quantitative analysis by digitizing tissue slides through a computer system. However, a screening process with high magnification still takes quite a long time. To solve these problems, recently the use of computer-aided design techniques for performing pathologic diagnosis has been increasing in digital pathology. For pathological diagnosis, we need different diagnostic methods for different regions with different characteristics. Therefore, in order to effectively diagnose different lesions and types of diseases, a quantitative method for extracting specific features is required in computerized pathologic diagnosis. This study is about an automated differential diagnosis system to differentiate between benign serous cystadenoma and possibly-malignant mucinous cystadenoma. In order to diagnose cystic tumors, the first step is identifying a cystic region and inspecting its epithelial cells. First, we identify the lumen boundary of a cyst using the Direction Cumulative Map considering 8-ways. Then, the Epithelial Nuclei Identification algorithm is used to discern epithelial nuclei. After that, three morphological features for the differential diagnosis of mucinous and serous cystadenomas are extracted. To demonstrate the superiority of the proposed features, the experiments compared performance of the classifiers learned by using the proposed morphological features and the classical morphological features based on nuclei. The classifiers in the simulations are as follows; Bayesian Classifier, k-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. The results show that all classifiers using the proposed features have the best

  13. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Zhao, H. L.; Gu, F.; Ball, A. D.

    2012-05-01

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods.

  14. Medium-Based Noninvasive Preimplantation Genetic Diagnosis for Human α-Thalassemias-SEA

    PubMed Central

    Wu, Haitao; Ding, Chenhui; Shen, Xiaoting; Wang, Jing; Li, Rong; Cai, Bing; Xu, Yanwen; Zhong, Yiping; Zhou, Canquan

    2015-01-01

    Abstract To develop a noninvasive medium-based preimplantation genetic diagnosis (PGD) test for α-thalassemias-SEA. The embryos of α-thalassemia-SEA carriers undergoing in vitro fertilization (IVF) were cultured. Single cells were biopsied from blastomeres and subjected to fluorescent gap polymerase chain reaction (PCR) analysis; the spent culture media that contained embryo genomic DNA and corresponding blastocysts as verification were subjected to quantitative-PCR (Q-PCR) detection of α-thalassemia-SEA. The diagnosis efficiency and allele dropout (ADO) ratio were calculated, and the cell-free DNA concentration was quantitatively assessed in the culture medium. The diagnosis efficiency of medium-based α-thalassemias–SEA detection significantly increased compared with that of biopsy-based fluorescent gap PCR analysis (88.6% vs 82.1%, P < 0.05). There is no significant difference regarding ADO ratio between them. The optimal time for medium-based α-thalassemias–SEA detection is Day 5 (D5) following IVF. Medium-based α-thalassemias–SEA detection could represent a novel, quick, and noninvasive approach for carriers to undergo IVF and PGD. PMID:25816038

  15. Plus Disease in Retinopathy of Prematurity: Pilot Study of Computer-Based and Expert Diagnosis

    PubMed Central

    Gelman, Rony; Jiang, Lei; Du, Yunling E.; Martinez-Perez, M. Elena; Flynn, John T.; Chiang, Michael F.

    2008-01-01

    Purpose To measure accuracy of plus disease diagnosis by recognized experts in retinopathy of prematurity (ROP), and to conduct a pilot study examining performance of a computer-based image analysis system, Retinal Image multiScale Analysis (RISA). Methods Twenty-two ROP experts independently interpreted a set of 34 wide-angle retinal images for presence of plus disease. A reference standard diagnosis based on expert consensus was defined for each image. Images were analyzed by the computer-based system using individual and linear combinations of system parameters for arterioles and venules: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis compared to the reference standard were determined for each expert, as well as for the computer-based system. Results Expert sensitivity ranged from 0.308–1.000, specificity ranged from 0.571–1.000, and AUC ranged from 0.784–1.000. Among individual computer system parameters, venular IC had highest AUC (0.853). Among all computer system parameters, the linear combination of arteriolar IC, arteriolar TI, venular IC, venular diameter, and venular TI had highest AUC (0.967), which was greater than that of 18 (81.8%) of 22 experts. Conclusions Accuracy of ROP experts for plus disease diagnosis is imperfect. A computer-based image analysis system has potential to diagnose plus disease with high accuracy. Further research involving RISA system parameter cut-off values from this study are required to fully validate performance of this computer-based system compared to that of human experts. PMID:18029210

  16. Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

    PubMed Central

    2010-01-01

    Background Medication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan. Methods The Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling. Results The concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R2 = 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R2 = 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R2 = 0.382) performed better than the models adjusted by Rx-MGs (R2 = 0.360) or ADGs (R2 = 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R2 = 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R2 = 0.638 and 0.505, respectively). The prospective models showed a

  17. Electrochemistry-based Battery Modeling for Prognostics

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew J.; Kulkarni, Chetan Shrikant

    2013-01-01

    Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

  18. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    NASA Astrophysics Data System (ADS)

    Li, Chuan; Sanchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

    2016-08-01

    Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes.

  19. Finding future high-cost cases: comparing prior cost versus diagnosis-based methods.

    PubMed Central

    Ash, A S; Zhao, Y; Ellis, R P; Schlein Kramer, M

    2001-01-01

    OBJECTIVE: To examine the value of two kinds of patient-level dat a (cost and diagnoses) for identifying a very small subgroup of a general population with high future costs that may be mitigated with medical management. DATA SOURCES: The study used the MEDSTAT Market Scan (R) Research Database, consisting of inpatient and ambulatory health care encounter records for individuals covered by employee- sponsored benefit plans during 1997 and 1998. STUDY DESIGN: Prior cost and a diagnostic cost group (DCG) risk model were each used with 1997 data to identify 0.5-percent-sized "top groups" of people most likely to be expensive in 1998. We compared the distributions of people, cost, and diseases commonly targeted for disease management for people in the two top groups and, as a bench mark, in the full population. PRINCIPAL FINDINGS: the prior cost- and DCG-identified top groups overlapped by only 38 percent. Each top group consisted of people with high year-two costs and high rates of diabetes, heart failure, major lung disease, and depression. The DCG top group identified people who are both somewhat more expensive ($27,292 vs. $25,981) and more likely ( 49.4 percent vs. 43.8 percent ) th an the prior-cost top group to have at least one of the diseases commonly targeted for disease management. The overlap group average cost was $46,219. CONCLUSIONS: Diagnosis-based risk models are at least as powerful as prior cost for identifying people who will be expensive. Combined cost and diagnostic data are even more powerful and more operation ally useful, especially because the diagnostic information identifies the medical problems that may be managed to achieve better out comes and lower costs. PMID:16148969

  20. Online motor fault detection and diagnosis using a hybrid FMM-CART model.

    PubMed

    Seera, Manjeevan; Lim, Chee Peng

    2014-04-01

    In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks. PMID:24807956

  1. Diagnosis and treatment of Merkel Cell Carcinoma. European consensus-based interdisciplinary guideline.

    PubMed

    Lebbe, Celeste; Becker, Jürgen C; Grob, Jean-Jacques; Malvehy, Josep; Del Marmol, Veronique; Pehamberger, Hubert; Peris, Ketty; Saiag, Philippe; Middleton, Mark R; Bastholt, Lars; Testori, Alessandro; Stratigos, Alexander; Garbe, Claus

    2015-11-01

    Merkel cell carcinoma (MCC) is a rare tumour of the skin of neuro-endocrine origin probably developing from neuronal mechanoreceptors. A collaborative group of multidisciplinary experts form the European Dermatology Forum (EDF), The European Association of Dermato-Oncology (EADO) and the European Organization of Research and Treatment of Cancer (EORTC) was formed to make recommendations on MCC diagnosis and management, based on a critical review of the literature, existing guidelines and expert's experience. Clinical features of the cutaneous/subcutaneous nodules hardly contribute to the diagnosis of MCC. The diagnosis is made by histopathology, and an incisional or excisional biopsy is mandatory. Immunohistochemical staining contributes to clarification of the diagnosis. Initial work-up comprises ultrasound of the loco-regional lymph nodes and total body scanning examinations. The primary tumour should be excised with 1-2cm margins. In patients without clinical evidence of regional lymph node involvement, sentinel node biopsy is recommended, if possible, and will be taken into account in a new version of the AJCC classification. In patients with regional lymph node involvement radical lymphadenectomy is recommended. Adjuvant radiotherapy might be considered in patients with multiple affected lymph nodes of extracapsular extension. In unresectable metastatic MCC mono- or poly-chemotherapy achieve high remission rates. However, responses are usually short lived. Treatment within clinical trials is regarded as a standard of care in disseminated MCC. PMID:26257075

  2. A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

    2015-08-01

    Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

  3. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

    PubMed Central

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2016-01-01

    Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

  4. Design-based approach to ethics in computer-aided diagnosis

    NASA Astrophysics Data System (ADS)

    Collmann, Jeff R.; Lin, Jyh-Shyan; Freedman, Matthew T.; Wu, Chris Y.; Hayes, Wendelin S.; Mun, Seong K.

    1996-04-01

    A design-based approach to ethical analysis examines how computer scientists, physicians and patients make and justify choices in designing, using and reacting to computer-aided diagnosis (CADx) systems. The basic hypothesis of this research is that values are embedded in CADx systems during all phases of their development, not just retrospectively imposed on them. This paper concentrates on the work of computer scientists and physicians as they attempt to resolve central technical questions in designing clinically functional CADx systems for lung cancer and breast cancer diagnosis. The work of Lo, Chan, Freedman, Lin, Wu and their colleagues provides the initial data on which this study is based. As these researchers seek to increase the rate of true positive classifications of detected abnormalities in chest radiographs and mammograms, they explore dimensions of the fundamental ethical principal of beneficence. The training of CADx systems demonstrates the key ethical dilemmas inherent in their current design.

  5. Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques

    NASA Astrophysics Data System (ADS)

    Meyer-Baese, Anke; Wismueller, Axel; Lange, Oliver; Leinsinger, Gerda

    2004-04-01

    Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammography as a first step of a computer-aided diagnosis system. Three new unsupervised clustering techniques are tested on biomedical time-series representing breast MRI scans: fuzzy clustering based on deterministic annealing, "neural gas" network, and topographic independent component analysis. While the first two methods enable a correct segmentation of the lesion, the latter, although incorporating a topographic mapping, fails to detect and subclassify lesions.

  6. Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity

    PubMed Central

    Wittenberg, Leah A.; Jonsson, Nina J.; Chan, RV Paul; Chiang, Michael F.

    2014-01-01

    Presence of plus disease in retinopathy of prematurity (ROP) is an important criterion for identifying treatment-requiring ROP. Plus disease is defined by a standard published photograph selected over 20 years ago by expert consensus. However, diagnosis of plus disease has been shown to be subjective and qualitative. Computer-based image analysis, using quantitative methods, has potential to improve the objectivity of plus disease diagnosis. The objective was to review the published literature involving computer-based image analysis for ROP diagnosis. The PubMed and Cochrane library databases were searched for the keywords “retinopathy of prematurity” AND “image analysis” AND/OR “plus disease.” Reference lists of retrieved articles were searched to identify additional relevant studies. All relevant English-language studies were reviewed. There are four main computer-based systems, ROPtool (AU ROC curve, plus tortuosity 0.95, plus dilation 0.87), RISA (AU ROC curve, arteriolar TI 0.71, venular diameter 0.82), Vessel Map (AU ROC curve, arteriolar dilation 0.75, venular dilation 0.96), and CAIAR (AU ROC curve, arteriole tortuosity 0.92, venular dilation 0.91), attempting to objectively analyze vessel tortuosity and dilation in plus disease in ROP. Some of them show promise for identification of plus disease using quantitative methods. This has potential to improve the diagnosis of plus disease, and may contribute to the management of ROP using both traditional binocular indirect ophthalmoscopy and image-based telemedicine approaches. PMID:21366159

  7. Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis

    PubMed Central

    Ortiz, Andrés; Munilla, Jorge; Álvarez-Illán, Ignacio; Górriz, Juan M.; Ramírez, Javier

    2015-01-01

    Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the

  8. Single nucleotide polymorphism-based microarray analysis for the diagnosis of hydatidiform moles

    PubMed Central

    XIE, YINGJUN; PEI, XIAOJUAN; DONG, YU; WU, HUIQUN; WU, JIANZHU; SHI, HUIJUAN; ZHUANG, XUYING; SUN, XIAOFANG; HE, JIALING

    2016-01-01

    In clinical diagnostics, single nucleotide polymorphism (SNP)-based microarray analysis enables the detection of copy number variations (CNVs), as well as copy number neutral regions, that are absent of heterozygosity throughout the genome. The aim of the present study was to evaluate the effectiveness and sensitivity of SNP-based microarray analysis in the diagnosis of hydatidiform mole (HM). By using whole-genome SNP microarray analysis, villous genotypes were detected, and the ploidy of villous tissue was determined to identify HMs. A total of 66 villous tissues and two twin tissues were assessed in the present study. Among these samples, 11 were triploid, one was tetraploid, 23 were abnormal aneuploidy, three were complete genome homozygosity, and the remaining ones were normal ploidy. The most noteworthy finding of the present study was the identification of six partial HMs and three complete HMs from those samples that were not identified as being HMs on the basis of the initial diagnosis of experienced obstetricians. This study has demonstrated that the application of an SNP-based microarray analysis was able to increase the sensitivity of diagnosis for HMs with partial and complete HMs, which makes the identification of these diseases at an early gestational age possible. PMID:27151252

  9. Single nucleotide polymorphism-based microarray analysis for the diagnosis of hydatidiform moles.

    PubMed

    Xie, Yingjun; Pei, Xiaojuan; Dong, Yu; Wu, Huiqun; Wu, Jianzhu; Shi, Huijuan; Zhuang, Xuying; Sun, Xiaofang; He, Jialing

    2016-07-01

    In clinical diagnostics, single nucleotide polymorphism (SNP)-based microarray analysis enables the detection of copy number variations (CNVs), as well as copy number neutral regions, that are absent of heterozygosity throughout the genome. The aim of the present study was to evaluate the effectiveness and sensitivity of SNP‑based microarray analysis in the diagnosis of hydatidiform mole (HM). By using whole‑genome SNP microarray analysis, villous genotypes were detected, and the ploidy of villous tissue was determined to identify HMs. A total of 66 villous tissues and two twin tissues were assessed in the present study. Among these samples, 11 were triploid, one was tetraploid, 23 were abnormal aneuploidy, three were complete genome homozygosity, and the remaining ones were normal ploidy. The most noteworthy finding of the present study was the identification of six partial HMs and three complete HMs from those samples that were not identified as being HMs on the basis of the initial diagnosis of experienced obstetricians. This study has demonstrated that the application of an SNP‑based microarray analysis was able to increase the sensitivity of diagnosis for HMs with partial and complete HMs, which makes the identification of these diseases at an early gestational age possible. PMID:27151252

  10. Towards a physiologically based diagnosis of anorexia nervosa and bulimia nervosa.

    PubMed

    Hatch, Kent A; Spangler, Diane L; Backus, Elizabeth M; Balagna, Jonathan T; Burns, Keven S; Guzman, Brooke S; Hubbard, Matthew J; Lindblad, Stephanie L; Roeder, Beverly L; Ryther, Natalie E; Seawright, Max A; Tyau, Jaymie N; Williams, Dustin

    2007-11-01

    Diagnosis of anorexia nervosa (AN) and bulimia nervosa (BN), while including such physiological data as weight and the reproductive status of the individual, are primarily based on questionnaires and interviews that rely on self-report of both body-related concerns and eating-related behaviors. While some key components of eating disorders are psychological and thus introspective in nature, reliance on self-report for the assessment of eating-related behaviors and nutritional status lacks the objectivity that a physiologically based measure could provide. The development of a more physiologically informed diagnosis for AN and BN would provide a more objective means of diagnosing these disorders, provide a sound physiological basis for diagnosing subclinical disorders and could also aid in monitoring the effectiveness of treatments for these disorders. Empirically supported, physiologically based methods for diagnosing AN and BN are reviewed herein as well as promising physiological measures that may potentially be used in the diagnosis of AN and BN. PMID:18020913

  11. Condition-based diagnosis of mechatronic systems using a fractional calculus approach

    NASA Astrophysics Data System (ADS)

    Gutiérrez-Carvajal, Ricardo Enrique; Flávio de Melo, Leonimer; Maurício Rosário, João; Tenreiro Machado, J. A.

    2016-07-01

    While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model's complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

  12. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Wang, T.; Gu, F.; Ball, A. D.

    2013-01-01

    Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time

  13. Early diagnosis and Early Start Denver Model intervention in autism spectrum disorders delivered in an Italian Public Health System service

    PubMed Central

    Devescovi, Raffaella; Monasta, Lorenzo; Mancini, Alice; Bin, Maura; Vellante, Valerio; Carrozzi, Marco; Colombi, Costanza

    2016-01-01

    Background Early diagnosis combined with an early intervention program, such as the Early Start Denver Model (ESDM), can positively influence the early natural history of autism spectrum disorders. This study evaluated the effectiveness of an early ESDM-inspired intervention, in a small group of toddlers, delivered at low intensity by the Italian Public Health System. Methods Twenty-one toddlers at risk for autism spectrum disorders, aged 20–36 months, received 3 hours/wk of one-to-one ESDM-inspired intervention by trained therapists, combined with parents’ and teachers’ active engagement in ecological implementation of treatment. The mean duration of treatment was 15 months. Cognitive and communication skills, as well as severity of autism symptoms, were assessed by using standardized measures at pre-intervention (Time 0 [T0]; mean age =27 months) and post-intervention (Time 1 [T1]; mean age =42 months). Results Children made statistically significant improvements in the language and cognitive domains, as demonstrated by a series of nonparametric Wilcoxon tests for paired data. Regarding severity of autism symptoms, younger age at diagnosis was positively associated with greater improvement at post-assessment. Conclusion Our results are consistent with the literature that underlines the importance of early diagnosis and early intervention, since prompt diagnosis can reduce the severity of autism symptoms and improve cognitive and language skills in younger children. Particularly in toddlers, it seems that an intervention model based on the ESDM principles, involving the active engagement of parents and nursery school teachers, may be effective even when the individual treatment is delivered at low intensity. Furthermore, our study supports the adaptation and the positive impact of the ESDM entirely sustained by the Italian Public Health System. PMID:27366069

  14. An Antibody-based Blood Test Utilizing a Panel of Biomarkers as a New Method for Improved Breast Cancer Diagnosis.

    PubMed

    Yahalom, Galit; Weiss, Daria; Novikov, Ilya; Bevers, Therese B; Radvanyi, Laszlo G; Liu, Mei; Piura, Benjamin; Iacobelli, Stefano; Sandri, Maria T; Cassano, Enrico; Allweis, Tanir M; Bitterman, Arie; Engelman, Pnina; Vence, Luis M; Rosenberg, Marvin M

    2013-01-01

    In order to develop a new tool for diagnosis of breast cancer based on autoantibodies against a panel of biomarkers, a clinical trial including blood samples from 507 subjects was conducted. All subjects showed a breast abnormality on exam or breast imaging and final biopsy pathology of either breast cancer patients or healthy controls. Using an enzyme-linked immunosorbent assay, the samples were tested for autoantibodies against a predetermined number of biomarkers in various models that were used to determine a diagnosis, which was compared to the clinical status. Our new assay achieved a sensitivity of 95.2% [CI = 92.8-96.8%] at a fixed specificity of 49.5%. Receiver-operator characteristic curve analysis showed an area under the curve of 80.1% [CI = 72.6-87.6%]. These results suggest that a blood test which is based on models comprising several autoantibodies to specific biomarkers may be a new and novel tool for improving the diagnostic evaluation of breast cancer. PMID:24324350

  15. An Antibody-based Blood Test Utilizing a Panel of Biomarkers as a New Method for Improved Breast Cancer Diagnosis

    PubMed Central

    Yahalom, Galit; Weiss, Daria; Novikov, Ilya; Bevers, Therese B.; Radvanyi, Laszlo G.; Liu, Mei; Piura, Benjamin; Iacobelli, Stefano; Sandri, Maria T.; Cassano, Enrico; Allweis, Tanir M.; Bitterman, Arie; Engelman, Pnina; Vence, Luis M.; Rosenberg, Marvin M.

    2013-01-01

    In order to develop a new tool for diagnosis of breast cancer based on autoantibodies against a panel of biomarkers, a clinical trial including blood samples from 507 subjects was conducted. All subjects showed a breast abnormality on exam or breast imaging and final biopsy pathology of either breast cancer patients or healthy controls. Using an enzyme-linked immunosorbent assay, the samples were tested for autoantibodies against a predetermined number of biomarkers in various models that were used to determine a diagnosis, which was compared to the clinical status. Our new assay achieved a sensitivity of 95.2% [CI = 92.8–96.8%] at a fixed specificity of 49.5%. Receiver-operator characteristic curve analysis showed an area under the curve of 80.1% [CI = 72.6–87.6%]. These results suggest that a blood test which is based on models comprising several autoantibodies to specific biomarkers may be a new and novel tool for improving the diagnostic evaluation of breast cancer. PMID:24324350

  16. Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel

    2004-01-01

    Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.

  17. [Atypical manifestation of severe mitral valve insufficiency. On the diagnosis and differential diagnosis based on a case report].

    PubMed

    Schwohl, T; Herhahn, J; Schroeder, B

    1990-04-01

    Report on a severe mitral valve insufficiency in a patient in whom all chordae tendinae of the posterior cusp of the mitral valve had completely ruptured for inexplicable reasons. An unusual feature of this condition was the prolonged clinical course for a period of two weeks and the markedly unilateral lung infiltrations seen on the plain x-ray of the thorax. Evidently non-specific inflammation parameters, such as elevated temperature, accelerated sedimentation rate, leukocytosis with shift to the left, prompted differential diagnosis of atypical pneumonia, e.g. legionellosis due to the identification of legionella antigen in the urine. In view of the fact that the patient had the initial signs and symptoms (dyspnoea, partly sanguineous sputum) after working in the garden (possible inhalation of a noxious substance?) we suspected an exogenous allergic alveolitis. This, however, could be excluded by a bronchoalveolar lavage (there were no lymphocytes in the wash). Last but not least, differential diagnosis of Goodpasture's syndrome was considered, where the pulmonary manifestation (haemorrhagic pneumonia) may precede the renal sign (glomerulonephritis). Diagnosis was finally established in the typical manner via echocardiography. Quantification of the mitral insufficiency was achieved by right cardiac catheterisation (v-wave 60 mmHg) and cardioangiography. Immediate mitral valve replacement surgery was effected without problems. However, the patient died on the 10th postoperative day from bacterial pneumonia. PMID:2360711

  18. A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

    SciTech Connect

    Xu, Songhua; Tourassi, Georgia

    2012-01-01

    The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using the BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.

  19. Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement

    NASA Astrophysics Data System (ADS)

    He, Qingbo; Wang, Jun; Hu, Fei; Kong, Fanrang

    2013-10-01

    The diagnosis of train bearing defects plays a significant role to maintain the safety of railway transport. Among various defect detection techniques, acoustic diagnosis is capable of detecting incipient defects of a train bearing as well as being suitable for wayside monitoring. However, the wayside acoustic signal will be corrupted by the Doppler effect and surrounding heavy noise. This paper proposes a solution to overcome these two difficulties in wayside acoustic diagnosis. In the solution, a dynamically resampling method is firstly presented to reduce the Doppler effect, and then an adaptive stochastic resonance (ASR) method is proposed to enhance the defective characteristic frequency automatically by the aid of noise. The resampling method is based on a frequency variation curve extracted from the time-frequency distribution (TFD) of an acoustic signal by dynamically minimizing the local cost functions. For the ASR method, the genetic algorithm is introduced to adaptively select the optimal parameter of the multiscale noise tuning (MST)-based stochastic resonance (SR) method. The proposed wayside acoustic diagnostic scheme combines signal resampling and information enhancement, and thus is expected to be effective in wayside defective bearing detection. The experimental study verifies the effectiveness of the proposed solution.

  20. Image-based computer-assisted diagnosis system for benign paroxysmal positional vertigo

    NASA Astrophysics Data System (ADS)

    Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu

    2005-04-01

    We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.

  1. Recombinant antigen-based enzyme-linked immunosorbent assay for diagnosis of Baylisascaris procyonis larva migrans.

    PubMed

    Dangoudoubiyam, Sriveny; Vemulapalli, Ramesh; Ndao, Momar; Kazacos, Kevin R

    2011-10-01

    Baylisascaris larva migrans is an important zoonotic disease caused by Baylisascaris procyonis, the raccoon roundworm, and is being increasingly considered in the differential diagnosis of eosinophilic meningoencephalitis in children and young adults. Although a B. procyonis excretory-secretory (BPES) antigen-based enzyme-linked immunosorbent assay (ELISA) and a Western blot assay are useful in the immunodiagnosis of this infection, cross-reactivity remains a major problem. Recently, a recombinant B. procyonis antigen, BpRAG1, was reported for use in the development of improved serological assays for the diagnosis of Baylisascaris larva migrans. In this study, we tested a total of 384 human patient serum samples in a BpRAG1 ELISA, including samples from 20 patients with clinical Baylisascaris larva migrans, 137 patients with other parasitic infections (8 helminth and 4 protozoan), and 227 individuals with unknown/suspected parasitic infections. A sensitivity of 85% and a specificity of 86.9% were observed with the BpRAG1 ELISA, compared to only 39.4% specificity with the BPES ELISA. In addition, the BpRAG1 ELISA had a low degree of cross-reactivity with antibodies to Toxocara infection (25%), while the BPES antigen showed 90.6% cross-reactivity. Based on these results, the BpRAG1 antigen has a high degree of sensitivity and specificity and should be very useful and reliable in the diagnosis and seroepidemiology of Baylisascaris larva migrans by ELISA. PMID:21832102

  2. Development of the Task-Based Expert System for Machine Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Bo, Ma; Zhi-nong, Jiang; Zhong-qing, Wei

    2012-05-01

    The operating mechanism of expert systems widely used in fault diagnosis is to formulate a set of diagnostic rules, according to the mechanism and symptoms of faults, in order to instruct the fault diagnosis or directly give diagnostic results. In practice, due to differences existing in such aspects as production technology, drivers, etc., a certain fault may derive from different causes, which will lead to a lower diagnostic accuracy of expert systems. Besides, a variety of expert systems now available have a dual problem of low generality and low expandability, of which the former can lead to the repeated development of expert systems for different machines, while the latter restricts users from expanding the system. Aimed at these problems, a type of task-based software architecture of expert system is proposed in this paper, which permits a specific optimization based on a set of common rules, and allows users to add or modify rules on a man-machine dialog so as to keep on absorbing and improving the expert knowledge. Finally, the integration of the expert system with the condition monitoring system to implement the automatic and semi-automatic diagnosis is introduced.

  3. A general model for the study of fault tolerance and diagnosis.

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.

    1973-01-01

    The concept of a 'system with faults' is introduced as a suggested point of departure for the theoretical study of fault tolerance and diagnosis in systems. The model is defined relative to a general representation scheme for systems and, depending on the choice of representation, can be used to investigate either hardware or software faults that occur during either the design or use of a system.

  4. Model-Based Reasoning

    ERIC Educational Resources Information Center

    Ifenthaler, Dirk; Seel, Norbert M.

    2013-01-01

    In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper…

  5. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    NASA Astrophysics Data System (ADS)

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

  6. Model-based software design

    NASA Technical Reports Server (NTRS)

    Iscoe, Neil; Liu, Zheng-Yang; Feng, Guohui; Yenne, Britt; Vansickle, Larry; Ballantyne, Michael

    1992-01-01

    Domain-specific knowledge is required to create specifications, generate code, and understand existing systems. Our approach to automating software design is based on instantiating an application domain model with industry-specific knowledge and then using that model to achieve the operational goals of specification elicitation and verification, reverse engineering, and code generation. Although many different specification models can be created from any particular domain model, each specification model is consistent and correct with respect to the domain model.

  7. A framework for final drive simultaneous failure diagnosis based on fuzzy entropy and sparse bayesian extreme learning machine.

    PubMed

    Ye, Qing; Pan, Hao; Liu, Changhua

    2015-01-01

    This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

  8. A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

    PubMed Central

    Ye, Qing; Pan, Hao; Liu, Changhua

    2015-01-01

    This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

  9. Diagnosis of cardiovascular abnormalities from compressed ECG: a data mining-based approach.

    PubMed

    Sufi, Fahim; Khalil, Ibrahim

    2011-01-01

    Usage of compressed ECG for fast and efficient telecardiology application is crucial, as ECG signals are enormously large in size. However, conventional ECG diagnosis algorithms require the compressed ECG packets to be decompressed before diagnosis can be performed. This added step of decompression before performing diagnosis for every ECG packet introduces unnecessary delay, which is undesirable for cardiovascular diseased (CVD) patients. In this paper, we are demonstrating an innovative technique that performs real-time classification of CVD. With the help of this real-time classification of CVD, the emergency personnel or the hospital can automatically be notified via SMS/MMS/e-mail when a life-threatening cardiac abnormality of the CVD affected patient is detected. Our proposed system initially uses data mining techniques, such as attribute selection (i.e., selects only a few features from the compressed ECG) and expectation maximization (EM)-based clustering. These data mining techniques running on a hospital server generate a set of constraints for representing each of the abnormalities. Then, the patient's mobile phone receives these set of constraints and employs a rule-based system that can identify each of abnormal beats in real time. Our experimentation results on 50 MIT-BIH ECG entries reveal that the proposed approach can successfully detect cardiac abnormalities (e.g., ventricular flutter/fibrillation, premature ventricular contraction, atrial fibrillation, etc.) with 97% accuracy on average. This innovative data mining technique on compressed ECG packets enables faster identification of cardiac abnormality directly from the compressed ECG, helping to build an efficient telecardiology diagnosis system. PMID:21097383

  10. Principles of models based engineering

    SciTech Connect

    Dolin, R.M.; Hefele, J.

    1996-11-01

    This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.

  11. APPLYING TENSOR-BASED MORPHOMETRY TO PARAMETRIC SURFACES CAN IMPROVE MRI-BASED DISEASE DIAGNOSIS

    PubMed Central

    Wang, Yalin; Yuan, Lei; Shi, Jie; Greve, Alexander; Ye, Jieping; Toga, Arthur W.; Reiss, Allan L.; Thompson, Paul M.

    2013-01-01

    Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an ℓ1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification. PMID:23435208

  12. Model-Based Fault Diagnosis for Turboshaft Engines

    NASA Technical Reports Server (NTRS)

    Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

    1998-01-01

    Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

  13. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images.

    PubMed

    Kowal, Marek; Filipczuk, Paweł; Obuchowicz, Andrzej; Korbicz, Józef; Monczak, Roman

    2013-10-01

    Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Góra, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96-100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis. PMID:24034748

  14. Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Liu, Zongyao; Lin, Jing; Lu, Fanbo

    2016-05-01

    Condition monitoring and fault diagnosis of epicyclic gearboxes using vibration signals are not as straightforward as that of fixed-axis gearboxes since epicyclic gearboxes behave quite differently from fixed-axis gearboxes in many aspects, like spectral structures. Aiming to present the spectral structures of vibration signals of epicyclic gearboxes, phenomenological models of vibration signals of epicyclic gearboxes are developed by algebraic equations and spectral structures of these models are deduced using Fourier series analysis. In the phenomenological models, all the possible vibration transfer paths from gear meshing points to a fixed transducer and the effects of angular shifts of planet gears on the spectral structures are considered. Accordingly, time-varying vibration transfer paths from sun-planet/ring-planet gear meshing points to the fixed transducer due to carrier rotation are given by window functions with different amplitudes. And an angular shift in one planet gear position is introduced in the process of modeling. After the theoretical derivations, three experiments are conducted on an epicyclic gearbox test rig and the spectral structures of collected vibration signals are analyzed. As a result, the effects of angular shifts of planet gears are verified, and the phenomenological models of vibration signals when a local fault occurs on the sun gear and the planet gear are validated, respectively. The experiment results demonstrate that the established phenomenological models in this paper are helpful to the condition monitoring and fault diagnosis of epicyclic gearboxes.

  15. Participatory diagnosis in urban planning: proposal for a learning process based on geographical information.

    PubMed

    Joerin, Florent; Desthieux, Gilles; Beuze, Sandrine Billeau; Nembrini, Aurore

    2009-05-01

    Urban planning involves compromise between the diverse and often contradictory issues supported by the different stakeholders. The literature generally agrees on the need to broaden the participation base to overcome this difficulty. However, participation should not be limited to problem solving, but should also take place in the problem setting phase. This paper proposes a participatory diagnosis process for structuring the problem setting phase. We describe an experiment in a participatory diagnosis conducted with the residents of a Geneva neighborhood. The experiment began by identifying the residents' concerns, which were then reformulated under broader issues. Some 20 spatial indicators were built using GIS tools, and were then applied in a second phase of resident consultations to assess the relative importance of each issue. The ensuing priority issues formed the core of the diagnosis. The approach emphasized comparison between the daily experiences of residents and so-called official information (i.e. census tract, traffic measurement, and so on). The residents were therefore involved in a learning process that allowed them to consolidate or modify their opinions. The process led to the emergence of a clearly defined collective awareness that supplanted individual aspirations. PMID:18562082

  16. Diffuse lung disease of infancy: a pattern-based, algorithmic approach to histological diagnosis.

    PubMed

    Armes, Jane E; Mifsud, William; Ashworth, Michael

    2015-02-01

    Diffuse lung disease (DLD) of infancy has multiple aetiologies and the spectrum of disease is substantially different from that seen in older children and adults. In many cases, a specific diagnosis renders a dire prognosis for the infant, with profound management implications. Two recently published series of DLD of infancy, collated from the archives of specialist centres, indicate that the majority of their cases were referred, implying that the majority of biopsies taken for DLD of infancy are first received by less experienced pathologists. The current literature describing DLD of infancy takes a predominantly aetiological approach to classification. We present an algorithmic, histological, pattern-based approach to diagnosis of DLD of infancy, which, with the aid of appropriate multidisciplinary input, including clinical and radiological expertise and ancillary diagnostic studies, may lead to an accurate and useful interim report, with timely exclusion of inappropriate diagnoses. Subsequent referral to a specialist centre for confirmatory diagnosis will be dependent on the individual case and the decision of the multidisciplinary team. PMID:25477529

  17. Diffuse lung disease of infancy: a pattern-based, algorithmic approach to histological diagnosis

    PubMed Central

    Armes, Jane E; Mifsud, William; Ashworth, Michael

    2015-01-01

    Diffuse lung disease (DLD) of infancy has multiple aetiologies and the spectrum of disease is substantially different from that seen in older children and adults. In many cases, a specific diagnosis renders a dire prognosis for the infant, with profound management implications. Two recently published series of DLD of infancy, collated from the archives of specialist centres, indicate that the majority of their cases were referred, implying that the majority of biopsies taken for DLD of infancy are first received by less experienced pathologists. The current literature describing DLD of infancy takes a predominantly aetiological approach to classification. We present an algorithmic, histological, pattern-based approach to diagnosis of DLD of infancy, which, with the aid of appropriate multidisciplinary input, including clinical and radiological expertise and ancillary diagnostic studies, may lead to an accurate and useful interim report, with timely exclusion of inappropriate diagnoses. Subsequent referral to a specialist centre for confirmatory diagnosis will be dependent on the individual case and the decision of the multidisciplinary team. PMID:25477529

  18. SFT based cosmological models

    NASA Astrophysics Data System (ADS)

    Koshelev, Alexey S.

    2010-11-01

    We consider the appearance of multiple scalar fields in SFT inspired non-local models with a single scalar field at late times. In this regime all the scalar fields are free. This system minimally coupled to gravity is mainly analyzed in this note. We build one exact solution to the equations of motion. We consider an exactly solvable model which obeys a simple exact solution in the cosmological context for the Friedmann equations and that reproduces the behavior expected from SFT in the asymptotic regime.

  19. Practical Application of Model-based Programming and State-based Architecture to Space Missions

    NASA Technical Reports Server (NTRS)

    Horvath, Gregory; Ingham, Michel; Chung, Seung; Martin, Oliver; Williams, Brian

    2006-01-01

    A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps

  20. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    NASA Astrophysics Data System (ADS)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-12-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  1. Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the “i-ROP” System and Image Features Associated With Expert Diagnosis

    PubMed Central

    Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J. Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R. V. Paul; Ostmo, Susan; Chiang, Michael F.

    2015-01-01

    Purpose We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. Methods A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the “i-ROP” system. Results Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). Conclusions This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Translational Relevance Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists. PMID:26644965

  2. Olfactory neuroepithelium as a cellular model for the diagnosis of neuropsychiatric diseases.

    PubMed

    Soto-Vázquez, Ramón; Labastida-López, Carlos; Romero-Castello, Samuel; Benítez-King, Gloria; Parra-Cervantes, Patricia

    2014-01-01

    The neuroepithelium has been used as an experimental model to find biological markers for neuropsychiatric disease diagnosis. Patent information permits understanding of the state of the art of neuroepithelium in neuropsychiatric disease diagnosis, as well as the identification of trends in research and development on this theme. In this article, we discuss diverse methods for obtaining primary cultures of olfactory neurons obtained by animal dissection or by postmortem biopsy of human cadavers. The principal owners of patents related to olfactory neuroepithelia are universities such as John Hopkins and Bristol-Myers Squibb. The USA has the most research lines and approved patents in the world, while Rutgers, the State University of New Jersey, provides composition and methods related to the diagnoses and treatment of neuropsychiatric disorders. PMID:24354978

  3. Microwave-based stroke diagnosis making global prehospital thrombolytic treatment possible.

    PubMed

    Persson, Mikael; Fhager, Andreas; Trefná, Hana Dobsicek; Yu, Yinan; McKelvey, Tomas; Pegenius, Göran; Karlsson, Jan-Erik; Elam, Mikael

    2014-11-01

    Here, we present two different brain diagnostic devices based on microwave technology and the associated two first proof-of-principle measurements that show that the systems can differentiate hemorrhagic from ischemic stroke in acute stroke patients, as well as differentiate hemorrhagic patients from healthy volunteers. The system was based on microwave scattering measurements with an antenna system worn on the head. Measurement data were analyzed with a machine-learning algorithm that is based on training using data from patients with a known condition. Computer tomography images were used as reference. The detection methodology was evaluated with the leave-one-out validation method combined with a Monte Carlo-based bootstrap step. The clinical motivation for this project is that ischemic stroke patients may receive acute thrombolytic treatment at hospitals, dramatically reducing or abolishing symptoms. A microwave system is suitable for prehospital use, and therefore has the potential to allow significantly earlier diagnosis and treatment than today. PMID:24951677

  4. Improving liver fibrosis diagnosis based on forward and backward second harmonic generation signals

    NASA Astrophysics Data System (ADS)

    Peng, Qiwen; Zhuo, Shuangmu; So, Peter T. C.; Yu, Hanry

    2015-02-01

    The correlation of forward second harmonic generation (SHG) signal and backward SHG signal in different liver fibrosis stages was investigated. We found that three features, including the collagen percentage for forward SHG, the collagen percentage for backward SHG, and the average intensity ratio of two kinds of SHG signals, can quantitatively stage liver fibrosis in thioacetamide-induced rat model. We demonstrated that the combination of all three features by using a support vector machine classification algorithm can provide a more accurate prediction than each feature alone in fibrosis diagnosis.

  5. Haplotype-based approach for noninvasive prenatal diagnosis of congenital adrenal hyperplasia by maternal plasma DNA sequencing.

    PubMed

    Ma, Dingyuan; Ge, Huijuan; Li, Xuchao; Jiang, Tao; Chen, Fang; Zhang, Yanyan; Hu, Ping; Chen, Shengpei; Zhang, Jingjing; Ji, Xiuqing; Xu, Xun; Jiang, Hui; Chen, Minfeng; Wang, Wei; Xu, Zhengfeng

    2014-07-10

    Prenatal diagnosis of congenital adrenal hyperplasia (CAH) is of clinical significance because in utero treatment is available to prevent virilization of an affected female fetus. However, traditional prenatal diagnosis of CAH relies on genetic testing of fetal genomic DNA obtained using amniocentesis or chorionic villus sampling, which is associated with an increased risk of miscarriage. The aim of this study was to demonstrate the feasibility of a new haplotype-based approach for the noninvasive prenatal testing of CAH due to 21-hydroxylase deficiency. Parental haplotypes were constructed using target-region sequencing data of the parents and the proband. With the assistance of the parental haplotypes, we recovered fetal haplotypes using a hidden Markov model (HMM) through maternal plasma DNA sequencing. In the genomic region around the CYP21A2 gene, the fetus inherited the paternal haplotype '0' alleles linked to the mutant CYP21A2 gene, but the maternal haplotype '1' alleles linked to the wild-type gene. The fetus was predicted to be an unaffected carrier of CAH, which was confirmed by genetic analysis of fetal genomic DNA from amniotic fluid cells. This method was further validated by comparing the inferred SNP genotypes with the direct sequencing data of fetal genomic DNA. The result showed an accuracy of 96.41% for the inferred maternal alleles and an accuracy of 97.81% for the inferred paternal alleles. The haplotype-based approach is feasible for noninvasive prenatal testing of CAH. PMID:24768736

  6. Rule-based simulation models

    NASA Technical Reports Server (NTRS)

    Nieten, Joseph L.; Seraphine, Kathleen M.

    1991-01-01

    Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

  7. Diagnosis System Based on Wavelet Transform, Fractal Dimension and Neural Network

    NASA Astrophysics Data System (ADS)

    El-Ramsisi, Abdallah M.; Khalil, Hassan A.

    In this study we introduce a diagnosis system based on wavelet and fractal dimension for diagnose the Heart Mitral Valve Diseases. This study deals with the feature extraction from the Doppler signal waveform at heart mitral valve using ultrasound. Wavelet packet transforms, Fourier transform and Fractal Dimension methods are used for feature extraction from the DHS signals. The back-propagation neural network is used to classify the extracted features. The system has been evaluated in 162 samples that contain 89 normal and 73 abnormal. The results showed that the classification was about 91% for normal and abnormal cases.

  8. Diagnosis of disc herniation based on classifiers and features generated from spine MR images

    NASA Astrophysics Data System (ADS)

    Koh, Jaehan; Chaudhary, Vipin; Dhillon, Gurmeet

    2010-03-01

    In recent years the demand for an automated method for diagnosis of disc abnormalities has grown as more patients suffer from lumbar disorders and radiologists have to treat more patients reliably in a limited amount of time. In this paper, we propose and compare several classifiers that diagnose disc herniation, one of the common problems of the lumbar spine, based on lumbar MR images. Experimental results on a limited data set of 68 clinical cases with 340 lumbar discs show that our classifiers can diagnose disc herniation with 97% accuracy.

  9. A logic based expert system (LBES) for fault diagnosis of power system

    SciTech Connect

    Park, Y.M.; Kim, G.W.; Sohn, J.M.

    1997-02-01

    This paper proposes an expert system for fault diagnosis of power system using a new inference method. Expertise is, in this paper, represented by logical implications and converted into a Boolean function. Unlike conventional rule-based expert systems, the expertise is converted into Prime Implicants (PIs) which are logically complete and sound. Therefore, off-line inference is possible by off-line identification of PIs, which reduces the on-line inference time considerably and makes it possible to utilize the proposed expert system in real-time environment. This paper also presents alarm verification and correction method for relay and circuit breaker (CB) using pre-identified PIs.

  10. A dynamic integrated fault diagnosis method for power transformers.

    PubMed

    Gao, Wensheng; Bai, Cuifen; Liu, Tong

    2015-01-01

    In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

  11. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

    PubMed Central

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

  12. A feature extraction method based on information theory for fault diagnosis of reciprocating machinery.

    PubMed

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

  13. Classification and clinical diagnosis of fibromyalgia syndrome: recommendations of recent evidence-based interdisciplinary guidelines.

    PubMed

    Fitzcharles, Mary-Ann; Shir, Yoram; Ablin, Jacob N; Buskila, Dan; Amital, Howard; Henningsen, Peter; Häuser, Winfried

    2013-01-01

    Objectives. Fibromyalgia syndrome (FMS), characterized by subjective complaints without physical or biomarker abnormality, courts controversy. Recommendations in recent guidelines addressing classification and diagnosis were examined for consistencies or differences. Methods. Systematic searches from January 2008 to February 2013 of the US-American National Guideline Clearing House, the Scottish Intercollegiate Guidelines Network, Guidelines International Network, and Medline for evidence-based guidelines for the management of FMS were conducted. Results. Three evidence-based interdisciplinary guidelines, independently developed in Canada, Germany, and Israel, recommended that FMS can be clinically diagnosed by a typical cluster of symptoms following a defined evaluation including history, physical examination, and selected laboratory tests, to exclude another somatic disease. Specialist referral is only recommended when some other physical or mental illness is reasonably suspected. The diagnosis can be based on the (modified) preliminary American College of Rheumatology (ACR) 2010 diagnostic criteria. Discussion. Guidelines from three continents showed remarkable consistency regarding the clinical concept of FMS, acknowledging that FMS is neither a distinct rheumatic nor mental disorder, but rather a cluster of symptoms, not explained by another somatic disease. While FMS remains an integral part of rheumatology, it is not an exclusive rheumatic condition and spans a broad range of medical disciplines. PMID:24379886

  14. Classification and Clinical Diagnosis of Fibromyalgia Syndrome: Recommendations of Recent Evidence-Based Interdisciplinary Guidelines

    PubMed Central

    Fitzcharles, Mary-Ann; Shir, Yoram; Ablin, Jacob N.; Buskila, Dan; Henningsen, Peter

    2013-01-01

    Objectives. Fibromyalgia syndrome (FMS), characterized by subjective complaints without physical or biomarker abnormality, courts controversy. Recommendations in recent guidelines addressing classification and diagnosis were examined for consistencies or differences. Methods. Systematic searches from January 2008 to February 2013 of the US-American National Guideline Clearing House, the Scottish Intercollegiate Guidelines Network, Guidelines International Network, and Medline for evidence-based guidelines for the management of FMS were conducted. Results. Three evidence-based interdisciplinary guidelines, independently developed in Canada, Germany, and Israel, recommended that FMS can be clinically diagnosed by a typical cluster of symptoms following a defined evaluation including history, physical examination, and selected laboratory tests, to exclude another somatic disease. Specialist referral is only recommended when some other physical or mental illness is reasonably suspected. The diagnosis can be based on the (modified) preliminary American College of Rheumatology (ACR) 2010 diagnostic criteria. Discussion. Guidelines from three continents showed remarkable consistency regarding the clinical concept of FMS, acknowledging that FMS is neither a distinct rheumatic nor mental disorder, but rather a cluster of symptoms, not explained by another somatic disease. While FMS remains an integral part of rheumatology, it is not an exclusive rheumatic condition and spans a broad range of medical disciplines. PMID:24379886

  15. Identifying Model-Based Reconfiguration Goals through Functional Deficiencies

    NASA Technical Reports Server (NTRS)

    Benazera, Emmanuel; Trave-Massuyes, Louise

    2004-01-01

    Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.

  16. Next generation sequencing for molecular diagnosis of neurological disorders using ataxias as a model

    PubMed Central

    Kwasniewska, Alexandra C.; Lise, Stefano; Parolin Schnekenberg, Ricardo; Becker, Esther B. E.; Bera, Katarzyna D.; Shanks, Morag E.; Gregory, Lorna; Buck, David; Zameel Cader, M.; Talbot, Kevin; de Silva, Rajith; Fletcher, Nicholas; Hastings, Rob; Jayawant, Sandeep; Morrison, Patrick J.; Worth, Paul; Taylor, Malcolm; Tolmie, John; O’Regan, Mary; Valentine, Ruth; Packham, Emily; Evans, Julie; Seller, Anneke; Ragoussis, Jiannis

    2013-01-01

    Many neurological conditions are caused by immensely heterogeneous gene mutations. The diagnostic process is often long and complex with most patients undergoing multiple invasive and costly investigations without ever reaching a conclusive molecular diagnosis. The advent of massively parallel, next-generation sequencing promises to revolutionize genetic testing and shorten the ‘diagnostic odyssey’ for many of these patients. We performed a pilot study using heterogeneous ataxias as a model neurogenetic disorder to assess the introduction of next-generation sequencing into clinical practice. We captured 58 known human ataxia genes followed by Illumina Next-Generation Sequencing in 50 highly heterogeneous patients with ataxia who had been extensively investigated and were refractory to diagnosis. All cases had been tested for spinocerebellar ataxia 1–3, 6, 7 and Friedrich’s ataxia and had multiple other biochemical, genetic and invasive tests. In those cases where we identified the genetic mutation, we determined the time to diagnosis. Pathogenicity was assessed using a bioinformatics pipeline and novel variants were validated using functional experiments. The overall detection rate in our heterogeneous cohort was 18% and varied from 8.3% in those with an adult onset progressive disorder to 40% in those with a childhood or adolescent onset progressive disorder. The highest detection rate was in those with an adolescent onset and a family history (75%). The majority of cases with detectable mutations had a childhood onset but most are now adults, reflecting the long delay in diagnosis. The delays were primarily related to lack of easily available clinical testing, but other factors included the presence of atypical phenotypes and the use of indirect testing. In the cases where we made an eventual diagnosis, the delay was 3–35 years (mean 18.1 years). Alignment and coverage metrics indicated that the capture and sequencing was highly efficient and the

  17. A theoretical model for fault diagnosis of localized bearing defects under non-weight-dominant conditions

    NASA Astrophysics Data System (ADS)

    Han, Q. K.; Chu, F. L.

    2015-07-01

    Fault diagnosis of localized bearing defects under non-weight-dominant conditions is studied in this paper. A theoretical model with eight degrees of freedom is established, considering two transverse vibrations of the rotor and bearing raceway and one high-frequency resonant degree of freedom. Both the Hertzian contact between rolling elements and raceways, bearing clearance, unbalance force and self-weight of rotor are taken into account in the model. The localized defects in both inner and outer raceways are modeled as half sinusoidal waves. Then, the theoretical model is solved numerically and the vibrational responses are obtained. Through envelope analysis, the fault characteristic frequencies of inner/outer raceway defects for various conditions, including the weight-dominant condition and non-weight-dominant condition, are presented and compared with each other.

  18. Development and Evaluation of Reference Standards for Image-based Telemedicine Diagnosis and Clinical Research Studies in Ophthalmology

    PubMed Central

    Ryan, Michael C.; Ostmo, Susan; Jonas, Karyn; Berrocal, Audina; Drenser, Kimberly; Horowitz, Jason; Lee, Thomas C.; Simmons, Charles; Martinez-Castellanos, Maria-Ana; Chan, R.V. Paul; Chiang, Michael F.

    2014-01-01

    Information systems managing image-based data for telemedicine or clinical research applications require a reference standard representing the correct diagnosis. Accurate reference standards are difficult to establish because of imperfect agreement among physicians, and discrepancies between clinical vs. image-based diagnosis. This study is designed to describe the development and evaluation of reference standards for image-based diagnosis, which combine diagnostic impressions of multiple image readers with the actual clinical diagnoses. We show that agreement between image reading and clinical examinations was imperfect (689 [32%] discrepancies in 2148 image readings), as was inter-reader agreement (kappa 0.490-0.652). This was improved by establishing an image-based reference standard defined as the majority diagnosis given by three readers (13% discrepancies with image readers). It was further improved by establishing an overall reference standard that incorporated the clinical diagnosis (10% discrepancies with image readers). These principles of establishing reference standards may be applied to improve robustness of real-world systems supporting image-based diagnosis. PMID:25954463

  19. Computer-aided diagnosis of proliferative diabetic retinopathy via modeling of the major temporal arcade in retinal fundus images.

    PubMed

    Oloumi, Faraz; Rangayyan, Rangaraj M; Ells, Anna L

    2013-12-01

    Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate diagnosis and treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided semiautomated modeling and measurement of the openness of the MTA based on Gabor filters for the detection of retinal vessels, morphological image processing, and a form of the generalized Hough transform for the detection of parabolas. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 11 normal individuals and 11 patients with PDR in the present pilot study on potential clinical application. A method of arcade angle measurement was used for comparative analysis. The results using the openness parameters of single- and dual-parabolic models as well as the arcade angle measurements indicate areas under the receiver operating characteristics of A z = 0.87, 0.82, and 0.80, respectively. The proposed methods are expected to facilitate quantitative analysis of the architecture of the MTA, as well as assist in detection and diagnosis of PDR. PMID:23579735

  20. Model based vibration monitoring

    SciTech Connect

    Esat, I.; Paya, B.; Badi, M.N.M.

    1996-11-01

    The principal source of vibratory excitation of gear system is the unsteady component of the relative angular motion of pair of meshing spur gears. This vibratory excitation is described by the transmission error. The transmission error present itself as a varying force at the contact point of the meshing gear teeth. The varying force is also influenced by the varying tooth stiffness due to change of orientation of teeth relative to each other, during the contact phase of each pair. Such a varying force produces both lateral and torsional excitation to the gear system. This paper presents analytical formulation of a simple two meshing spur gear system as a three mass system (18 DOF). The mathematical model also incorporates the analytical formulation of the tooth stiffness. The analytical results are compared with the experimental results. At this stage of analysis the procedure developed for handling the nonlinear influences of the tooth geometry is not fully implemented and the tooth stiffness taken as a constant value representing the average tooth stiffness. The comparison between the analytical and experimental results are encouraging as three main frequency obtained from FFT of the experimental results correlates very closely with the analytical results.

  1. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Zuo, Ming J.

    2009-12-01

    A Hilbert-Huang transform (HHT) is a time-frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD.

  2. Optical and dielectric sensors based on antimicrobial peptides for microorganism diagnosis

    PubMed Central

    Silva, Rafael R.; Avelino, Karen Y. P. S.; Ribeiro, Kalline L.; Franco, Octavio L.; Oliveira, Maria D. L.; Andrade, Cesar A. S.

    2014-01-01

    Antimicrobial peptides (AMPs) are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants, and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi, and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories. PMID:25191319

  3. Evidence-based consensus on the diagnosis, prevention and management of hepatitis C virus disease

    PubMed Central

    Shaheen, Mahrukh Akbar; Idrees, Muhammad

    2015-01-01

    Hepatitis C virus (HCV) is a potent human pathogen and is one of the main causes of chronic hepatitis round the world. The present review describes the evidence-based consensus on the diagnosis, prevention and management of HCV disease. Various techniques, for the detection of anti-HCV immunoglobulin G immunoassays, detection of HCV RNA by identifying virus-specific molecules nucleic acid testings, recognition of core antigen for diagnosis of HCV, quantitative antigen assay, have been used to detect HCV RNA and core antigen. Advanced technologies such as nanoparticle-based diagnostic assays, loop-mediated isothermal amplification and aptamers and Ortho trak-C assay have also come to the front that provides best detection results with greater ease and specificity for detection of HCV. It is of immense importance to prevent this infection especially among the sexual partners, injecting drug users, mother-to-infant transmission of HCV, household contact, healthcare workers and people who get tattoos and piercing on their skin. Management of this infection is intended to eradicate it out of the body of patients. Management includes examining the treatment (efficacy and protection), assessment of hepatic condition before commencing therapy, controlling the parameters upon which dual and triple therapies work, monitoring the body after treatment and adjusting the co-factors. Examining the treatment in some special groups of people (HIV/HCV co-infected, hemodialysis patients, renal transplanted patients). PMID:25848486

  4. SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient

    NASA Astrophysics Data System (ADS)

    Qiao, Zijian; Pan, Zhengrong

    2015-08-01

    Aiming at solving the existing sharp problems by using singular value decomposition (SVD) in the fault diagnosis of rolling bearings, such as the determination of the delay step k for creating the Hankel matrix and selection of effective singular values, the present study proposes a novel adaptive SVD method for fault feature detection based on the correlation coefficient by analyzing the principles of the SVD method. This proposed method achieves not only the optimal determination of the delay step k by means of the absolute value {{r}k} of the autocorrelation function sequence of the collected vibration signal, but also the adaptive selection of effective singular values using the index ρ corresponding to useful component signals including weak fault information to detect weak fault signals for rolling bearings, especially weak impulse signals. The effectiveness of this method has been verified by contrastive results between the proposed method and traditional SVD, even using the wavelet-based method through simulated experiments. Finally, the proposed method has been applied to fault diagnosis for a deep-groove ball bearing in which a single point fault located on either the inner or outer race of rolling bearings is obtained successfully. Therefore, it can be stated that the proposed method is of great practical value in engineering applications.

  5. Bond graph modeling and experimental verification of a novel scheme for fault diagnosis of rolling element bearings in special operating conditions

    NASA Astrophysics Data System (ADS)

    Mishra, C.; Samantaray, A. K.; Chakraborty, G.

    2016-09-01

    Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.

  6. Portable Optical Fiber Probe-Based Spectroscopic Scanner for Rapid Cancer Diagnosis: A New Tool for Intraoperative Margin Assessment

    PubMed Central

    Lue, Niyom; Kang, Jeon Woong; Yu, Chung-Chieh; Barman, Ishan; Dingari, Narahara Chari; Dasari, Ramachandra R.; Fitzmaurice, Maryann

    2012-01-01

    There continues to be a significant clinical need for rapid and reliable intraoperative margin assessment during cancer surgery. Here we describe a portable, quantitative, optical fiber probe-based, spectroscopic tissue scanner designed for intraoperative diagnostic imaging of surgical margins, which we tested in a proof of concept study in human tissue for breast cancer diagnosis. The tissue scanner combines both diffuse reflectance spectroscopy (DRS) and intrinsic fluorescence spectroscopy (IFS), and has hyperspectral imaging capability, acquiring full DRS and IFS spectra for each scanned image pixel. Modeling of the DRS and IFS spectra yields quantitative parameters that reflect the metabolic, biochemical and morphological state of tissue, which are translated into disease diagnosis. The tissue scanner has high spatial resolution (0.25 mm) over a wide field of view (10 cm×10 cm), and both high spectral resolution (2 nm) and high spectral contrast, readily distinguishing tissues with widely varying optical properties (bone, skeletal muscle, fat and connective tissue). Tissue-simulating phantom experiments confirm that the tissue scanner can quantitatively measure spectral parameters, such as hemoglobin concentration, in a physiologically relevant range with a high degree of accuracy (<5% error). Finally, studies using human breast tissues showed that the tissue scanner can detect small foci of breast cancer in a background of normal breast tissue. This tissue scanner is simpler in design, images a larger field of view at higher resolution and provides a more physically meaningful tissue diagnosis than other spectroscopic imaging systems currently reported in literatures. We believe this spectroscopic tissue scanner can provide real-time, comprehensive diagnostic imaging of surgical margins in excised tissues, overcoming the sampling limitation in current histopathology margin assessment. As such it is a significant step in the development of a platform

  7. Model-Based Method for Sensor Validation

    NASA Technical Reports Server (NTRS)

    Vatan, Farrokh

    2012-01-01

    Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

  8. Tetralogy of Fallot Cardiac Function Evaluation and Intelligent Diagnosis Based on Dual-Source Computed Tomography Cardiac Images.

    PubMed

    Cai, Ken; Rongqian, Yang; Li, Lihua; Xie, Zi; Ou, Shanxing; Chen, Yuke; Dou, Jianhong

    2016-05-01

    Tetralogy of Fallot (TOF) is the most common complex congenital heart disease (CHD) of the cyanotic type. Studies on ventricular functions have received an increasing amount of attention as the development of diagnosis and treatment technology for CHD continues to advance. Reasonable options for imaging examination and accurate assessment of preoperative and postoperative left ventricular functions of TOF patients are important in improving the cure rate of TOF radical operation, therapeutic evaluation, and judgment prognosis. Therefore, with the aid of dual-source computed tomography (DSCT), cardiac images with high temporal resolution and high definition, we measured the left ventricular time-volume curve using image data and calculating the left ventricular function parameters to conduct the preliminary evaluation on TOF patients. To comprehensively evaluate the cardiac function, the segmental ventricular wall function parameters were measured, and the measurement results were mapped to a bull's eye diagram to realize the standardization of segmental ventricular wall function evaluation. Finally, we introduced a new clustering method based on auto-regression model parameters and combined this method with Euclidean distance measurements to establish an intelligent diagnosis of TOF. The results of this experiment show that the TOF evaluation and the intelligent diagnostic methods proposed in this article are feasible. PMID:26496001

  9. Modeling clinical judgment and implicit guideline compliance in the diagnosis of melanomas using machine learning.

    PubMed

    Sboner, Andrea; Aliferis, Constantin F

    2005-01-01

    We explore several machine learning techniques to model clinical decision making of 6 dermatologists in the clinical task of melanoma diagnosis of 177 pigmented skin lesions (76 malignant, 101 benign). In particular we apply Support Vector Machine (SVM) classifiers to model clinician judgments, Markov Blanket and SVM feature selection to eliminate clinical features that are effectively ignored by the dermatologists, and a novel explanation technique whereby regression tree induction is run on the reduced SVM model's output to explain the physicians' implicit patterns of decision making. Our main findings include: (a) clinician judgments can be accurately predicted, (b) subtle decision making rules are revealed enabling the explanation of differences of opinion among physicians, and (c) physician judgment is non-compliant with the diagnostic guidelines that physicians self-report as guiding their decision making. PMID:16779123

  10. Model-Based Safety Analysis

    NASA Technical Reports Server (NTRS)

    Joshi, Anjali; Heimdahl, Mats P. E.; Miller, Steven P.; Whalen, Mike W.

    2006-01-01

    System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.

  11. SPECT- and PET-Based Approaches for Noninvasive Diagnosis of Acute Renal Allograft Rejection

    PubMed Central

    Pawelski, Helga; Schnöckel, Uta; Kentrup, Dominik; Grabner, Alexander; Schäfers, Michael; Reuter, Stefan

    2014-01-01

    Molecular imaging techniques such as single photon emission computed tomography (SPECT) or positron emission tomography are promising tools for noninvasive diagnosis of acute allograft rejection (AR). Given the importance of renal transplantation and the limitation of available donors, detailed analysis of factors that affect transplant survival is important. Episodes of acute allograft rejection are a negative prognostic factor for long-term graft survival. Invasive core needle biopsies are still the “goldstandard” in rejection diagnostics. Nevertheless, they are cumbersome to the patient and carry the risk of significant graft injury. Notably, they cannot be performed on patients taking anticoagulant drugs. Therefore, a noninvasive tool assessing the whole organ for specific and fast detection of acute allograft rejection is desirable. We herein review SPECT- and PET-based approaches for noninvasive molecular imaging-based diagnostics of acute transplant rejection. PMID:24804257

  12. Space-Based Diagnosis of Surface Ozone Sensitivity to Anthropogenic Emissions

    NASA Technical Reports Server (NTRS)

    Martin, Randall V.; Fiore, Arlene M.; VanDonkelaar, Aaron

    2004-01-01

    We present a novel capability in satellite remote sensing with implications for air pollution control strategy. We show that the ratio of formaldehyde columns to tropospheric nitrogen dioxide columns is an indicator of the relative sensitivity of surface ozone to emissions of nitrogen oxides (NO(x) = NO + NO2) and volatile organic compounds (VOCs). The diagnosis from these space-based observations is highly consistent with current understanding of surface ozone chemistry based on in situ observations. The satellite-derived ratios indicate that surface ozone is more sensitive to emissions of NO(x) than of VOCs throughout most continental regions of the Northern Hemisphere during summer. Exceptions include Los Angeles and industrial areas of Germany. A seasonal transition occurs in the fall when surface ozone becomes less sensitive to NOx and more sensitive to VOCs.

  13. Current Advances in Polymer-Based Nanotheranostics for Cancer Treatment and Diagnosis

    PubMed Central

    2015-01-01

    Nanotheranostics is a relatively new, fast-growing field that combines the advantages of treatment and diagnosis via a single nanoscale carrier. The ability to bundle both therapeutic and diagnostic capabilities into one package offers exciting prospects for the development of novel nanomedicine. Nanotheranostics can deliver treatment while simultaneously monitoring therapy response in real-time, thereby decreasing the potential of over- or under-dosing patients. Polymer-based nanomaterials, in particular, have been used extensively as carriers for both therapeutic and bioimaging agents and thus hold great promise for the construction of multifunctional theranostic formulations. Herein, we review recent advances in polymer-based systems for nanotheranostics, with a particular focus on their applications in cancer research. We summarize the use of polymer nanomaterials for drug delivery, gene delivery, and photodynamic therapy, combined with imaging agents for magnetic resonance imaging, radionuclide imaging, and fluorescence imaging. PMID:25014486

  14. Induction motor fault diagnosis based on the k-NN and optimal feature selection

    NASA Astrophysics Data System (ADS)

    Nguyen, Ngoc-Tu; Lee, Hong-Hee

    2010-09-01

    The k-nearest neighbour (k-NN) rule is applied to diagnose the conditions of induction motors. The features are extracted from the time vibration signals while the optimal features are selected by a genetic algorithm based on a distance criterion. A weight value is assigned to each feature to help select the best quality features. To improve the classification performance of the k-NN rule, each of the k neighbours are evaluated by a weight factor based on the distance to the test pattern. The proposed k-NN is compared to the conventional k-NN and support vector machine classification to verify the performance of an induction motor fault diagnosis.

  15. Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants

    NASA Astrophysics Data System (ADS)

    Narayan, Anand P.

    1998-07-01

    The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory in conjunction with advanced fluid mechanics to achieve the objective of developing an implementable comprehensive monitoring systems, suitable for use on space missions. First, a three-dimensional transport model is developed in order to model the dispersion of air contaminants. The flow field, which is an important input to the transport model, is obtained by solving the Navier Stokes equations for the cabin geometry and the appropriate boundary conditions, using a finite element method. Steady flow fields are computed for various conditions for both laminar and turbulent cases. Contamination dispersion studies are undertaken both for routine substances introduced through the inlet ducts and for emissions of toxics inside the cabin volume. The dispersion studies indicate that lumped models and even a two-dimensional model are sometimes inadequate to assure that the Spacecraft Maximum Allowable Concentrations (SMACs) are not exceeded locally. Since the research was targeted at real-time application aboard Spacecraft, a state estimation routine is implemented using Implicit Kalman Filtering. The routine makes use of the model predictions and measurements from the sensor system in order to arrive at an optimal estimate of the state of the system for each time step. Fault detection is accomplished through the use of analytical redundancy, where error residuals from the Kalman filter are monitored in order to detect any faults in the system, and to distinguish between sensor and process faults. Finally, a fault diagnosis system is developed, which is a combination of sensitivity analysis and an

  16. Irritable bowel syndrome: Pathogenesis, diagnosis, treatment, and evidence-based medicine

    PubMed Central

    Saha, Lekha

    2014-01-01

    Irritable bowel syndrome (IBS) is a chronic and debilitating functional gastrointestinal disorder that affects 9%-23% of the population across the world. The percentage of patients seeking health care related to IBS approaches 12% in primary care practices and is by far the largest subgroup seen in gastroenterology clinics. It has been well documented that these patients exhibit a poorer quality of life and utilize the health care system to a greater degree than patients without this diagnosis. The pathophysiology of IBS is not clear. Many theories have been put forward, but the exact cause of IBS is still uncertain. According to the updated ROME III criteria, IBS is a clinical diagnosis and presents as one of the three predominant subtypes: (1) IBS with constipation (IBS-C); (2) IBS with diarrhea (IBS-D); and (3) mixed IBS (IBS-M); former ROME definitions refer to IBS-M as alternating IBS (IBS-A). Across the IBS subtypes, the presentation of symptoms may vary among patients and change over time. Patients report the most distressing symptoms to be abdominal pain, straining, myalgias, urgency, bloating and feelings of serious illness. The complexity and diversity of IBS presentation makes treatment difficult. Although there are reviews and guidelines for treating IBS, they focus on the efficacy of medications for IBS symptoms using high-priority endpoints, leaving those of lower priority largely unreported. Therefore, the aim of this review is to provide a comprehensive evidence-based review of the diagnosis, pathogenesis and treatment to guide clinicians diagnosing and treating their patients. PMID:24944467

  17. Diagnosis of alpha-1 antitrypsin deficiency: a population-based study

    PubMed Central

    Barrecheguren, Miriam; Monteagudo, Mónica; Simonet, Pere; Llor, Carl; Rodriguez, Esther; Ferrer, Jaume; Esquinas, Cristina; Miravitlles, Marc

    2016-01-01

    Introduction Alpha-1 antitrypsin deficiency (AATD) remains an underdiagnosed condition despite initiatives developed to increase awareness. The objective was to describe the current situation of the diagnosis of AATD in primary care (PC) in Catalonia, Spain. Methods We performed a population-based study with data from the Information System for Development in Research in Primary Care, a population database that contains information of 5.8 million inhabitants (80% of the population of Catalonia). We collected the number of alpha-1 antitrypsin (AAT) determinations performed in the PC in two periods (2007–2008 and 2010–2011) and described the characteristics of the individuals tested. Results A total of 12,409 AAT determinations were performed (5,559 in 2007–2008 and 6,850 in 2010–2011), with 10.7% of them in children. As a possible indication for AAT determination, 28.9% adults and 29.4% children had a previous diagnosis of a disease related to AATD; transaminase levels were above normal in 17.7% of children and 47.1% of adults. In total, 663 (5.3%) individuals had intermediate AATD (50–100 mg/dL), 24 (0.2%) individuals had a severe deficiency (<50 mg/dL), with a prevalence of 0.19 cases of severe deficiency per 100 determinations. Nine (41%) of the adults with severe deficiency had a previous diagnosis of COPD/emphysema, and four (16.7%) were diagnosed with COPD within 6 months. Conclusion The number of AAT determinations in the PC is low in relation to the prevalence of COPD but increased slightly along the study period. The indication to perform the test is not always clear, and patients detected with deficiency are not always referred to a specialist. PMID:27274221

  18. An Economic Evaluation of Home Versus Laboratory-Based Diagnosis of Obstructive Sleep Apnea

    PubMed Central

    Kim, Richard D.; Kapur, Vishesh K.; Redline-Bruch, Julie; Rueschman, Michael; Auckley, Dennis H.; Benca, Ruth M.; Foldvary-Schafer, Nancy R.; Iber, Conrad; Zee, Phyllis C.; Rosen, Carol L.; Redline, Susan; Ramsey, Scott D.

    2015-01-01

    Study Objectives: We conducted an economic analysis of the HomePAP study, a multicenter randomized clinical trial that compared home-based versus laboratory-based testing for the diagnosis and management of obstructive sleep apnea (OSA). Design: A cost-minimization analysis from the payer and provider perspectives was performed, given that 3-mo clinical outcomes were equivalent. Setting: Seven academic sleep centers. Participants: There were 373 subjects at high risk for moderate to severe OSA. Interventions: Subjects were randomized to either home-based limited channel portable monitoring followed by unattended autotitration with continuous positive airway pressure (CPAP), versus a traditional pathway of in-laboratory sleep study and CPAP titration. Measurements and Results: From the payer perspective, per subject costs for the laboratory-based pathway were $1,840 (95% confidence interval [CI] $1,660, $2,015) compared to $1,575 (95% CI $1,439, $1,716) for the home-based pathway under the base case. Costs were $264 (95% CI $39, $496, P = 0.02) in favor of the home arm. From the provider perspective, per subject costs for the laboratory arm were $1,697 (95% CI $1,566, $1,826) compared to $1,736 (95% CI $1,621, $1,857) in the home arm, for a difference of $40 (95% CI −$213, $142, P = 0.66) in favor of the laboratory arm under the base case. The provider operating margin was $142 (95% CI $85, $202,P < 0.01) in the laboratory arm, compared to a loss of −$161 (95% CI −$202, −$120, P < 0.01) in the home arm. Conclusions: For payers, a home-based diagnostic pathway for obstructive sleep apnea with robust patient support incurs fewer costs than a laboratory-based pathway. For providers, costs are comparable if not higher, resulting in a negative operating margin. Clinicaltrials.gov Identifier: NCT00642486. Citation: Kim RD, Kapur VK, Redline-Bruch J, Rueschman M, Auckley DH, Benca RM, Foldvary-Schafer NR, Iber C, Zee PC, Rosen CL, Redline S, Ramsey SD. An economic

  19. Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

    NASA Technical Reports Server (NTRS)

    Herrin, Stephanie; Iverson, David; Spukovska, Lilly; Souza, Kenneth A. (Technical Monitor)

    1994-01-01

    Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

  20. Constraint Based Modeling Going Multicellular

    PubMed Central

    Martins Conde, Patricia do Rosario; Sauter, Thomas; Pfau, Thomas

    2016-01-01

    Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches. PMID:26904548

  1. Modeling Guru: Knowledge Base for NASA Modelers

    NASA Astrophysics Data System (ADS)

    Seablom, M. S.; Wojcik, G. S.; van Aartsen, B. H.

    2009-05-01

    Modeling Guru is an on-line knowledge-sharing resource for anyone involved with or interested in NASA's scientific models or High End Computing (HEC) systems. Developed and maintained by the NASA's Software Integration and Visualization Office (SIVO) and the NASA Center for Computational Sciences (NCCS), Modeling Guru's combined forums and knowledge base for research and collaboration is becoming a repository for the accumulated expertise of NASA's scientific modeling and HEC communities. All NASA modelers and associates are encouraged to participate and provide knowledge about the models and systems so that other users may benefit from their experience. Modeling Guru is divided into a hierarchy of communities, each with its own set forums and knowledge base documents. Current modeling communities include those for space science, land and atmospheric dynamics, atmospheric chemistry, and oceanography. In addition, there are communities focused on NCCS systems, HEC tools and libraries, and programming and scripting languages. Anyone may view most of the content on Modeling Guru (available at http://modelingguru.nasa.gov/), but you must log in to post messages and subscribe to community postings. The site offers a full range of "Web 2.0" features, including discussion forums, "wiki" document generation, document uploading, RSS feeds, search tools, blogs, email notification, and "breadcrumb" links. A discussion (a.k.a. forum "thread") is used to post comments, solicit feedback, or ask questions. If marked as a question, SIVO will monitor the thread, and normally respond within a day. Discussions can include embedded images, tables, and formatting through the use of the Rich Text Editor. Also, the user can add "Tags" to their thread to facilitate later searches. The "knowledge base" is comprised of documents that are used to capture and share expertise with others. The default "wiki" document lets users edit within the browser so others can easily collaborate on the

  2. Uncertain diagnosis after newborn screening for cystic fibrosis: An ethics-based approach to a clinical dilemma.

    PubMed

    Massie, John; Gillam, Lynn

    2014-01-01

    There is uncertainty about the diagnosis of cystic fibrosis after newborn screening (NBS) for some babies, either because of an intermediate sweat chloride test or inconclusive gene mutation analysis. There is considerable difficulty knowing how best to manage these babies, some of whom will develop cystic fibrosis, but many not. This article offers an ethics-based approach to this clinical dilemma that should be helpful to clinicians managing the baby with an uncertain diagnosis of cystic fibrosis after NBS. PMID:24166986

  3. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

    NASA Astrophysics Data System (ADS)

    He, Shuilong; Chen, Jinglong; Zhou, Zitong; Zi, Yanyang; Wang, Yanxue; Wang, Xiaodong

    2016-08-01

    Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

  4. Irritable bowel syndrome in children: Pathogenesis, diagnosis and evidence-based treatment

    PubMed Central

    Sandhu, Bhupinder Kaur; Paul, Siba Prosad

    2014-01-01

    Irritable bowel syndrome (IBS) is the commonest cause of recurrent abdominal pain (RAP) in children in both more developed and developing parts of the world. It is defined by the Rome III criteria for functional gastrointestinal disorders. It is characterized by abdominal pain that is improved by defecation and whose onset is associated with a change in stool form and or frequency and is not explained by structural or biochemical abnormalities. It is estimated that 10%-15% of older children and adolescents suffer from IBS. IBS can be considered to be a brain-gut disorder possibly due to complex interaction between environmental and hereditary factors. The diagnosis of IBS is made based on the Rome III criteria together with ruling out organic causes of RAP in children such as inflammatory bowel disease and celiac disease. Once the diagnosis of IBS is made, it is important to explain to the parents (and children) that there is no serious underlying disease. This reassurance may be effective treatment in a large number of cases. Lifestyle modifications, stress management, dietary interventions and probiotics may be beneficial in some cases. Although there is limited evidence for efficacy of pharmacological therapies such as antispasmodics and antidiarrheals; these have a role in severe cases. Biopsychosocial therapies have shown encouraging results in initial trials but are beset by limited availability. Further research is necessary to understand the pathophysiology and provide specific focused therapies. PMID:24876724

  5. Evidence-based recommendations for genetic diagnosis of familial Mediterranean fever.

    PubMed

    Giancane, Gabriella; Ter Haar, Nienke M; Wulffraat, Nico; Vastert, Sebastiaan J; Barron, Karyl; Hentgen, Veronique; Kallinich, Tilmann; Ozdogan, Huri; Anton, Jordi; Brogan, Paul; Cantarini, Luca; Frenkel, Joost; Galeotti, Caroline; Gattorno, Marco; Grateau, Gilles; Hofer, Michael; Kone-Paut, Isabelle; Kuemmerle-Deschner, Jasmin; Lachmann, Helen J; Simon, Anna; Demirkaya, Erkan; Feldman, Brian; Uziel, Yosef; Ozen, Seza

    2015-04-01

    Familial Mediterranean fever (FMF) is a disease of early onset which can lead to significant morbidity. In 2012, Single Hub and Access point for pediatric Rheumatology in Europe (SHARE) was launched with the aim of optimising and disseminating diagnostic and management regimens for children and young adults with rheumatic diseases. The objective was to establish recommendations for FMF focusing on provision of diagnostic tools for inexperienced clinicians particularly regarding interpretation of MEFV mutations. Evidence-based recommendations were developed using the European League against Rheumatism standard operating procedure. An expert committee of paediatric rheumatologists defined search terms for the systematic literature review. Two independent experts scored articles for validity and level of evidence. Recommendations derived from the literature were evaluated by an online survey and statements with less than 80% agreement were reformulated. Subsequently, all recommendations were discussed at a consensus meeting using the nominal group technique and were accepted if more than 80% agreement was reached. The literature search yielded 3386 articles, of which 25 were considered relevant and scored for validity and level of evidence. In total, 17 articles were scored valid and used to formulate the recommendations. Eight recommendations were accepted with 100% agreement after the consensus meeting. Topics covered were clinical versus genetic diagnosis of FMF, genotype-phenotype correlation, genotype-age at onset correlation, silent carriers and risk of amyloid A (AA) amyloidosis, and role of the specialist in FMF diagnosis. The SHARE initiative provides recommendations for diagnosing FMF aimed at facilitating improved and uniform care throughout Europe. PMID:25628446

  6. The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

    NASA Technical Reports Server (NTRS)

    Lee, S. C.

    1989-01-01

    The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

  7. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection.

    PubMed

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-01-01

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

  8. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

    PubMed Central

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-01-01

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

  9. Recent advances in diagnosis and treatment of gliomas using chlorotoxin-based bioconjugates

    PubMed Central

    Cheng, Yongjun; Zhao, Jinhua; Qiao, Wenli; Chen, Kai

    2014-01-01

    Malignant gliomas, especially glioblastoma multiforme, are the most widely distributed and deadliest brain tumors because of their resistance to surgical and medical treatment. Research of glioma-specific bioconjugates for diagnosis and therapy developed rapidly during the past several years. Many studies have demonstrated that chlorotoxin (CTX) and Buthus martensii Karsch chlorotoxin (BmK CT) specifically inhibited glioma cells growth and metastasis, and accelerated tumor apoptosis. The bioconjugates of CTX or BmK CT with other molecules have played an increasing role in diagnostic imaging and treatment of gliomas. To date, CTX-based bioconjugates have achieved great success in phase I/II clinical trials about safety profiles. Here, we will provide a review on the important role of ion channels in the underlying mechanisms of gliomas invasive growth and how CTX suppresses gliomas proliferation and migration. We will summarize the recent advances in the applications of CTX bioconjugates for gliomas diagnosis and treatment. In addition, we will review recent studies on BmK CT bioconjugates and compare their efficacies with CTX derivatives. Finally, we will address advantages and challenges in the use of CTX or BmK CT bioconjugates as specific agents for theranostic applications in gliomas. PMID:25143859

  10. Efficient Targeted Next Generation Sequencing-Based Workflow for Differential Diagnosis of Alport-Related Disorders.

    PubMed

    Kovács, Gábor; Kalmár, Tibor; Endreffy, Emőke; Ondrik, Zoltán; Iványi, Béla; Rikker, Csaba; Haszon, Ibolya; Túri, Sándor; Sinkó, Mária; Bereczki, Csaba; Maróti, Zoltán

    2016-01-01

    Alport syndrome (AS) is an inherited type IV collagen nephropathies characterized by microscopic hematuria during early childhood, the development of proteinuria and progression to end-stage renal disease. Since choosing the right therapy, even before the onset of proteinuria, can delay the onset of end-stage renal failure and improve life expectancy, the earliest possible differential diagnosis is desired. Practically, this means the identification of mutation(s) in COL4A3-A4-A5 genes. We used an efficient, next generation sequencing based workflow for simultaneous analysis of all three COL4A genes in three individuals and fourteen families involved by AS or showing different level of Alport-related symptoms. We successfully identified mutations in all investigated cases, including 14 unpublished mutations in our Hungarian cohort. We present an easy to use unified clinical/diagnostic terminology and workflow not only for X-linked but for autosomal AS, but also for Alport-related diseases. In families where a diagnosis has been established by molecular genetic analysis, the renal biopsy may be rendered unnecessary. PMID:26934356

  11. Differential diagnosis between mesothelioma and adenocarcinoma: a multimodal approach based on ultrastructure and immunocytochemistry

    SciTech Connect

    Bedrossian, C.W.; Bonsib, S.; Moran, C. )

    1992-05-01

    Most compensations for asbestos-related deaths secondary to cancer center around mesothelioma and bronchogenic carcinoma. The differential diagnosis between mesothelioma and adenocarcinoma is a common and troublesome one, necessitating the correlation between clinical history, radiographic findings, and pathologic examination of tissues and cells. We describe a multimodal approach based on the use of routine and special stains, immunocytochemistry, and electron microscopy for distinguishing between mesothelioma and adenocarcinoma. Once a malignant diagnosis is arrived at by careful pathological examination, the tumor is classified as mesothelioma if mesothelial cells are identified as the constituent cells of the neoplasm. Mesothelial cells are recognized by (1) their main ultrastructural features: slender and elongated microvilli, abundant intermediate filaments, and lacking secretory granules; and (2) their characteristic immunocytochemical reactivity: positivity for cytokeratin, EMA, and vimentin, and negativity for carcinoembryonic antigen (CEA), B72-3, Leu-M1, and other gland-cell markers. A variety of methods have been attempted in an effort to distinguish between reactive and malignant mesothelial cells. In practice, however, such distinction depends more on experience and expertise than in any fool-proof ancillary tests. A number of these tests are discussed along with the illustration of classical and unusual examples of mesothelioma and other pleural tumors.

  12. Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    Liu, Hongmei; Wang, Xuan; Lu, Chen

    2015-08-01

    A rolling bearing vibration signal is nonlinear and non-stationary and has multiple components and multifractal properties. A rolling-bearing fault-diagnosis method based on Local Characteristic-scale Decomposition-Teager Energy Operator (LCD-TEO) and multifractal detrended fluctuation analysis (MF-DFA) is first proposed in this paper. First, the vibration signal was decomposed into several intrinsic scale components (ISCs) by using LCD, which is a newly developed signal decomposition method. Second, the instantaneous amplitude was obtained by applying the TEO to each major ISC for demodulation. Third, the intrinsic multifractality features hidden in each major ISC were extracted by using MF-DFA, among which the generalized Hurst exponents are selected as the multifractal feature in this paper. Finally, the feature vectors were obtained by applying principal components analysis (PCA) to the extracted multifractality features, thus reducing the dimension of the multifractal features and obtaining the fault feature insensitive to variation in working conditions, further enhancing the accuracy of diagnosis. According to the extracted feature vector, rolling bearing faults can be diagnosed under variable working conditions. The experimental results demonstrate its desirable diagnostic performance under both different working conditions and different fault severities. Simultaneously, the results of comparison show that the performance of the proposed diagnostic method outperforms that of Hilbert-Huang transform (HHT) combined with MF-DFA or LCD-TEO combined with mono-fractal analysis.

  13. Efficient Targeted Next Generation Sequencing-Based Workflow for Differential Diagnosis of Alport-Related Disorders

    PubMed Central

    Endreffy, Emőke; Ondrik, Zoltán; Iványi, Béla; Rikker, Csaba; Haszon, Ibolya; Túri, Sándor; Sinkó, Mária; Bereczki, Csaba; Maróti, Zoltán

    2016-01-01

    Alport syndrome (AS) is an inherited type IV collagen nephropathies characterized by microscopic hematuria during early childhood, the development of proteinuria and progression to end-stage renal disease. Since choosing the right therapy, even before the onset of proteinuria, can delay the onset of end-stage renal failure and improve life expectancy, the earliest possible differential diagnosis is desired. Practically, this means the identification of mutation(s) in COL4A3-A4-A5 genes. We used an efficient, next generation sequencing based workflow for simultaneous analysis of all three COL4A genes in three individuals and fourteen families involved by AS or showing different level of Alport-related symptoms. We successfully identified mutations in all investigated cases, including 14 unpublished mutations in our Hungarian cohort. We present an easy to use unified clinical/diagnostic terminology and workflow not only for X-linked but for autosomal AS, but also for Alport-related diseases. In families where a diagnosis has been established by molecular genetic analysis, the renal biopsy may be rendered unnecessary. PMID:26934356

  14. Development of a Fluorescence-Based Sensor for Rapid Diagnosis of Cyanide Exposure

    PubMed Central

    2015-01-01

    Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The “heavy” use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture “apparatus”, consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was <3 min, the linear range was 3.13–200 μM, and the limit of detection was 0.78 μM. None of the potential interferents investigated (NaHS, NH4OH, NaSCN, and human serum albumin) produced a signal that could be interpreted as a false positive or a false negative for cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits. PMID:24383576

  15. Towards the Neuropsychological Diagnosis of Alzheimer's Disease: A Hybrid Model in Decision Making

    NASA Astrophysics Data System (ADS)

    de Castro, Ana Karoline Araujo; Pinheiro, Placido Rogerio; Pinheiro, Mirian Caliope Dantas

    Dementias are syndromes described by a decline in memory and other neuropsychological changes especially occurring in the elderly and increasing exponentially in function of age. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, diagnosis of Alzheimer's disease is extremely important and it can provide better life conditions to patients and their families. This work presents a hybrid model, combining Influence Diagrams and the Multicriteria Method, for aiding to discover, from a battery of tests, which are the most attractive questions, in relation to the stages of CDR (Clinical Dementia Rating) in decision making for the diagnosis of Alzheimer's disease. This disease is the most common dementia. Influence Diagram is implemented using GeNie tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimer's disease developed by Consortium to Establish a Registry for Alzheimer's disease (CERAD).

  16. Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

    Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.

  17. The PCMDI Climate Data Analysis Tool (CDAT) - an open system approach to model diagnosis infrastructure

    NASA Astrophysics Data System (ADS)

    Fiorino, M.

    2001-05-01

    The Climate Data Analysis Tool (CDAT) is software infrastructure that uses the object-oriented python scripting language to link separate software subsystems and thus form an integrated environment for solving model diagnosis problems. The power of the system comes from python and the software subsystems. Python provides a general purpose and full-featured scripting language with a variety of user interfaces including command line interaction, stand-alone scripts (applications) and GUIs. The CDAT subsystems, implemented as python modules, provide access and management of gridded data; large-array numerical operations; and visualization. We characterize CDAT as "open system" because the software subsystems are independent and the object-oriented nature of python allows CDAT to be "delay bound" or that the actual tool is built at run time, i.e., is not fixed. Thus, CDAT is easily extended and represents a different approach to the technical problem of model diagnosis. In this paper, we compare and contrast the CDAT approach with more traditional tools built from system-level software (e.g., C and X windows), such as GrADS and ferret, and show how CDAT complements and offers an alternative interface to data accessible by these popular tools.

  18. Serological assays based on recombinant viral proteins for the diagnosis of arenavirus hemorrhagic fevers.

    PubMed

    Fukushi, Shuetsu; Tani, Hideki; Yoshikawa, Tomoki; Saijo, Masayuki; Morikawa, Shigeru

    2012-10-01

    The family Arenaviridae, genus Arenavirus, consists of two phylogenetically independent groups: Old World (OW) and New World (NW) complexes. The Lassa and Lujo viruses in the OW complex and the Guanarito, Junin, Machupo, Sabia, and Chapare viruses in the NW complex cause viral hemorrhagic fever (VHF) in humans, leading to serious public health concerns. These viruses are also considered potential bioterrorism agents. Therefore, it is of great importance to detect these pathogens rapidly and specifically in order to minimize the risk and scale of arenavirus outbreaks. However, these arenaviruses are classified as BSL-4 pathogens, thus making it difficult to develop diagnostic techniques for these virus infections in institutes without BSL-4 facilities. To overcome these difficulties, antibody detection systems in the form of an enzyme-linked immunosorbent assay (ELISA) and an indirect immunofluorescence assay were developed using recombinant nucleoproteins (rNPs) derived from these viruses. Furthermore, several antigen-detection assays were developed. For example, novel monoclonal antibodies (mAbs) to the rNPs of Lassa and Junin viruses were generated. Sandwich antigen-capture (Ag-capture) ELISAs using these mAbs as capture antibodies were developed and confirmed to be sensitive and specific for detecting the respective arenavirus NPs. These rNP-based assays were proposed to be useful not only for an etiological diagnosis of VHFs, but also for seroepidemiological studies on VHFs. We recently developed arenavirus neutralization assays using vesicular stomatitis virus (VSV)-based pseudotypes bearing arenavirus recombinant glycoproteins. The goal of this article is to review the recent advances in developing laboratory diagnostic assays based on recombinant viral proteins for the diagnosis of VHFs and epidemiological studies on the VHFs caused by arenaviruses. PMID:23202455

  19. Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms.

    PubMed

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-01-01

    Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. PMID:24936949

  20. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

    PubMed Central

    Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Kanda, Paulo A. M.; Anghinah, Renato

    2014-01-01

    Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment. PMID:24723886

  1. Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

    PubMed Central

    2012-01-01

    Background Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer’s Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. Methods It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. Results Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. Conclusions All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes

  2. Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

    NASA Astrophysics Data System (ADS)

    Cabrera, Diego; Sancho, Fernando; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Li, Chuan; Vásquez, Rafael E.

    2015-09-01

    This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal's condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients' energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters' space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

  3. Model-based machine learning

    PubMed Central

    Bishop, Christopher M.

    2013-01-01

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  4. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  5. Thyroid Fine-Needle Aspiration Biopsy and Thyroid Cancer Diagnosis: A Nationwide Population-Based Study

    PubMed Central

    Huang, Li-Ying; Lee, Ya-Ling; Chou, Pesus; Chiu, Wei-Yih; Chu, Dachen

    2015-01-01

    Background Thyroid cancer is the most common endocrine gland malignancy and fine-needle aspiration biopsy is widely used for thyroid nodule evaluation. Repeated aspiration biopsies are needed due to plausible false-negative results. This study aimed to investigate the overall relationship between aspiration biopsy and thyroid cancer diagnosis, and to explore factors related to shorter diagnostic time. Methods This nationwide retrospective cohort study retrieved data from the Longitudinal Health Insurance Database in Taiwan. Subjects without known thyroid malignancies and who received the first thyroid aspiration biopsy after 2004 were followed-up from 2004 to 2009 (n = 7700). Chi-square test, Kaplan-Meier survival analysis, and Cox proportional hazards model were used for data analysis. Results Of 7700 newly-aspirated patients, 276 eventually developed thyroid cancer (malignancy rate 3.6%). Among the 276 patients with thyroid cancer, 61.6% underwent only one aspiration biopsy and 81.2% were found within the first year after the initial aspiration. Cox proportional hazards model revealed that aspiration frequency (HR 1.07, 95% CI 1.06–1.08), ultrasound frequency (HR 1.02, 95% CI 1.01–1.03), older age, male sex, and aspiration biopsies arranged by surgery, endocrinology or otolaryngology subspecialties were all associated with shorter time to thyroid cancer diagnosis. Conclusions About 17.4% of thyroid cancer cases received more than two aspiration biopsies and 18.8% were diagnosed one year after the first biopsy. Regular follow-up with repeated aspiration or ultrasound may be required for patients with clinically significant thyroid nodules. PMID:26020790

  6. a Historical Timber Frame Model for Diagnosis and Documentation Before Building Restoration

    NASA Astrophysics Data System (ADS)

    Koehl, M.; Viale, A.; Reeb, S.

    2013-09-01

    The aim of the project that is described in this paper was to define a four-level timber frame survey mode of a historical building: the so-called "Andlau's Seigniory", Alsace, France. This historical building (domain) was built in the late XVIth century and is now in a stage of renovation in order to become a heritage interpretation centre. The used measurement methods combine Total Station measurements, Photogrammetry and 3D Terrestrial Laser scanner. Different modelling workflows were tested and compared according to the data acquisition method, but also according to the characteristics of the reconstructed model in terms of accuracy and level of detail. 3D geometric modelling of the entire structure was performed including modelling the degree of detail adapted to the needs. The described 3D timber framework exists now in different versions, from a theoretical and geometrical one up to a very detailed one, in which measurements and evaluation of deformation by time are potentially allowed. The virtually generated models involving archaeologists, architects, historians and specialists in historical crafts, are intended to be used during the four stages of the project: (i) knowledge of the current state of needs for diagnosis and understanding of former construction techniques; (ii) preparation and evaluation of restoration steps; (iii) knowledge and documentation concerning the archaeological object; (iv) transmission and dissemination of knowledge through the implementation of museum animations. Among the generated models we can also find a documentation of the site in the form of virtual tours created from panoramic photographs before and during the restoration works. Finally, the timber framework model was structured and integrated into a 3D GIS, where the association of descriptive and complementary digital documents was possible. Both offer tools leading to the diagnosis, the understanding of the structure, knowledge dissemination, documentation and the

  7. Supporting diagnosis and treatment in medical care based on Big Data processing.

    PubMed

    Lupşe, Oana-Sorina; Crişan-Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara; Bernard, Elena

    2014-01-01

    With information and data in all domains growing every day, it is difficult to manage and extract useful knowledge for specific situations. This paper presents an integrated system architecture to support the activity in the Ob-Gin departments with further developments in using new technology to manage Big Data processing - using Google BigQuery - in the medical domain. The data collected and processed with Google BigQuery results from different sources: two Obstetrics & Gynaecology Departments, the TreatSuggest application - an application for suggesting treatments, and a home foetal surveillance system. Data is uploaded in Google BigQuery from Bega Hospital Timişoara, Romania. The analysed data is useful for the medical staff, researchers and statisticians from public health domain. The current work describes the technological architecture and its processing possibilities that in the future will be proved based on quality criteria to lead to a better decision process in diagnosis and public health. PMID:24743079

  8. A Smartphone-Based Automatic Diagnosis System for Facial Nerve Palsy

    PubMed Central

    Kim, Hyun Seok; Kim, So Young; Kim, Young Ho; Park, Kwang Suk

    2015-01-01

    Facial nerve palsy induces a weakness or loss of facial expression through damage of the facial nerve. A quantitative and reliable assessment system for facial nerve palsy is required for both patients and clinicians. In this study, we propose a rapid and portable smartphone-based automatic diagnosis system that discriminates facial nerve palsy from normal subjects. Facial landmarks are localized and tracked by an incremental parallel cascade of the linear regression method. An asymmetry index is computed using the displacement ratio between the left and right side of the forehead and mouth regions during three motions: resting, raising eye-brow and smiling. To classify facial nerve palsy, we used Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), and Leave-one-out Cross Validation (LOOCV) with 36 subjects. The classification accuracy rate was 88.9%. PMID:26506352

  9. Fetal Cell Based Prenatal Diagnosis: Perspectives on the Present and Future

    PubMed Central

    Fiddler, Morris

    2014-01-01

    The ability to capture and analyze fetal cells from maternal circulation or other sources during pregnancy has been a goal of prenatal diagnostics for over thirty years. The vision of replacing invasive prenatal diagnostic procedures with the prospect of having the entire fetal genome in hand non-invasively for chromosomal and molecular studies for both clinical and research use has brought many investigators and innovations into the effort. While the object of this desire, however, has remained elusive, the aspiration for this approach to non-invasive prenatal diagnosis remains and the inquiry has continued. With the advent of screening by cell-free DNA analysis, the standards for fetal cell based prenatal diagnostics have been sharpened. Relevant aspects of the history and the current status of investigations to meet the goal of having an accessible and reliable strategy for capturing and analyzing fetal cells during pregnancy are reviewed. PMID:26237488

  10. Graphene based aptasensor for glycated albumin in diabetes mellitus diagnosis and monitoring.

    PubMed

    Apiwat, Chayachon; Luksirikul, Patraporn; Kankla, Pacharapon; Pongprayoon, Prapasiri; Treerattrakoon, Kiatnida; Paiboonsukwong, Kittiphong; Fucharoen, Suthat; Dharakul, Tararaj; Japrung, Deanpen

    2016-08-15

    We selected and modified DNA aptamers specifically bound glycated human serum albumin (GHSA), which is an intermediate marker for diabetes mellitus. Our aptamer truncation study indicated that the hairpin-loop structure with 23 nucleotides length containing triple G-C hairpins and 15-nucleotide loop, plays an important role in GHSA binding. Fluorescent quenching graphene oxide (GO) and Cy5-labeled G8 aptamer were used in this study to develop simple and sensitive graphene based aptasensor for GHSA detection. The limit of detection (LOD) of our aptasensor was 50 μg/mL, which was lower than other existing methods. In addition, with the nuclease resistance system, our GHSA detection platform could also be used in clinical samples. Importantly, our approach could significantly reveal the higher levels of GHSA concentrations in diabetes than normal serums. These indicate that our aptasensor has a potential for diagnosis and monitoring of diabetes mellitus. PMID:27084987

  11. Online damage diagnosis for civil infrastructure employing a flexibility-based approach

    NASA Astrophysics Data System (ADS)

    Gao, Y.; Spencer, B. F., Jr.

    2006-02-01

    Structural health monitoring (SHM) and damage detection have recently emerged as a new research area in civil engineering. Continuous and long-term monitoring of civil infrastructure is desirable, because it allows the damage in the structure to be detected at an early stage so that necessary measures can be carried out to prolong and optimize the associated service life and cost. In this paper, an approach which extends a flexibility-based damage detection technique, the damage locating vector (DLV) method, for continuous online SHM is presented. The essence of the proposed approach is to construct an approximate flexibility matrix for the damaged structure utilizing the modal normalization constants from the undamaged structure. This extended DLV method can then be applied for online damage diagnosis. Numerical simulation has been conducted using a 14-bay planar truss structure, with the results showing that the proposed approach works well for both single- and multiple-damage scenarios.

  12. Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

    PubMed Central

    Li, Rongjian; Zhang, Wenlu; Suk, Heung-Il; Wang, Li; Li, Jiang; Shen, Dinggang; Ji, Shuiwang

    2015-01-01

    Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multi-modality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods. PMID:25320813

  13. A smartphone-based automatic diagnosis system for facial nerve palsy.

    PubMed

    Kim, Hyun Seok; Kim, So Young; Kim, Young Ho; Park, Kwang Suk

    2015-01-01

    Facial nerve palsy induces a weakness or loss of facial expression through damage of the facial nerve. A quantitative and reliable assessment system for facial nerve palsy is required for both patients and clinicians. In this study, we propose a rapid and portable smartphone-based automatic diagnosis system that discriminates facial nerve palsy from normal subjects. Facial landmarks are localized and tracked by an incremental parallel cascade of the linear regression method. An asymmetry index is computed using the displacement ratio between the left and right side of the forehead and mouth regions during three motions: resting, raising eye-brow and smiling. To classify facial nerve palsy, we used Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), and Leave-one-out Cross Validation (LOOCV) with 36 subjects. The classification accuracy rate was 88.9%. PMID:26506352

  14. Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

    NASA Astrophysics Data System (ADS)

    Zhang, Shengli; Tang, Jiong

    2016-04-01

    Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

  15. Fault diagnosis of rolling bearing based on fast nonlocal means and envelop spectrum.

    PubMed

    Lv, Yong; Zhu, Qinglin; Yuan, Rui

    2015-01-01

    The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

  16. AuNPs modified, disposable, ITO based biosensor: Early diagnosis of heat shock protein 70.

    PubMed

    Sonuç Karaboğa, Münteha Nur; Şimşek, Çiğdem Sayıklı; Sezgintürk, Mustafa Kemal

    2016-10-15

    This paper describes a novel, simple, and disposable immunosensor based on indium-tin oxide (ITO) sheets modified with gold nanoparticles to sensitively analyze heat shock protein 70 (HSP70), a potential biomarker that could be evaluated in diagnosis of some carcinomas. Disposable ITO coated Polyethylene terephthalate (PET) electrodes were used and modified with gold nanoparticles in order to construct the biosensors. Optimization and characterization steps were analyzed by electrochemical techniques such as electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Surface morphology of the biosensor was also identified by electrochemical methods, scanning electron microscopy (SEM), and atomic force microscopy (AFM). To interpret binding characterization of HSP70 to anti-HSP70 single frequency impedance method was successfully operated. Moreover, the proposed HSP70 immunosensor acquired good stability, repeatability, and reproducibility. Ultimately, proposed biosensor was introduced to real human serum samples to determine HSP70 sensitively and accurately. PMID:26318579

  17. Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

    PubMed Central

    Lv, Yong; Zhu, Qinglin; Yuan, Rui

    2015-01-01

    The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

  18. A method of real-time fault diagnosis for power transformers based on vibration analysis

    NASA Astrophysics Data System (ADS)

    Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie

    2015-11-01

    In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

  19. Evidence-based guideline summary: Diagnosis and treatment of limb-girdle and distal dystrophies

    PubMed Central

    Narayanaswami, Pushpa; Weiss, Michael; Selcen, Duygu; David, William; Raynor, Elizabeth; Carter, Gregory; Wicklund, Matthew; Barohn, Richard J.; Ensrud, Erik; Griggs, Robert C.; Gronseth, Gary; Amato, Anthony A.

    2014-01-01

    Objective: To review the current evidence and make practice recommendations regarding the diagnosis and treatment of limb-girdle muscular dystrophies (LGMDs). Methods: Systematic review and practice recommendation development using the American Academy of Neurology guideline development process. Results: Most LGMDs are rare, with estimated prevalences ranging from 0.07 per 100,000 to 0.43 per 100,000. The frequency of some muscular dystrophies varies based on the ethnic background of the population studied. Some LGMD subtypes have distinguishing features, including pattern of muscle involvement, cardiac abnormalities, extramuscular involvement, and muscle biopsy findings. The few published therapeutic trials were not designed to establish clinical efficacy of any treatment. Principal recommendations: For patients with suspected muscular dystrophy, clinicians should use a clinical approach to guide genetic diagnosis based on clinical phenotype, inheritance pattern, and associated manifestations (Level B). Clinicians should refer newly diagnosed patients with an LGMD subtype and high risk of cardiac complications for cardiology evaluation even if they are asymptomatic from a cardiac standpoint (Level B). In patients with LGMD with a known high risk of respiratory failure, clinicians should obtain periodic pulmonary function testing (Level B). Clinicians should refer patients with muscular dystrophy to a clinic that has access to multiple specialties designed specifically to care for patients with neuromuscular disorders (Level B). Clinicians should not offer patients with LGMD gene therapy, myoblast transplantation, neutralizing antibody to myostatin, or growth hormone outside of a research study designed to determine efficacy and safety of the treatment (Level R). Detailed results and recommendations are available on the Neurology® Web site at Neurology.org. PMID:25313375

  20. Evaluation of Recombinant Antigen-Based Assays for Diagnosis of Bullous Autoimmune Diseases

    PubMed Central

    D'Agosto, G.; Latini, A.; Carducci, M.; Mastroianni, A.; Vento, A.; Fei, P. Cordiali

    2004-01-01

    The diagnosis of autoimmune bullous diseases is based on clinical observation and on the presence of autoantibodies directed to molecules involved in the adhesion systems of the skin. Immunofluorescence assays are the currently accepted method for detection of autoantibodies; such assays depend greatly on the skill of operators and are difficult to standardize. Recombinant desmoglein-1 (Dsg1), Dsg3, and BP180 peptides, the main autoantigens in pemphigus or bullous pemphigoid, have been used to develop new quantitative enzyme immunoassays (EIA) for the detection of specific antibodies. The present study was undertaken to evaluate the sensitivity and specificity of these immunoassays and to determine the correlation between the results and the clinical aspects of diseases. Serum samples from patients with pemphigus vulgaris, pemphigus foliaceus, bullous pemphigoid, or mucous membrane pemphigoid, from healthy individuals, and from patients with unrelated autoimmune conditions were tested. Anti-desmoglein reactivity was detected in all the patients with pemphigus and in none of the controls. Patients with the more benign form of cutaneous disease had anti-Dsg1 antibodies, while patients with deeper cutaneous lesions or with mucosal involvement had anti-Dsg3 reactivity also, or exclusively. The BP180-based assay was positive for 66.6% of patients with bullous pemphigoid and for none of the patients with mucous membrane pemphigoid, and no reactivity was detected in the control sera. In conclusion, the anti-Dsg1 and anti-Dsg3 assays are useful in the diagnosis of pemphigus and provide information on the clinical phenotype of the disease. However, the sensitivity of EIA for detection of autoantibodies in bullous pemphigoid should be improved by the use of additional antigens or epitopes. PMID:15242953

  1. PC-based workstation for global PACS remote consultation and diagnosis in rural clinics

    NASA Astrophysics Data System (ADS)

    Martinez, Ralph; Robles, Saul; Kim, Jinman

    1995-05-01

    Most rural clinics across the country have limited facilities to provide state-of-the-art medical services. The availability of enabling technologies, such as telecommunication networks, multimedia workstations, and telemedicine systems which provide medical services to patients without requiring them to travel from their cities represents a great step in patient care. In previous work, we have developed a distributed software for remote consultation and diagnosis (RCD) in a Global PACS environment over the Internet. The RCD system has been designed and tested on DEC and SUN workstations. In this paper, we present a Unix-PC based platform to implement the RCD over a standard telephone line and Serial Line Internet Protocol (SLIP). The Unix-PC platform offers an inexpensive option for telemedicine workstations in rural clinics, where no Internet is available. If an Internet connection is available at the rural clinic, full RCD multimedia services are possible. The Unix-PC platform has been developed by using Linux, a Unix-like operating system available from several public sites over the Internet. We call the system PC-PACS. The PC-PACS workstation has been tested from different rural sites by connecting the Unix-PC system to the Internet through SLIP. Once the system is connected, RCD sessions have been performed between the Unix- PC platform and SUN workstations. The tests have included diagnosis on radiology and pathology images. A separate telephone line for voice communications during the RCD session is required. This paper describes performance tests for the PC-based workstation and the RCD system over SLIP and Ethernet interfaces. Results show acceptable performance of the workstation and the RCD software.

  2. Real time diagnosis of bladder cancer with probe-based confocal laser endomicroscopy

    NASA Astrophysics Data System (ADS)

    Liu, Jen-Jane; Wu, Katherine; Adams, Winifred; Hsiao, Shelly T.; Mach, Kathleen E.; Beck, Andrew H.; Jensen, Kristin C.; Liao, Joseph C.

    2011-02-01

    Probe-based confocal laser endomicroscopy (pCLE) is an emerging technology for in vivo optical imaging of the urinary tract. Particularly for bladder cancer, real time optical biopsy of suspected lesions will likely lead to improved management of bladder cancer. With pCLE, micron scale resolution is achieved with sterilizable imaging probes (1.4 or 2.6 mm diameter), which are compatible with standard cystoscopes and resectoscopes. Based on our initial experience to date (n = 66 patients), we have demonstrated the safety profile of intravesical fluorescein administration and established objective diagnostic criteria to differentiate between normal, benign, and neoplastic urothelium. Confocal images of normal bladder showed organized layers of umbrella cells, intermediate cells, and lamina propria. Low grade bladder cancer is characterized by densely packed monomorphic cells with central fibrovascular cores, whereas high grade cancer consists of highly disorganized microarchitecture and pleomorphic cells with indistinct cell borders. Currently, we are conducting a diagnostic accuracy study of pCLE for bladder cancer diagnosis. Patients scheduled to undergo transurethral resection of bladder tumor are recruited. Patients undergo first white light cystocopy (WLC), followed by pCLE, and finally histologic confirmation of the resected tissues. The diagnostic accuracy is determined both in real time by the operative surgeon and offline after additional image processing. Using histology as the standard, the sensitivity, specificity, positive and negative predictive value of WLC and WLC + pCLE are calculated. With additional validation, pCLE may prove to be a valuable adjunct to WLC for real time diagnosis of bladder cancer.

  3. Diagnosis and treatment of melanoma. European consensus-based interdisciplinary guideline - Update 2016.

    PubMed

    Garbe, Claus; Peris, Ketty; Hauschild, Axel; Saiag, Philippe; Middleton, Mark; Bastholt, Lars; Grob, Jean-Jacques; Malvehy, Josep; Newton-Bishop, Julia; Stratigos, Alexander J; Pehamberger, Hubert; Eggermont, Alexander M

    2016-08-01

    Cutaneous melanoma (CM) is potentially the most dangerous form of skin tumour and causes 90% of skin cancer mortality. A unique collaboration of multi-disciplinary experts from the European Dermatology Forum, the European Association of Dermato-Oncology and the European Organisation of Research and Treatment of Cancer was formed to make recommendations on CM diagnosis and treatment, based on systematic literature reviews and the experts' experience. Diagnosis is made clinically using dermoscopy and staging is based upon the AJCC system. CMs are excised with 1-2 cm safety margins. Sentinel lymph node dissection is routinely offered as a staging procedure in patients with tumours >1 mm in thickness, although there is as yet no clear survival benefit for this approach. Interferon-α treatment may be offered to patients with stage II and III melanoma as an adjuvant therapy, as this treatment increases at least the disease-free survival and less clear the overall survival (OS) time. The treatment is however associated with significant toxicity. In distant metastasis, all options of surgical therapy have to be considered thoroughly. In the absence of surgical options, systemic treatment is indicated. For first-line treatment particularly in BRAF wild-type patients, immunotherapy with PD-1 antibodies alone or in combination with CTLA-4 antibodies should be considered. BRAF inhibitors like dabrafenib and vemurafenib in combination with the MEK inhibitors trametinib and cobimetinib for BRAF mutated patients should be offered as first or second line treatment. Therapeutic decisions in stage IV patients should be primarily made by an interdisciplinary oncology team ('Tumour Board'). PMID:27367293

  4. Diagnosis and treatment of acute ankle injuries: development of an evidence-based algorithm

    PubMed Central

    Polzer, Hans; Kanz, Karl Georg; Prall, Wolf Christian; Haasters, Florian; Ockert, Ben; Mutschler, Wolf; Grote, Stefan

    2011-01-01

    Acute ankle injuries are among the most common injuries in emergency departments. However, there are still no standardized examination procedures or evidence-based treatment. Therefore, the aim of this study was to systematically search the current literature, classify the evidence, and develop an algorithm for the diagnosis and treatment of acute ankle injuries. We systematically searched PubMed and the Cochrane Database for randomized controlled trials, meta-analyses, systematic reviews or, if applicable, observational studies and classified them according to their level of evidence. According to the currently available literature, the following recommendations have been formulated: i) the Ottawa Ankle/Foot Rule should be applied in order to rule out fractures; ii) physical examination is sufficient for diagnosing injuries to the lateral ligament complex; iii) classification into stable and unstable injuries is applicable and of clinical importance; iv) the squeeze-, crossed leg- and external rotation test are indicative for injuries of the syndesmosis; v) magnetic resonance imaging is recommended to verify injuries of the syndesmosis; vi) stable ankle sprains have a good prognosis while for unstable ankle sprains, conservative treatment is at least as effective as operative treatment without the related possible complications; vii) early functional treatment leads to the fastest recovery and the least rate of reinjury; viii) supervised rehabilitation reduces residual symptoms and re-injuries. Taken these recommendations into account, we present an applicable and evidence-based, step by step, decision pathway for the diagnosis and treatment of acute ankle injuries, which can be implemented in any emergency department or doctor's practice. It provides quality assurance for the patient and promotes confidence in the attending physician. PMID:22577506

  5. Consistency of Cluster Analysis for Cognitive Diagnosis: The Reduced Reparameterized Unified Model and the General Diagnostic Model.

    PubMed

    Chiu, Chia-Yi; Köhn, Hans-Friedrich

    2016-09-01

    The asymptotic classification theory of cognitive diagnosis (ACTCD) provided the theoretical foundation for using clustering methods that do not rely on a parametric statistical model for assigning examinees to proficiency classes. Like general diagnostic classification models, clustering methods can be useful in situations where the true diagnostic classification model (DCM) underlying the data is unknown and possibly misspecified, or the items of a test conform to a mix of multiple DCMs. Clustering methods can also be an option when fitting advanced and complex DCMs encounters computational difficulties. These can range from the use of excessive CPU times to plain computational infeasibility. However, the propositions of the ACTCD have only been proven for the Deterministic Input Noisy Output "AND" gate (DINA) model and the Deterministic Input Noisy Output "OR" gate (DINO) model. For other DCMs, there does not exist a theoretical justification to use clustering for assigning examinees to proficiency classes. But if clustering is to be used legitimately, then the ACTCD must cover a larger number of DCMs than just the DINA model and the DINO model. Thus, the purpose of this article is to prove the theoretical propositions of the ACTCD for two other important DCMs, the Reduced Reparameterized Unified Model and the General Diagnostic Model. PMID:27230079

  6. Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks

    PubMed Central

    Wen, Qiaoyan; Zhang, Yudong; Li, Wenmin

    2014-01-01

    With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients' health information and doctors' diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions. PMID:25126107

  7. Privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks.

    PubMed

    Sun, Yi; Wen, Qiaoyan; Zhang, Yudong; Li, Wenmin

    2014-01-01

    With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients' health information and doctors' diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions. PMID:25126107

  8. Web-based computer-aided-diagnosis (CAD) system for bone age assessment (BAA) of children

    NASA Astrophysics Data System (ADS)

    Zhang, Aifeng; Uyeda, Joshua; Tsao, Sinchai; Ma, Kevin; Vachon, Linda A.; Liu, Brent J.; Huang, H. K.

    2008-03-01

    Bone age assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on a left hand and wrist radiograph. The most commonly used standard: Greulich and Pyle (G&P) Hand Atlas was developed 50 years ago and exclusively based on Caucasian population. Moreover, inter- & intra-observer discrepancies using this method create a need of an objective and automatic BAA method. A digital hand atlas (DHA) has been collected with 1,400 hand images of normal children from Asian, African American, Caucasian and Hispanic descends. Based on DHA, a fully automatic, objective computer-aided-diagnosis (CAD) method was developed and it was adapted to specific population. To bring DHA and CAD method to the clinical environment as a useful tool in assisting radiologist to achieve higher accuracy in BAA, a web-based system with direct connection to a clinical site is designed as a novel clinical implementation approach for online and real time BAA. The core of the system, a CAD server receives the image from clinical site, processes it by the CAD method and finally, generates report. A web service publishes the results and radiologists at the clinical site can review it online within minutes. This prototype can be easily extended to multiple clinical sites and will provide the foundation for broader use of the CAD system for BAA.

  9. Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images

    PubMed Central

    2012-01-01

    Background Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. Method The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. Results The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice. PMID:22236465

  10. Fault detection, diagnosis, and data-driven modeling in HVAC chillers

    NASA Astrophysics Data System (ADS)

    Namburu, Setu M.; Luo, Jianhui; Azam, Mohammad; Choi, Kihoon; Pattipati, Krishna R.

    2005-05-01

    Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and most of the schemes are systemspecific. A generic real-time FDD scheme, applicable to all possible operating conditions, can significantly reduce HVAC equipment downtime, thus improving the efficiency of building energy management systems. This paper presents a FDD methodology for faults in centrifugal chillers. The FDD scheme compares the diagnostic performance of three data-driven techniques, namely support vector machines (SVM), principal component analysis (PCA), and partial least squares (PLS). In addition, a nominal model of a chiller that can predict system response under new operating conditions is developed using PLS. We used the benchmark data on a 90-ton real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables under nominal and eight fault conditions of different severities at twenty seven operating modes.

  11. Sketch-based geologic modeling

    NASA Astrophysics Data System (ADS)

    Rood, M. P.; Jackson, M.; Hampson, G.; Brazil, E. V.; de Carvalho, F.; Coda, C.; Sousa, M. C.; Zhang, Z.; Geiger, S.

    2015-12-01

    Two-dimensional (2D) maps and cross-sections, and 3D conceptual models, are fundamental tools for understanding, communicating and modeling geology. Yet geologists lack dedicated and intuitive tools that allow rapid creation of such figures and models. Standard drawing packages produce only 2D figures that are not suitable for quantitative analysis. Geologic modeling packages can produce 3D models and are widely used in the groundwater and petroleum communities, but are often slow and non-intuitive to use, requiring the creation of a grid early in the modeling workflow and the use of geostatistical methods to populate the grid blocks with geologic information. We present an alternative approach to rapidly create figures and models using sketch-based interface and modelling (SBIM). We leverage methods widely adopted in other industries to prototype complex geometries and designs. The SBIM tool contains built-in geologic rules that constrain how sketched lines and surfaces interact. These rules are based on the logic of superposition and cross-cutting relationships that follow from rock-forming processes, including deposition, deformation, intrusion and modification by diagenesis or metamorphism. The approach allows rapid creation of multiple, geologically realistic, figures and models in 2D and 3D using a simple, intuitive interface. The user can sketch in plan- or cross-section view. Geologic rules are used to extrapolate sketched lines in real time to create 3D surfaces. Quantitative analysis can be carried our directly on the models. Alternatively, they can be output as simple figures or imported directly into other modeling tools. The software runs on a tablet PC and can be used in a variety of settings including the office, classroom and field. The speed and ease of use of SBIM enables multiple interpretations to be developed from limited data, uncertainty to be readily appraised, and figures and models to be rapidly updated to incorporate new data or concepts.

  12. Usability Evaluation of a Web-Based Support System for People With a Schizophrenia Diagnosis

    PubMed Central

    Emerencia, Ando C; Aiello, Marco; Sytema, Sjoerd

    2012-01-01

    Background Routine Outcome Monitoring (ROM) is a systematic way of assessing service users’ health conditions for the purpose of better aiding their care. ROM consists of various measures used to assess a service user’s physical, psychological, and social condition. While ROM is becoming increasingly important in the mental health care sector, one of its weaknesses is that ROM is not always sufficiently service user-oriented. First, clinicians tend to concentrate on those ROM results that provide information about clinical symptoms and functioning, whereas it has been suggested that a service user-oriented approach needs to focus on personal recovery. Second, service users have limited access to ROM results and they are often not equipped to interpret them. These problems need to be addressed, as access to resources and the opportunity to share decision making has been indicated as a prerequisite for service users to become a more equal partner in communication with their clinicians. Furthermore, shared decision making has been shown to improve the therapeutic alliance and to lead to better care. Objective Our aim is to build a web-based support system which makes ROM results more accessible to service users and to provide them with more concrete and personalized information about their functioning (ie, symptoms, housing, social contacts) that they can use to discuss treatment options with their clinician. In this study, we will report on the usability of the web-based support system for service users with schizophrenia. Methods First, we developed a prototype of a web-based support system in a multidisciplinary project team, including end-users. We then conducted a usability study of the support system consisting of (1) a heuristic evaluation, (2) a qualitative evaluation and (3) a quantitative evaluation. Results Fifteen service users with a schizophrenia diagnosis and four information and communication technology (ICT) experts participated in the study. The

  13. Essentials in the diagnosis of acid-base disorders and their high altitude application.

    PubMed

    Paulev, P E; Zubieta-Calleja, G R

    2005-09-01

    This report describes the historical development in the clinical application of chemical variables for the interpretation of acid-base disturbances. The pH concept was already introduced in 1909. Following World War II, disagreements concerning the definition of acids and bases occurred, and since then two strategies have been competing. Danish scientists in 1923 defined an acid as a substance able to give off a proton at a given pH, and a base as a substance that could bind a proton, whereas the North American Singer-Hasting school in 1948 defined acids as strong non-buffer anions and bases as non-buffer cations. As a consequence of this last definition, electrolyte disturbances were mixed up with real acid-base disorders and the variable, strong ion difference (SID), was introduced as a measure of non-respiratory acid-base disturbances. However, the SID concept is only an empirical approximation. In contrast, the Astrup/Siggaard-Andersen school of scientists, using computer strategies and the Acid-base Chart, has made diagnosis of acid-base disorders possible at a glance on the Chart, when the data are considered in context with the clinical development. Siggaard-Andersen introduced Base Excess (BE) or Standard Base Excess (SBE) in the extracellular fluid volume (ECF), extended to include the red cell volume (eECF), as a measure of metabolic acid-base disturbances and recently replaced it by the term Concentration of Titratable Hydrogen Ion (ctH). These two concepts (SBE and ctH) represent the same concentration difference, but with opposite signs. Three charts modified from the Siggaard-Andersen Acid-Base Chart are presented for use at low, medium and high altitudes of 2500 m, 3500 m, and 4000 m, respectively. In this context, the authors suggest the use of Titratable Hydrogen Ion concentration Difference (THID) in the extended extracellular fluid volume, finding it efficient and better than any other determination of the metabolic component in acid-base

  14. Effect of health belief model and health promotion model on breast cancer early diagnosis behavior: a systematic review.

    PubMed

    Ersin, Fatma; Bahar, Zuhal

    2011-01-01

    Breast cancer is an important public health problem on the grounds that it is frequently seen and it is a fatal disease. The objective of this systematic analysis is to indicate the effects of interventions performed by nurses by using the Health Belief Model (HBM) and Health Promotion Model (HPM) on the breast cancer early diagnosis behaviors and on the components of the Health Belief Model and Health Promotion Model. The reveiw was created in line with the Centre for Reviews and Dissemination guide dated 2009 (CRD) and developed by York University National Institute of Health Researches. Review was conducted by using PUBMED, OVID, EBSCO and COCHRANE databases. Six hundred seventy eight studies (PUBMED: 236, OVID: 162, EBSCO: 175, COCHRANE:105) were found in total at the end of the review. Abstracts and full texts of these six hundred seventy eight studies were evaluated in terms of inclusion and exclusion criteria and 9 studies were determined to meet the criteria. Samplings of the studies varied between ninety four and one thousand six hundred fifty five. It was detected in the studies that educations provided by taking the theories as basis became effective on the breast cancer early diagnosis behaviors. When the literature is examined, it is observed that the experimental researches which compare the concepts of Health Belief Model (HBM) and Health Promotion Model (HPM) preoperatively and postoperatively and show the effect of these concepts on education and are conducted by nurses are limited in number. Randomized controlled studies which compare HBM and HPM concepts preoperatively and postoperatively and show the efficiency of the interventions can be useful in evaluating the efficiency of the interventions. PMID:22320955

  15. A fault diagnosis approach for diesel engine valve train based on improved ITD and SDAG-RVM

    NASA Astrophysics Data System (ADS)

    Yu, Liu; Junhong, Zhang; Fengrong, Bi; Jiewei, Lin; Wenpeng, Ma

    2015-02-01

    Targeting the non-stationary characteristics of the vibration signals of a diesel engine valve train, and the limitation of the autoregressive (AR) model, a novel approach based on the improved intrinsic time-scale decomposition (ITD) and relevance vector machine (RVM) is proposed in this paper for the identification of diesel engine valve train faults. The approach mainly consists of three stages: First, prior to the feature extraction, non-uniform B-spline interpolation is introduced to the ITD method for the fitting of baseline signal, then the improved ITD is used to decompose the non-stationary signals into a set of stationary proper rotation components (PRCs). Second, the AR model is established for each PRC, and the first several AR coefficients together with the remnant variance of all PRCs are regarded as the fault feature vectors. Finally, a new separability based directed acyclic graph (SDAG) method is proposed to determine the structure of multi-class RVM, and the fault feature vectors are classified using the SDAG-RVM classifier to recognize the fault of the diesel engine valve train. The experimental results demonstrate that the proposed fault diagnosis approach can effectively extract the fault features and accurately identify the fault patterns.

  16. Formal Methods for Automated Diagnosis of Autosub 6000

    NASA Technical Reports Server (NTRS)

    Ernits, Juhan; Dearden, Richard; Pebody, Miles

    2009-01-01

    This is a progress report on applying formal methods in the context of building an automated diagnosis and recovery system for Autosub 6000, an Autonomous Underwater Vehicle (AUV). The diagnosis task involves building abstract models of the control system of the AUV. The diagnosis engine is based on Livingstone 2, a model-based diagnoser originally built for aerospace applications. Large parts of the diagnosis model can be built without concrete knowledge about each mission, but actual mission scripts and configuration parameters that carry important information for diagnosis are changed for every mission. Thus we use formal methods for generating the mission control part of the diagnosis model automatically from the mission script and perform a number of invariant checks to validate the configuration. After the diagnosis model is augmented with the generated mission control component model, it needs to be validated using verification techniques.

  17. Hospital payment systems based on diagnosis-related groups: experiences in low- and middle-income countries

    PubMed Central

    Wittenbecher, Friedrich

    2013-01-01

    Abstract Objective This paper provides a comprehensive overview of hospital payment systems based on diagnosis-related groups (DRGs) in low- and middle-income countries. It also explores design and implementation issues and the related challenges countries face. Methods A literature research for papers on DRG-based payment systems in low- and middle-income countries was conducted in English, French and Spanish through Pubmed, the Pan American Health Organization’s Regional Library of Medicine and Google. Findings Twelve low- and middle-income countries have DRG-based payment systems and another 17 are in the piloting or exploratory stage. Countries have chosen from a wide range of imported and self-developed DRG models and most have adapted such models to their specific contexts. All countries have set expenditure ceilings. In general, systems were piloted before being implemented. The need to meet certain requirements in terms of coding standardization, data availability and information technology made implementation difficult. Private sector providers have not been fully integrated, but most countries have managed to delink hospital financing from public finance budgeting. Conclusion Although more evidence on the impact of DRG-based payment systems is needed, our findings suggest that (i) the greater portion of health-care financing should be public rather than private; (ii) it is advisable to pilot systems first and to establish expenditure ceilings; (iii) countries that import an existing variant of a DRG-based system should be mindful of the need for adaptation; and (iv) countries should promote the cooperation of providers for appropriate data generation and claims management. PMID:24115798

  18. Diagnosis and Treatment of Lymphedema Following Breast Cancer: A Population-based Study

    PubMed Central

    Sayko, Oksana; Pezzin, Liliana E.; Yen, Tina W.F.; Nattinger, Ann B.

    2013-01-01

    Objective To examine factors associated with variations in diagnosis and rehabilitation treatments received by women with self-reported lymphedema secondary to breast cancer care. Design Population-based, prospective study. Setting California, Florida, Illinois, New York. Participants Elderly (65+) women identified from Medicare claims as having had an incident breast cancer surgery in 2003. Interventions N.A. Main Outcome Measures Self-reported incidence of lymphedema symptoms, formal lymphedema diagnosis; treatments for lymphedema. Results Of the 450 breast cancer survivors with lymphedema who participated in the study, 290 (64.4%) were formally diagnosed with the condition by a physician. An additional 160 (35.6%) reported symptoms consistent with lymphedema (arm swelling on the side of surgery that is absent on the contralateral arm) but were not formally diagnosed. Of those reporting as being diagnosed by a physician, 39 (13.4%) received the comprehensive decongestive therapy (CDT) that included multiple components of treatment (manual lymphatic drainage (MLD), bandaging with short stretch bandages, using compression sleeves, skin care and remedial exercises), 24 (8.3%) received MLD only, 162 (55.9%) used bandages, compression garments or a pneumatic pump only, 8 (2.8%) relied solely on skin care or exercise to relieve symptoms and 65 (22.4%) received no treatment at all. Multivariate regressions revealed that race (African American), lower income, and lower levels of social support increased a woman’s probability of having undiagnosed lymphedema. Even when formally diagnosed, African American women were more likely to receive no treatment or to be treated with bandages/compression only, rather than to receive the multi-modality, comprehensive decongestive therapy. Conclusions Lymphedema is a disabling chronic condition related to breast cancer treatment. Our results suggest that a substantial proportion of those reporting symptoms were not formally

  19. Diagnosis and mortality in prehospital emergency patients transported to hospital: a population-based and registry-based cohort study

    PubMed Central

    Christensen, Erika Frischknecht; Larsen, Thomas Mulvad; Jensen, Flemming Bøgh; Bendtsen, Mette Dahl; Hansen, Poul Anders; Johnsen, Søren Paaske; Christiansen, Christian Fynbo

    2016-01-01

    Objective Knowledge about patients after calling for an ambulance is limited to subgroups, such as patients with cardiac arrest, myocardial infarction, trauma and stroke, while population-based studies including all diagnoses are few. We examined the diagnostic pattern and mortality among all patients brought to hospital by ambulance after emergency calls. Design Registry-based cohort study. Setting and participants We included patients brought to hospital in an ambulance dispatched after emergency calls during 2007–2014 in the North Denmark Region (580 000 inhabitants). We reported hospital diagnosis according to the chapters of the International Classification of Diseases, 10th Edition (ICD-10), and studied death on days 1 and 30 after the call. Cohort characteristics and diagnoses were described, and the Kaplan-Meier method was used to estimate mortality and 95% CIs. Results In total, 148 757 patients were included, mean age 52.9 (SD 24.3) years. The most frequent ICD-10 diagnosis chapters were: ‘injury and poisoning’ (30.0%), and the 2 non-specific diagnosis chapters: ‘symptoms and abnormal findings, not elsewhere classified’ (17.5%) and ‘factors influencing health status and contact with health services’ (14.1%), followed by ‘diseases of the circulatory system’ (10.6%) and ‘diseases of the respiratory system’ (6.7%). The overall 1-day mortality was 1.8% (CI 1.7% to 1.8%) and 30-day mortality 4.7% (CI 4.6% to 4.8%). ‘Diseases of the circulatory system’ had the highest 1-day mortality of 7.7% (CI 7.3% to 8.1%) accounting for 1209 deaths. After 30 days, the highest number of deaths were among circulatory diseases (2313), respiratory diseases (1148), ‘symptoms and abnormal findings, not elsewhere classified’ (1119) and ‘injury and poisoning’ (741), and 30 days mortality in percentage was 14.7%, 11.6%, 4.3% and 1.7%, respectively. Conclusions Patients' diagnoses from hospital stay after calling 1-1-2 in this population-based

  20. Cell-based polymerase chain reaction for canine transmissible venereal tumor (CTVT) diagnosis

    PubMed Central

    SETTHAWONGSIN, Chanokchon; TECHANGAMSUWAN, Somporn; TANGKAWATTANA, Sirikachorn; RUNGSIPIPAT, Anudep

    2016-01-01

    Canine transmissible venereal tumor (CTVT) is the only naturally contagious tumor that is transmitted during coitus or social behaviors. Based on the tumor’s location, the diagnosis of genital TVT (GTVT) is comparably easier than those in the extragenital area (ETVT) that are more easily incorrectly diagnosed. Fortunately, CTVT cells contain a specific long interspersed nuclear elements (LINE), inserted upstream of the myc gene, allowing a diagnostic polymerase chain reaction (PCR) based detection assay. The objectives of this study were aimed to improve the diagnostic accuracy by applying the diagnostic LINE1-c-myc PCR assay and fine needle aspiration (FNA) collection in direct comparison with standard cytological and histopathological analyses. Seventy-four dogs, comprised of 41 and 31 dogs with tumor masses at their external genitalia and extragenital areas (e.g. skin and nasal cavity), respectively, were included in this study. The signalment of these 65 dogs and clinical history of 20 client-owned dogs were collected. Samples were taken by biopsy for both histopathological examination and FNA for cytological examination and diagnostic PCR. The PCR products from 10 apparently CTVT samples were purified and sequenced. Sixty-one CTVT cases were diagnosed by cytological and histological analyses, but 65 were positive by the PCR assay. Overall, the PCR assay improved the accuracy of diagnostic CTVT results, especially for the more difficult ETVT tumors. Moreover, this PCR-based approach can facilitate the decision as to discontinue chemotherapy by discrimination between residual tumor cell masses and fibrotic tissue. PMID:27075116

  1. Evidence-based diagnosis, health care, and rehabilitation for children with cerebral palsy.

    PubMed

    Novak, Iona

    2014-08-01

    Safer and more effective interventions have been invented for children with cerebral palsy, but the rapid expansion of the evidence base has made keeping up-to-date difficult. Unfortunately, outdated care is being provided. The aims were to survey the questions parents asked neurologists and provide evidence-based answers, using knowledge translation techniques. Parents asked the following questions: (1) what's wrong with my baby? An algorithm for early diagnosis was proposed. (2) What is cerebral palsy and what online resources are current? Reputable information websites were sourced and hyperlinks provided. (3) The prognosis? Prognostic data from meta-analyses were summarized in an infographic. (4) What interventions offer the most evidence-supported results? Systematic review data about the most effective interventions was mapped into a bubble chart infographic. Finally, (5) What can we expect? Predictors and facilitators of good outcomes were summarized. This article provides an overview of the most up-to-date diagnostic practices and evidence-based intervention options. PMID:24958005

  2. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

    2016-03-01

    As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.

  3. Ultrasound-based elastography for the diagnosis of portal hypertension in cirrhotics

    PubMed Central

    Şirli, Roxana; Sporea, Ioan; Popescu, Alina; Dănilă, Mirela

    2015-01-01

    Progressive fibrosis is encountered in almost all chronic liver diseases. Its clinical signs are diagnostic in advanced cirrhosis, but compensated liver cirrhosis is harder to diagnose. Liver biopsy is still considered the reference method for staging the severity of fibrosis, but due to its drawbacks (inter and intra-observer variability, sampling errors, unequal distribution of fibrosis in the liver, and risk of complications and even death), non-invasive methods were developed to assess fibrosis (serologic and elastographic). Elastographic methods can be ultrasound-based or magnetic resonance imaging-based. All ultrasound-based elastographic methods are valuable for the early diagnosis of cirrhosis, especially transient elastography (TE) and acoustic radiation force impulse (ARFI) elastography, which have similar sensitivities and specificities, although ARFI has better feasibility. TE is a promising method for predicting portal hypertension in cirrhotic patients, but it cannot replace upper digestive endoscopy. The diagnostic accuracy of using ARFI in the liver to predict portal hypertension in cirrhotic patients is debatable, with controversial results in published studies. The accuracy of ARFI elastography may be significantly increased if spleen stiffness is assessed, either alone or in combination with liver stiffness and other parameters. Two-dimensional shear-wave elastography, the ElastPQ technique and strain elastography all need to be evaluated as predictors of portal hypertension. PMID:26556985

  4. Cell-based polymerase chain reaction for canine transmissible venereal tumor (CTVT) diagnosis.

    PubMed

    Setthawongsin, Chanokchon; Techangamsuwan, Somporn; Tangkawattana, Sirikachorn; Rungsipipat, Anudep

    2016-08-01

    Canine transmissible venereal tumor (CTVT) is the only naturally contagious tumor that is transmitted during coitus or social behaviors. Based on the tumor's location, the diagnosis of genital TVT (GTVT) is comparably easier than those in the extragenital area (ETVT) that are more easily incorrectly diagnosed. Fortunately, CTVT cells contain a specific long interspersed nuclear elements (LINE), inserted upstream of the myc gene, allowing a diagnostic polymerase chain reaction (PCR) based detection assay. The objectives of this study were aimed to improve the diagnostic accuracy by applying the diagnostic LINE1-c-myc PCR assay and fine needle aspiration (FNA) collection in direct comparison with standard cytological and histopathological analyses. Seventy-four dogs, comprised of 41 and 31 dogs with tumor masses at their external genitalia and extragenital areas (e.g. skin and nasal cavity), respectively, were included in this study. The signalment of these 65 dogs and clinical history of 20 client-owned dogs were collected. Samples were taken by biopsy for both histopathological examination and FNA for cytological examination and diagnostic PCR. The PCR products from 10 apparently CTVT samples were purified and sequenced. Sixty-one CTVT cases were diagnosed by cytological and histological analyses, but 65 were positive by the PCR assay. Overall, the PCR assay improved the accuracy of diagnostic CTVT results, especially for the more difficult ETVT tumors. Moreover, this PCR-based approach can facilitate the decision as to discontinue chemotherapy by discrimination between residual tumor cell masses and fibrotic tissue. PMID:27075116

  5. Development of A MEMS Based Manometric Catheter for Diagnosis of Functional Swallowing Disorders.

    NASA Astrophysics Data System (ADS)

    Hsu, H. Y.; Hariz, A. J.; Omari, T.; Teng, M. F.; Sii, D.; Chan, S.; Lau, L.; Tan, S.; Lin, G.; Haskard, M.; Mulcahy, D.; Bakewell, M.

    2006-04-01

    Silicon pressure sensors based on micro-electro-mechanical-systems (MEMS) technologies are gaining popularity for applications in bio-medical devices. In this study, a silicon piezo-resistive pressure sensor die is used in a feasibility study of developing a manometric catheter for functional swallowing disorders diagnosis. The function of a manometric catheter is to measure the peak and intrabolus pressures along the esophageal segment during the swallowing action. Previous manometric catheters used the water perfusion technique to measure the pressure changes. This type of catheter is reusable, large in size and the pressure reading is recorded by an external transducer. Current manometric catheters use a solid state pressure sensor on the catheter itself to measure the pressure changes. This type of catheter reduces the discomfort to the patient but it is reusable and is very expensive. We carried out several studies and experiments on the MEMS-based pressure sensor die, and the results show the MEMS-based pressure sensors have a good stability and a good linearity output response, together with the advantage of low excitation biasing voltage and extremely small size. The MEMS-based sensor is the best device to use in the new generation of manometric catheters. The concept of the new MEMS-based manometric catheter consists of a pressure sensing sensor, supporting ring, the catheter tube and a data connector. Laboratory testing shows that the new calibrated catheter is capable of measuring pressure in the range from 0 to 100mmHg and maintaining stable condition on the zero baseline setting when no pressure is applied. In-vivo tests are carried out to compare the new MEMS based catheter with the current version of catheters used in the hospital.

  6. Teaching Musculoskeletal Physical Diagnosis Using A Web-based Tutorial and Pathophysiology-Focused Cases

    PubMed Central

    Modica, Renee F; Thundiyil, Josef G; Chou, Calvin; Diab, Mohammad; Von Scheven, Emily

    2009-01-01

    Objective: To assess the effectiveness of an experimental curriculum on teaching first-year medical students the musculoskeletal exam as compared to a traditional curriculum. Background: Musculoskeletal complaints are common in the primary care setting. Practitioners are often deficient in examination skills and knowledge regarding musculoskeletal diseases. There is a lack of uniformity regarding how to teach the musculoskeletal examination among sub-specialists. We propose a novel web-based approach to teaching the musculoskeletal exam that is enhanced by peer practice with pathophysiology-focused cases. We sought to assess the effectiveness of an innovative musculoskeletal curriculum on the knowledge and skills of first-year medical students related to musculoskeletal physical diagnosis as compared to a traditional curriculum. The secondary purpose of this study was to assess satisfaction of students and preceptors exposed to this teaching method. Methods: This quasi-experimental study was conducted at a single LCME-accredited medical school and included a convenience sample from 2 consecutive classes of medical students during the musculoskeletal portion of their physical diagnosis class. We conducted a needs assessment of the traditional curriculum used to teach musculoskeletal examination. The needs assessment informed the development of an experimental curriculum. One class (control group) received the traditional curriculum while the second class (experimental group) received the experimental curriculum, consisting of a web-based musculoskeletal tutorial, pathophysiology-focused cases, and facilitator preparation. We used multiple-choice questions and musculoskeletal OSCE scores to assess differences between knowledge and skills in the 2 groups. Results: The sample consisted of 140 students in each medical school class. There were no statistically significant differences between the 2 groups. One hundred seven students from the control group and 120 students

  7. Role of Liquid-based Cytology and Cell Block in the Diagnosis of Endometrial Lesions

    PubMed Central

    Zhang, Hui; Wen, Jia; Xu, Pi-Li; Chen, Rui; Yang, Xi; Zhou, Lian-Er; Jiang, Ping; Wan, An-Xia; Liao, Qin-Ping

    2016-01-01

    Background: Liquid-based cytology (LBC) offers an alternative method to biopsy in screening endometrial cancer. Cell block (CB), prepared by collecting residual cytological specimen, represents a novel method to supplement the diagnosis of endometrial cytology. This study aimed to compare the specimen adequacy and diagnostic accuracy of LBC and CB in the diagnosis of endometrial lesions. Methods: A total of 198 women with high risks of endometrial carcinoma (EC) from May 2014 to April 2015 were enrolled in this study. The cytological specimens were collected by the endometrial sampler (SAP-1) followed by histopathologic evaluation of dilatation and curettage or biopsy guided by hysteroscopy. The residual cytological specimens were processed into paraffin-embedded CB after LBC preparation. Diagnostic accuracies of LBC and CB for detecting endometrial lesions were correlated with histological diagnoses. Chi-square test was used to compare the specimen adequacies of LBC and CB. Results: The specimen inadequate rate of CB was significantly higher than that of LBC (22.2% versus 7.1%, P < 0.01). There were 144 cases with adequate specimens for LBC and CB preparation. Among them, 29 cases were atypical endometrial hyperplasia (11 cases) or carcinoma (18 cases) confirmed by histology evaluation. Taking atypical hyperplasia and carcinoma as positive, the diagnostic accuracy of CB was 95.1% while it was 93.8% in LBC. When combined LBC with CB, the diagnostic accuracy was improved to 95.8%, with a sensitivity of 89.7% and specificity of 97.4%. Conclusions: CB is a feasible and reproducible adjuvant method for screening endometrial lesions. A combination of CB and LBC can improve the diagnostic accuracy of endometrial lesions. PMID:27270542

  8. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images

    PubMed Central

    Ozkan, Murat; Cakiroglu, Murat; Kocaman, Orhan; Kurt, Mevlut; Yilmaz, Bulent; Can, Guray; Korkmaz, Ugur; Dandil, Emre; Eksi, Ziya

    2016-01-01

    Aim: The aim was to develop a high-performance computer-aided diagnosis (CAD) system with image processing and pattern recognition in diagnosing pancreatic cancer by using endosonography images. Materials and Methods: On the images, regions of interest (ROI) of three groups of patients (<40, 40-60 and >60) were extracted by experts; features were obtained from images using three different techniques and were trained separately for each age group with an Artificial Neural Network (ANN) to diagnose cancer. The study was conducted on endosonography images of 202 patients with pancreatic cancer and 130 noncancer patients. Results: 122 features were identified from the 332 endosonography images obtained in the study, and the 20 most appropriate features were selected by using the relief method. Images classified under three age groups (in years; <40, 40-60 and >60) were tested via 200 random tests and the following ratios were obtained in the classification: accuracy: 92%, 88.5%, and 91.7%, respectively; sensitivity: 87.5%, 85.7%, and 93.3%, respectively; and specificity: 94.1%, 91.7%, and 88.9%, respectively. When all the age groups were assessed together, the following values were obtained: accuracy: 87.5%, sensitivity: 83.3%, and specificity: 93.3%. Conclusions: It was observed that the CAD system developed in the study performed better in diagnosing pancreatic cancer images based on classification by patient age compared to diagnosis without classification. Therefore, it is imperative to take patient age into consideration to ensure higher performance. PMID:27080608

  9. Diagnosis of cervical cells based on fractal and Euclidian geometrical measurements: Intrinsic Geometric Cellular Organization

    PubMed Central

    2014-01-01

    Background Fractal geometry has been the basis for the development of a diagnosis of preneoplastic and neoplastic cells that clears up the undetermination of the atypical squamous cells of undetermined significance (ASCUS). Methods Pictures of 40 cervix cytology samples diagnosed with conventional parameters were taken. A blind study was developed in which the clinic diagnosis of 10 normal cells, 10 ASCUS, 10 L-SIL and 10 H-SIL was masked. Cellular nucleus and cytoplasm were evaluated in the generalized Box-Counting space, calculating the fractal dimension and number of spaces occupied by the frontier of each object. Further, number of pixels occupied by surface of each object was calculated. Later, the mathematical features of the measures were studied to establish differences or equalities useful for diagnostic application. Finally, the sensibility, specificity, negative likelihood ratio and diagnostic concordance with Kappa coefficient were calculated. Results Simultaneous measures of the nuclear surface and the subtraction between the boundaries of cytoplasm and nucleus, lead to differentiate normality, L-SIL and H-SIL. Normality shows values less than or equal to 735 in nucleus surface and values greater or equal to 161 in cytoplasm-nucleus subtraction. L-SIL cells exhibit a nucleus surface with values greater than or equal to 972 and a subtraction between nucleus-cytoplasm higher to 130. L-SIL cells show cytoplasm-nucleus values less than 120. The rank between 120–130 in cytoplasm-nucleus subtraction corresponds to evolution between L-SIL and H-SIL. Sensibility and specificity values were 100%, the negative likelihood ratio was zero and Kappa coefficient was equal to 1. Conclusions A new diagnostic methodology of clinic applicability was developed based on fractal and euclidean geometry, which is useful for evaluation of cervix cytology. PMID:24742118

  10. DIABETES, OBESITY AND DIAGNOSIS OF AMYOTROPHIC LATERAL SCLEROSIS: A POPULATION-BASED STUDY

    PubMed Central

    Kioumourtzoglou, Marianthi-Anna; Rotem, Ran S.; Seals, Ryan M.; Gredal, Ole; Hansen, Johnni; Weisskopf, Marc G.

    2016-01-01

    Importance Although prior studies have suggested a role of cardiometabolic health on pathogenesis of amyotrophic lateral sclerosis (ALS), the association with diabetes has not been widely examined. Objective Amyotrophic lateral sclerosis is the most common motor neuron disorder. Several vascular risk factors have been associated with decreased risk for ALS. Although diabetes is also a risk factor for vascular disease, the few studies of diabetes and ALS have been inconsistent. We examined the association between diabetes and obesity, each identified through ICD-8 or 10 codes in a hospital registry, and ALS using data from the Danish National Registers. Design and Setting Population-based nested case-control study. Participants 3,650 Danish residents diagnosed with ALS between 1982 and 2009, and 365,000 controls (100 for each ALS case), matched on age and sex. Main Outcome Measure Adjusted odds ratio (OR) for ALS associated with diabetes or obesity diagnoses at least three years prior to the ALS diagnosis date. Results When considering diabetes and our obesity indicator together, the estimated OR for ALS was 0.61 (95%CI: 0.46–0.80) for diabetes and 0.81 (95%CI: 0.57–1.16) for obesity. We observed no effect modification on the association with diabetes by gender, but a significant modification by age at first diabetes or age at ALS, with the protective association stronger with increasing age, consistent with different associations by diabetes type. Conclusions and Relevance We conducted a nationwide study to investigate the association between diabetes and ALS diagnosis. Our findings are in agreement with previous reports of a protective association between vascular risk factors and ALS, and suggest type 2 diabetes, but not type 1, is protective for ALS. PMID:26030836

  11. Internet-Based Medical Visit and Diagnosis for Common Medical Problems: Experience of First User Cohort

    PubMed Central

    Shevchik, Grant J.; Paone, Suzanne; Martich, G. Daniel

    2011-01-01

    Abstract Objective Internet-based medical visits, or “structured e-Visits,” allow patients to report symptoms and seek diagnosis and treatment from their doctor over a secure Web site, without calling or visiting the physician's office. While acceptability of e-Visits has been investigated, outcomes associated with e-Visits, that is, whether patients receiving diagnoses receive appropriate care or need to return to the doctor, remain unexplored. Materials and Methods: The first 156 e-Visit users from a large family medicine practice were surveyed regarding their experience with the e-Visit and e-Visit outcomes. In addition, medical records for patients making e-Visits were reviewed to examine need for follow-up care within 7 days. Results: Interviews were completed with 121 patients (77.6% participation). The most common type of e-Visit was for “other” symptoms or concerns (37%), followed by sinus/cold symptoms (35%). Back pain, urinary symptoms, cough, diarrhea, conjunctivitis, and vaginal irritation were each less frequent (<10%). A majority, 61% completed e-Visits with their own physician. The majority of patients (57.0%) reported receipt of a diagnosis without need for follow-up beyond a prescription; 75% of patients thought the e-Visit was as good as or better than an in-person visit, and only 11.6% felt that their concerns or questions were incompletely addressed. In a review of medical records, 16.9% had a follow-up visit within 7 days, mostly for the same condition. Four of these were on the same day as the e-Visit, including one emergency department visit. Conclusions: Outcomes for the e-Visit suggest that it is an appropriate and potentially cost-saving addition to in-person delivery of primary care. PMID:21457013

  12. Multi-Atlas Based Representations for Alzheimer’s Disease Diagnosis

    PubMed Central

    Min, Rui; Wu, Guorong; Cheng, Jian; Wang, Qian; Shen, Dinggang

    2014-01-01

    Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods. PMID:24753060

  13. Development of an Anti-Elicitin Antibody-Based Immunohistochemical Assay for Diagnosis of Pythiosis.

    PubMed

    Inkomlue, Ruchuros; Larbcharoensub, Noppadol; Karnsombut, Patcharee; Lerksuthirat, Tassanee; Aroonroch, Rangsima; Lohnoo, Tassanee; Yingyong, Wanta; Santanirand, Pitak; Sansopha, Lalana; Krajaejun, Theerapong

    2016-01-01

    Pythiosis is an emerging and life-threatening infectious disease of humans and animals living in tropical and subtropical countries and is caused by the fungus-like organism Pythium insidiosum. Antifungals are ineffective against this pathogen. Most patients undergo surgical removal of the infected organ, and many die from advanced infections. Early and accurate diagnosis leads to prompt management and promotes better prognosis for affected patients. Immunohistochemical assays (IHCs) have been developed using rabbit antibodies raised against P. insidiosum crude extract, i.e., culture filtrate antigen (CFA), for the histodiagnosis of pythiosis, but cross-reactivity with pathogenic fungi compromises the diagnostic performance of the IHC. Therefore, there is a need to improve detection specificity. Recently, the elicitin protein, ELI025, was identified in P. insidiosum, but it was not identified in other human pathogens, including true fungi. The ELI025-encoding gene was successfully cloned and expressed as a recombinant protein in Escherichia coli. This study aims to develop a new IHC using the rabbit anti-ELI025 antibody (anti-ELI) and to compare its performance with the previously reported anti-CFA-based IHC. Thirty-eight P. insidiosum histological sections stained positive by anti-ELI-based and anti-CFA-based IHCs indicating 100% detection sensitivity for the two assays. The anti-ELI antibody stained negative for all 49 negative-control sections indicating 100% detection specificity. In contrast, the anti-CFA antibody stained positive for one of the 49 negative controls (a slide prepared from Fusarium-infected tissue) indicating 98% detection specificity. In conclusion, the anti-ELI based IHC is sensitive and specific for the histodiagnosis of pythiosis and is an improvement over the anti-CFA-based assay. PMID:26719582

  14. Development of an Anti-Elicitin Antibody-Based Immunohistochemical Assay for Diagnosis of Pythiosis

    PubMed Central

    Inkomlue, Ruchuros; Larbcharoensub, Noppadol; Karnsombut, Patcharee; Lerksuthirat, Tassanee; Aroonroch, Rangsima; Lohnoo, Tassanee; Yingyong, Wanta; Santanirand, Pitak; Sansopha, Lalana

    2015-01-01

    Pythiosis is an emerging and life-threatening infectious disease of humans and animals living in tropical and subtropical countries and is caused by the fungus-like organism Pythium insidiosum. Antifungals are ineffective against this pathogen. Most patients undergo surgical removal of the infected organ, and many die from advanced infections. Early and accurate diagnosis leads to prompt management and promotes better prognosis for affected patients. Immunohistochemical assays (IHCs) have been developed using rabbit antibodies raised against P. insidiosum crude extract, i.e., culture filtrate antigen (CFA), for the histodiagnosis of pythiosis, but cross-reactivity with pathogenic fungi compromises the diagnostic performance of the IHC. Therefore, there is a need to improve detection specificity. Recently, the elicitin protein, ELI025, was identified in P. insidiosum, but it was not identified in other human pathogens, including true fungi. The ELI025-encoding gene was successfully cloned and expressed as a recombinant protein in Escherichia coli. This study aims to develop a new IHC using the rabbit anti-ELI025 antibody (anti-ELI) and to compare its performance with the previously reported anti-CFA-based IHC. Thirty-eight P. insidiosum histological sections stained positive by anti-ELI-based and anti-CFA-based IHCs indicating 100% detection sensitivity for the two assays. The anti-ELI antibody stained negative for all 49 negative-control sections indicating 100% detection specificity. In contrast, the anti-CFA antibody stained positive for one of the 49 negative controls (a slide prepared from Fusarium-infected tissue) indicating 98% detection specificity. In conclusion, the anti-ELI based IHC is sensitive and specific for the histodiagnosis of pythiosis and is an improvement over the anti-CFA-based assay. PMID:26719582

  15. PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELING

    EPA Science Inventory

    Physiologically-based pharmacokinetic (PB-PK) models attempt to provide both a realistic anatomic description of the animal to which a drug or toxic chemical has been administered and a biologically accurate representation of the physiological pathways for chemical storage, metab...

  16. A ROC-based feature selection method for computer-aided detection and diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

    2014-03-01

    Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

  17. Do diagnosis-related group-based payments incentivise hospitals to adjust output mix?

    PubMed

    Liang, Li-Lin

    2015-04-01

    This study investigates whether the diagnosis-related group (DRG)-based payment method motivates hospitals to adjust output mix in order to maximise profits. The hypothesis is that when there is an increase in profitability of a DRG, hospitals will increase the proportion of that DRG (own-price effects) and decrease those of other DRGs (cross-price effects), except in cases where there are scope economies in producing two different DRGs. This conjecture is tested in the context of the case payment scheme (CPS) under Taiwan's National Health Insurance programme over the period of July 1999 to December 2004. To tackle endogeneity of DRG profitability and treatment policy, a fixed-effects three-stage least squares method is applied. The results support the hypothesised own-price and cross-price effects, showing that DRGs which share similar resources appear to be complements rather substitutes. For-profit hospitals do not appear to be more responsive to DRG profitability, possibly because of their institutional characteristics and bonds with local communities. The ke