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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. 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.

  5. 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.

  6. Extending model-based diagnosis for analog thermodynamical devices

    NASA Technical Reports Server (NTRS)

    Rouquette, Nicolas; Chien, Steve; Robertson, Charles

    1993-01-01

    The increasing complexity of process control applications have posed difficult problems in fault detection, isolation, and recovery. Deep knowledge-based approaches, such as model-based diagnosis, have offered some promise in addressing these problems. However, the difficulties of adapting these techniques to situations involving numerical reasoning and noise have limited the applicability of these techniques. This paper describes an extension of classical model-based diagnosis techniques to deal with sparse data, noise, and complex noninvertible numerical models. These diagnosis techniques are being applied to the External Active Thermal Control System for Space Station Freedom.

  7. 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…

  8. 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.

  9. Model-based fault diagnosis in continuous dynamic systems.

    PubMed

    Lo, C H; Wong, Y K; Rad, A B

    2004-07-01

    Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain. In this paper, qualitative bond graph (QBG) reasoning is adopted as the modeling scheme to generate a set of qualitative equations. The QBG method provides a unified approach for modeling engineering systems, in particular, mechatronic systems. An input-output qualitative equation derived from QBG formalism performs continuous system monitoring. Fault diagnosis is activated when a discrepancy is observed between measured abnormal behavior and predicted system behavior. Genetic algorithms (GA's) are then used to search for possible faulty components among a system of qualitative equations. In order to demonstrate the performance of the proposed algorithm, we have tested it on a laboratory scale servo-tank liquid process rig. Results of the proposed model-based fault detection and diagnosis algorithm for the process rig are presented and discussed.

  10. Model-based fault detection and diagnosis in ALMA subsystems

    NASA Astrophysics Data System (ADS)

    Ortiz, José; Carrasco, Rodrigo A.

    2016-07-01

    The Atacama Large Millimeter/submillimeter Array (ALMA) observatory, with its 66 individual telescopes and other central equipment, generates a massive set of monitoring data every day, collecting information on the performance of a variety of critical and complex electrical, electronic and mechanical components. This data is crucial for most troubleshooting efforts performed by engineering teams. More than 5 years of accumulated data and expertise allow for a more systematic approach to fault detection and diagnosis. This paper presents model-based fault detection and diagnosis techniques to support corrective and predictive maintenance in a 24/7 minimum-downtime observatory.

  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

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

    2016-01-18

    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.

  16. 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

  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. Fault detection and diagnosis based on modeling and estimation methods.

    PubMed

    Huang, Sunan; Tan, Kok Kiong

    2009-05-01

    This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme.

  19. FY90 Based Cost Models to Support Diagnosis Related Management

    DTIC Science & Technology

    1992-08-04

    models presented here were based upon the final model formhs selected for use with the FY88 data. With the exception of identifying outlying fa- cilities...CENTER EXPENSE MODELS ARMY ALV SASER IC NONCLINICIAN EXPENSES 2 1 4 7 CLINICIAN EXPENSES 6 4 6 16 AMBULATORY EXPENSES 0 2 4 6 TOTAL EXPENSES* 1 0 2 3

  20. 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.

  1. Model-Based Fault Diagnosis in Electric Drives Using Machine Learning

    DTIC Science & Technology

    2005-08-09

    1 Model-Based Fault Diagnosis in Electric Drives Using Machine Learning Yi L. Murphey, Senior Member, IEEE, M. Abul Masrur, Senior...detection and diagnosis system for electric motors [19]. Their system used a transient empirical predictor modeled by a dynamic recurrent neural...networks and wavelet packet decomposition. Their diagnosis system was tested on a 373- kW and a 597-kW induction motor, and its diagnostics accuracy

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis.

    PubMed

    Huang, Sijia; Chong, Nicole; Lewis, Nathan E; Jia, Wei; Xie, Guoxiang; Garmire, Lana X

    2016-03-31

    More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies. We propose that higher-order functional representation of metabolomics data, such as pathway-based metabolomic features, can be used as robust biomarkers for breast cancer. Towards this, we have developed a new computational method that uses personalized pathway dysregulation scores for disease diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under the Curve, a receiver operating characteristic curve) of 0.968 and 0.934, sensitivities of 0.946 and 0.954, and specificities of 0.934 and 0.918. These two metabolomics-based pathway models are further validated by RNA-Seq-based TCGA (The Cancer Genome Atlas) breast cancer data, with AUCs of 0.995 and 0.993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. We have successfully developed a new type of pathway-based model to study metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease diagnosis.

  9. 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.

  10. 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

  11. 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.

  12. 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…

  13. 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.

  14. Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.

    PubMed

    Oliveira, Roberta B; Pereira, Aledir S; Tavares, João Manuel R S

    2017-10-01

    The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. 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.

  16. Observer based on-line fault diagnosis of continuous systems modeled as Petri nets.

    PubMed

    Renganathan, K; Bhaskar, Vidhyacharan

    2010-10-01

    This paper describes a technique for achieving on-line fault diagnosis in continuous systems that are modeled using Petri nets. The effect of place markings and transition markings are considered and based on the computed error between the initial marking and subsequent markings evolved in time, the faults are categorized assuming that the markings are both observable and unobservable. An algorithm has been suitably proposed for achieving detection of faults for a typical continuous three tank system along with suitable results.

  17. 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.

  18. Model-based diagnosis and prognosis of a water recycling system

    NASA Astrophysics Data System (ADS)

    Roychoudhury, I.; Hafiychuk, V.; Goebel, K.

    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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. Model-based cardiovascular disease diagnosis: a preliminary in-silico study.

    PubMed

    Ebrahimi Nejad, Shiva; Carey, Jason P; McMurtry, M Sean; Hahn, Jin-Oh

    2017-04-01

    In this study, we developed and examined the feasibility of a model-based system identification approach to cardiovascular disease diagnosis. The basic premise of the approach is that it may be possible to diagnose cardiovascular disease from disease-induced alterations in the arterial mechanical properties manifested in the proximal and distal arterial blood pressure waveforms. It first individualizes the lumped-parameter model of wave propagation and reflection in the artery using the measurement of proximal and distal arterial blood pressure waveforms. Then, it employs a diagnosis logic, in the form of disease-specific patterns in model parameters, referred as [Formula: see text] and pulse transit time. The longitudinal change in these parameters is used to diagnose the presence of peripheral artery disease and arterial stiffening. We illustrated the feasibility of the proposed approach by testing it in a full-scale in-silico arterial tree simulation. The results showed that the approach exhibited superior sensitivity to ankle-brachial index and convenience to carotid-femoral pulse wave velocity: The model parameters [Formula: see text] and [Formula: see text] responded with up to 100 and 40 % changes to peripheral artery disease with up to 50 % arterial blockage whereas the change in ankle-brachial index was [Formula: see text]; the same parameters responded with up to 300 and 40 % changes to up to 100 % arterial stiffening while pulse transit time changed by up to 24 %. Together with the development of more convenient techniques for the measurement of arterial blood pressure waveforms, the proposed approach may evolve into a viable alternative to the state-of-the-art techniques for cardiovascular disease diagnosis.

  5. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Shibin; Huang, Weiguo; Zhu, Z. K.

    2011-05-01

    At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. The transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time index) of the transient, and the time interval, i.e. period, between transients. Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection. With the proposed method, both parameters of a single transient and the period between transients can be identified from the vibration signal, and localized faults can be detected based on the parameters, especially the period. First, a simulation signal is used to test the performance of the proposed method. Then the method is applied to the vibration signals of different types of bearings with localized faults in the outer race, the inner race and the rolling element, respectively, and all the results show that the period between transients, representing the localized fault characteristic, is successfully detected. The method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period. Moreover, it can be drawn that for bearing fault detection, the single-side wavelet model is more suitable than double-side one, while the double-side model for gearbox fault detection. This research proposed an effective method of localized fault detection for rotating machine fault diagnosis through transient modeling and parameter detection.

  6. 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.

  7. 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.

  8. Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yan, Ruqiang

    2016-12-01

    The bearing failure, generating harmful vibrations, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing fault diagnosis is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing fault diagnosis could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, fault information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates fault information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of fault information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing faults), a novel weighted sparse model and its corresponding framework for bearing fault diagnosis is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the fault information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family

  9. 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.

  10. 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

  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. 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.

  13. 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…

  14. 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…

  15. 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.

  16. 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.

  17. 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

  18. 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

    2014-05-05

    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.

  19. 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.

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

    ERIC Educational Resources Information Center

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

    2008-01-01

    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,…

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

    ERIC Educational Resources Information Center

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

    2008-01-01

    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,…

  2. Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model

    PubMed Central

    Lee, So Mi; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

    2015-01-01

    Objective To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). Materials and Methods From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Results Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Conclusion Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology. PMID:26576128

  3. Combining FDI and AI approaches within causal-model-based diagnosis.

    PubMed

    Gentil, Sylviane; Montmain, Jacky; Combastel, Christophe

    2004-10-01

    This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.

  4. Classifier Model Based on Machine Learning Algorithms: Application to Differential Diagnosis of Suspicious Thyroid Nodules via Sonography.

    PubMed

    Wu, Hongxun; Deng, Zhaohong; Zhang, Bingjie; Liu, Qianyun; Chen, Junyong

    2016-06-24

    The purpose of this article is to construct classifier models using machine learning algorithms and to evaluate their diagnostic performances for differentiating malignant from benign thyroid nodules. This study included 970 histopathologically proven thyroid nodules in 970 patients. Two radiologists retrospectively reviewed ultrasound images, and nodules were graded according to a five-tier sonographic scoring system. Statistically significant variables based on an experienced radiologist's observations were obtained with attribute optimization using fivefold cross-validation and applied as the input nodes to build models for predicting malignancy of nodules. The performances of the machine learning algorithms and radiologists were compared using ROC curve analysis. Diagnosis by the experienced radiologist achieved the highest predictive accuracy of 88.66% with a specificity of 85.33%, whereas the radial basis function (RBF)-neural network (NN) achieved the highest sensitivity of 92.31%. The AUC value for diagnosis by the experienced radiologist (AUC = 0.9135) was greater than those for diagnosis by the less experienced radiologist, the naïve Bayes classifier, the support vector machine, and the RBF-NN (AUC = 0.8492, 0.8811, 0.9033, and 0.9103, respectively; p < 0.05). The machine learning algorithms underperformed with respect to the experienced radiologist's readings used to construct them, and the RBF-NN outperformed the other machine learning algorithm models.

  5. Diagnosis of Cognitive Impairment Compatible with Early Diagnosis of Alzheimer's Disease. A Bayesian Network Model based on the Analysis of Oral Definitions of Semantic Categories.

    PubMed

    Guerrero, J M; Martínez-Tomás, R; Rincón, M; Peraita, H

    2016-01-01

    mild and moderate AD patients) from Madrid region's hospitals. Experienced neurologists diagnosed these cases following the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA)'s Alzheimer's criteria, performing, among other explorations and tests, a minimum neuropsychological exploration that included the Mini-Mental State Examination test. BN's classification performance is remarkable compared with other machine learning methods, achieving 91% accuracy and 94% precision in mild and moderate AD patients. Apart from this, the BN model facilitates the explanation of the reasoning process and the validation of the conclusions and allows the study of uncommon declarative semantic memory impairments. Our method is able to analyse LSCD in a wide set of semantic categories throughout the progression of CI, being a valuable first screening method in AD diagnosis in its early stages. Because of its low cost, it can be used for routine clinical evaluations or screenings to detect AD in its early stages. Besides, due to its knowledge-based structure, it can be easily extended to provide an explanation of the diagnosis and to the study of other neurodegenerative diseases. Further, this is a key advantage of BNs over other machine learning methods with similar performance: it is a recognisable and explanatory model that allows one to study irregularities in different semantic categories.

  6. Power distribution system diagnosis with uncertainty information based on rough sets and clouds model

    NASA Astrophysics Data System (ADS)

    Sun, Qiuye; Zhang, Huaguang

    2006-11-01

    During the distribution system fault period, usually the explosive growth signals including fuzziness and randomness are too redundant to make right decision for the dispatcher. The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. So intelligent methods must be developed to aid users in maintaining and using this abundance of information effectively. An important issue in distribution fault diagnosis system (DFDS) is to allow the discovered knowledge to be as close as possible to natural languages to satisfy user needs with tractability, and to offer DFDS robustness. At this junction, the paper describes a systematic approach for detecting superfluous data. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. It is considered as a "white box" rather than a "black box" like in the case of neural network. The cloud theory is introduced and the mathematical description of cloud has effectively integrated the fuzziness and randomness of linguistic terms in a unified way. Based on it, a method of knowledge representation in DFDS is developed which bridges the gap between quantitative knowledge and qualitative knowledge. In relation to classical rough set, the cloud-rough method can deal with the uncertainty of the attribute and make a soft discretization for continuous ones (such as the current and the voltage). A novel approach, including discretization, attribute reduction, rule reliability computation and equipment reliability computation, is presented. The data redundancy is greatly reduced based on an integrated use of cloud theory and rough set theory. Illustrated with a power distribution DFDS shows the effectiveness and practicality of the proposed approach.

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

    DTIC Science & Technology

    2012-09-01

    Orchard & Vachtsevanos, 2009). However, the integration of diag- nosis and prognosis algorithms is seldom studied. In fact, many diagnosis...tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis...or the prognosis task. Some works have proposed the inte- gration of both tasks within a common framework (Patrick et al., 2007; Orchard

  8. Fault diagnosis in gas turbines using a model-based technique

    SciTech Connect

    Merrington, G.L. )

    1994-04-01

    Reliable methods for diagnosing faults and detecting degraded performance in gas turbine engines are continually being sought. In this paper, a model-based technique is applied to the problem of detect in degraded performance in a military turbofan engine from take-off acceleration-type transients. In the past, difficulty has been experienced in isolating the effects of some of the physical processes involved. One such effect is the influence of the bulk metal temperature on the measured engine parameters during large power excursions. It will be shown that the model-based technique provides a simple and convenient way of separating this effect from the faster dynamic components. The important conclusion from this work is at good fault coverage can be gleaned from the resultant pseudo-steady-state gain estimates derived in this way.

  9. Fault diagnosis in gas turbines using a model-based technique

    NASA Astrophysics Data System (ADS)

    Merrington, G. L.

    1994-04-01

    Reliable methods for diagnosing faults and detecting degraded performance in gas turbine engines are continually being sought. In this paper, a model-based technique is applied to the problem of detecting degraded performance in a military turbofan engine from take-off acceleration-type transients. In the past, difficulty has been experienced in isolating the effects of some of the physical processes involved. One such effect is the influence of the bulk metal temperature on the measured engine parameters during large power excursions. It will be shown that the model-based technique provides a simple and convenient way of separating this effect from the faster dynamic components. The important conclusion from this work is that good fault coverage can be gleaned from the resultant pseudo-steady-state gain estimates derived in this way.

  10. 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.

  11. Immunity-Based Diagnosis for a Motherboard

    PubMed Central

    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

  12. 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

  13. 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

  14. Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test

    NASA Astrophysics Data System (ADS)

    Wang, Xiyang; Makis, Viliam

    2009-11-01

    Vibration behavior induced by gear shaft crack is different from that induced by gear tooth crack. Hence, a fault indicator used to detect tooth damage may not be effective for monitoring shaft condition. This paper proposes an autoregressive model-based technique to detect the occurrence and advancement of gear shaft cracks. An autoregressive model is fitted to the time synchronously averaged signal of the gear shaft in its healthy state. The order of the autoregressive model is selected using Akaike information criterion and the coefficient estimates are obtained by solving the Yule-Walker equations with the Levinson-Durbin recursion algorithm. The established autoregressive model is then used as a linear prediction filter to process the future signal. The Kolmogorov-Smirnov test is applied on line for the prediction of error signals. The calculated distance is used as a fault indicator and its capability to diagnose shaft crack effectively is demonstrated using a full lifetime gear shaft vibration data history. The other frequently used statistical measures such as kurtosis and variance are also calculated and the results are compared with the Kolmogorov-Smirnov test.

  15. A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea.

    PubMed

    Karamanli, Harun; Yalcinoz, Tankut; Yalcinoz, Mehmet Akif; Yalcinoz, Tuba

    2016-05-01

    Recently, artificial neural networks (ANNs) have been widely applied in science, engineering, and medicine. In the present study, we evaluated the ability of artificial neural networks to be used as a computer program and assistant tool in the diagnosis of obstructive sleep apnea (OSA). Our hypothesis was that ANNs could use clinical information to precisely predict cases of OSA. The study population in this clinical trial consisted of 201 patients with suspected OSA (140 with a positive diagnosis of OSA and 61 with a negative diagnosis of OSA). The artificial neural network was trained by assessing five clinical variables from 201 patients; efficiency was then estimated in this group of 201 patients. The patients were classified using a five-element input vector. ANN classifiers were assessed with the multilayer perceptron (MLP) networks. Use of the MLP classifiers resulted in a diagnostic accuracy of 86.6 %, which in clinical practice is high enough to reduce the number of patients evaluated by polysomnography (PSG), an expensive and limited diagnostic resource. By establishing a pattern that allows the recognition of OSA, ANNs can be used to identify patients requiring PSG.

  16. 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.

  17. 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.

  18. 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.

  19. 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).

  20. 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.

  1. Applying Model-Based Diagnosis to a Rapid Propellant Loading System (Postprint)

    DTIC Science & Technology

    2009-06-01

    exception is significant, diagnose the problem, and decide whether or not to continue the countdown. KATE is a generic software shell for performing...Knowledge Based Expert System for Propellant System Monitoring at the Kennedy Space Center. In Proceedings of the 22nd Space Congress. Cocoa Beach, FL

  2. 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

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

    PubMed

    Nardini, Stefano; Annesi-Maesano, Isabella; Del Donno, Mario; Delucchi, Maurizio; Bettoncelli, Germano; Lamberti, Vincenzo; Patera, Carlo; Polverino, Mario; Russo, Antonio; Santoriello, Carlo; Soverina, Patrizio

    2014-01-01

    to 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.

  4. An intelligent model for liver disease diagnosis.

    PubMed

    Lin, Rong-Ho

    2009-09-01

    Liver disease, the most common disease in Taiwan, is not easily discovered in its initial stage; early diagnosis of this leading cause of mortality is therefore highly important. The design of an effective diagnosis model is therefore an important issue in liver disease treatment. This study accordingly employs classification and regression tree (CART) and case-based reasoning (CBR) techniques to structure an intelligent diagnosis model aiming to provide a comprehensive analytic framework to raise the accuracy of liver disease diagnosis. Based on the advice and assistance of doctors and medical specialists of liver conditions, 510 outpatient visitors using ICD-9 (International Classification of Diseases, 9th Revision) codes at a medical center in Taiwan from 2005 to 2006 were selected as the cases in the data set for liver disease diagnosis. Data on 340 patients was utilized for the development of the model and on 170 patients utilized to perform comparative analysis of the models. This paper accordingly suggests an intelligent model for the diagnosis of liver diseases which integrates CART and CBR. The major steps in applying the model include: (1) adopting CART to diagnose whether a patient suffers from liver disease; (2) for patients diagnosed with liver disease in the first step, employing CBR to diagnose the types of liver diseases. In the first phase, CART is used to extract rules from health examination data to show whether the patient suffers from liver disease. The results indicate that the CART rate of accuracy is 92.94%. In the second phase, CBR is developed to diagnose the type of liver disease, and the new case triggers the CBR system to retrieve the most similar case from the case base in order to support the treatment of liver disease. The new case is supported by a similarity ratio, and the CBR diagnostic accuracy rate is 90.00%. Actual implementation shows that the intelligent diagnosis model is capable of integrating CART and CBR techniques to

  5. 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.

  6. A resampling-based Markovian model for automated colon cancer diagnosis.

    PubMed

    Ozdemir, Erdem; Sokmensuer, Cenk; Gunduz-Demir, Cigdem

    2012-01-01

    In recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image variations. In order to meet this challenge, it is important to learn the systems on a large number of labeled images from a different range of variation. However, acquiring labeled images is quite difficult in this domain, and hence, the labeled training data are typically very limited. Although the issue of having limited labeled data is acknowledged by many researchers, it has rarely been considered in the system design. This paper successfully addresses this issue, introducing a new resampling framework to simulate variations in tissue images. This framework generates multiple sequences from an image for its representation and models them using a Markov process. Working with colon tissue images, our experiments show that this framework increases the generalization capacity of a learner by increasing the size and variation of the training data and improves the classification performance of a given image by combining the decisions obtained on its sequences.

  7. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    NASA Astrophysics Data System (ADS)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  8. 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.

  9. Two-stage damage diagnosis based on the distance between ARMA models and pre-whitening filters

    NASA Astrophysics Data System (ADS)

    Zheng, H.; Mita, A.

    2007-10-01

    This paper presents a two-stage damage diagnosis strategy for damage detection and localization. Auto-regressive moving-average (ARMA) models are fitted to time series of vibration signals recorded by sensors. In the first stage, a novel damage indicator, which is defined as the distance between ARMA models, is applied to damage detection. This stage can determine the existence of damage in the structure. Such an algorithm uses output only and does not require operator intervention. Therefore it can be embedded in the sensor board of a monitoring network. In the second stage, a pre-whitening filter is used to minimize the cross-correlation of multiple excitations. With this technique, the damage indicator can further identify the damage location and severity when the damage has been detected in the first stage. The proposed methodology is tested using simulation and experimental data. The analysis results clearly illustrate the feasibility of the proposed two-stage damage diagnosis methodology.

  10. Foehn diagnosis and model comparison

    NASA Astrophysics Data System (ADS)

    Duerr, B.; Sprenger, M.; Fuhrer, O.; Burri, K.; Gutermann, T.; Hächler, P.; Neururer, A.; Richner, H.; Werner, R.

    2010-09-01

    The «Alpine Research Group Foehn Rhine Valley - Lake Constance (AGF)» (Arbeitsgemeinschaft Föhnforschung Rheintal-Bodensee) is analysing meteorological parameters and investigating foehn phenomena in the Rhine valley since 1971. Their main goal is to find plausible criteria for reliable foehn forecasts and to deepen our understanding of foehn in the target area. The presentation will focus on two main topics: (a) application of a fully automated foehn diagnosis tool; and (b) comparison of model data (COSMO-2, COSMO-7, VERA, INCA) with measurements in our target area. With respect to (a), the foehn diagnosis tool is applied for different meteorological stations of the Swiss meteorological network (SMN). Although it always uses the same six criteria (relative humidity, wind sector, wind force, gale maximum, difference of potential temperature to SMN site Gütsch and wind sector Gütsch), empirical thresholds have to be determined for each site individually. In doing so, foehn can be objectively detected and automatically identified as such in the most cases. In the second part, referring to (b), the foehn case of the 8th December 2006 is considered. It brought high wind velocities as well as a unusually far-reaching foehn, which was observed even north of Lake Constance. The synoptic- to local-scale dynamics of this foehn case is presented in detail, and the further developments within the framework of COSMO-2 to a high spatial resolution of 2.2 km facilitates the comparison of model forecasts with surface measurements. This comparison will be carried out with several parameters such as wind force, potential temperature and air pressure. Furthermore, the forecasted temporal evolution of foehn will be compared to the foehn's beginning and end detected by the foehn diagnosis tool.

  11. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.

    PubMed

    Zhang, B; Liang, X L; Gao, H Y; Ye, L S; Wang, Y G

    2016-05-13

    We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum tumor marker levels in colorectal cancer patients and healthy subjects, early diagnosis models for colorectal cancer were built using three machine learning algorithms to assess their corresponding diagnostic values. Except for serum alpha-fetoprotein, the levels of 11 other serum markers of patients in the colorectal cancer group were higher than those in the benign colorectal cancer group (P < 0.05). The results of logistic regression analysis indicted that individual detection of serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153 and their combined detection was effective for diagnosing colorectal cancer. Combined detection had a better diagnostic effect with a sensitivity of 94.2% and specificity of 97.7%; combining serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153, with the support vector machine diagnosis model and back-propagation, a neural network diagnosis model was built with diagnostic accuracies of 82 and 75%, sensitivities of 85 and 80%, and specificities of 80 and 70%, respectively. Colorectal cancer diagnosis models based on the three machine learning algorithms showed high diagnostic value and can help obtain evidence for the early diagnosis of colorectal cancer.

  12. 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

  13. 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.

  14. 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…

  15. 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.

  16. A new mathematical model based on clinical and laboratory variables for the diagnosis of Sjögren's syndrome.

    PubMed

    Martin-Martin, L S; Latini, A; Pagano, A; Ragno, A; Stasi, R; Coppè, A; Davoli, G; Crescenzi, A; Alimonti, A; Migliore, A

    2003-05-01

    Sjögren's syndrome (SS) is a systemic autoimmune disease that mainly affects exocrine glands. A diagnosis of SS in its early stages has a potential clinical relevance, but it is difficult and cannot be made solely on clinical grounds. Several sets of diagnostic criteria have been proposed, but none has met with a general consensus. Minor salivary gland has been judged to be the "gold standard" for the diagnosis of SS. However, it is a painful procedure and has a small but significant proportion of both false positive and false negative results. The aim of our study was to develop a simple mathematical score that uses clinical and laboratory variables for diagnosing SS, thereby reducing the need of minor salivary gland. The following variables were included in the model: ANA, SS-A/SS-B, Schirmer's Test/BUT, C3/C4, serum gammaglobulin levels. One hundred consecutive individuals reporting clinical syndromes consistent with a sicca syndrome were included in the study. The application of our multifactorial mathematical model has shown a high predictive value for SS vs controls or vs patients with other autoimmune disorders (Sensitivity 93%, Specificity 100%), with an estimated minor salivary gland reduction of 77%. We conclude that our mathematical model can be considered a useful non-invasive approach for diagnosing Sjogren's Syndrome and recommend its validation on a larger scale.

  17. Vibration-Based Damage Diagnosis in a Laboratory Cable-Stayed Bridge Model via an RCP-ARX Model Based Method

    NASA Astrophysics Data System (ADS)

    Michaelides, P. G.; Apostolellis, P. G.; Fassois, S. D.

    2011-07-01

    Vibration-based damage detection and identification in a laboratory cable-stayed bridge model is addressed under inherent, environmental, and experimental uncertainties. The problem is challenging as conventional stochastic methods face difficulties due to uncertainty underestimation. A novel method is formulated based on identified Random Coefficient Pooled ARX (RCP-ARX) representations of the dynamics and statistical hypothesis testing. The method benefits from the ability of RCP models in properly capturing uncertainty. Its effectiveness is demonstrated via a high number of experiments under a variety of damage scenarios.

  18. 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

  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. 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.

  1. 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.

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

    PubMed

    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.

  3. 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…

  4. Damage/fault diagnosis in an operating wind turbine under uncertainty via a vibration response Gaussian mixture random coefficient model based framework

    NASA Astrophysics Data System (ADS)

    Avendaño-Valencia, Luis David; Fassois, Spilios D.

    2017-07-01

    The study focuses on vibration response based health monitoring for an operating wind turbine, which features time-dependent dynamics under environmental and operational uncertainty. A Gaussian Mixture Model Random Coefficient (GMM-RC) model based Structural Health Monitoring framework postulated in a companion paper is adopted and assessed. The assessment is based on vibration response signals obtained from a simulated offshore 5 MW wind turbine. The non-stationarity in the vibration signals originates from the continually evolving, due to blade rotation, inertial properties, as well as the wind characteristics, while uncertainty is introduced by random variations of the wind speed within the range of 10-20 m/s. Monte Carlo simulations are performed using six distinct structural states, including the healthy state and five types of damage/fault in the tower, the blades, and the transmission, with each one of them characterized by four distinct levels. Random vibration response modeling and damage diagnosis are illustrated, along with pertinent comparisons with state-of-the-art diagnosis methods. The results demonstrate consistently good performance of the GMM-RC model based framework, offering significant performance improvements over state-of-the-art methods. Most damage types and levels are shown to be properly diagnosed using a single vibration sensor.

  5. Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study.

    PubMed

    Nalls, Mike A; McLean, Cory Y; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel M; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T Ryan; Nicolas, Aude; Keller, Margaux F; Molony, Cliona; Gibbs, J Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S; Mapstone, Mark; Federoff, Howard J; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie A M; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G; Stone, David J; Ioannidis, John P A; Singleton, Andrew B

    2015-10-01

    Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000

  6. Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives.

    PubMed

    Cordier, Marie-Odile; Dague, Philippe; Lévy, François; Montmain, Jacky; Staroswiecki, Marcel; Travé-Massuyès, Louise

    2004-10-01

    Two distinct and parallel research communities have been working along the lines of the model-based diagnosis approach: the fault detection and isolation (FDI) community and the diagnostic (DX) community that have evolved in the fields of automatic control and artificial intelligence, respectively. This paper clarifies and links the concepts and assumptions that underlie the FDI analytical redundancy approach and the DX consistency-based logical approach. A formal framework is proposed in order to compare the two approaches and the theoretical proof of their equivalence together with the necessary and sufficient conditions is provided.

  7. Theory-Based Diagnosis and Remediation of Writing Disabilities.

    ERIC Educational Resources Information Center

    Berninger, Virginia W.; And Others

    1991-01-01

    Briefly reviews recent trends in research on writing; introduces theory-based model being developed for differential diagnosis of writing disabilities at neuropsychological, linguistic, and cognitive levels; presents cases and patterns in cases that illustrate differential diagnosis of writing disabilities at linguistic level; and suggests…

  8. 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.

  9. A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume.

    PubMed

    Tan, Lirong; Guo, Xinyu; Ren, Sheng; Epstein, Jeff N; Lu, Long J

    2017-01-01

    In this paper, we investigated the problem of computer-aided diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) using machine learning techniques. With the ADHD-200 dataset, we developed a Support Vector Machine (SVM) model to classify ADHD patients from typically developing controls (TDCs), using the regional brain volumes as predictors. Conventionally, the volume of a brain region was considered to be an anatomical feature and quantified using structural magnetic resonance images. One major contribution of the present study was that we had initially proposed to measure the regional brain volumes using fMRI images. Brain volumes measured from fMRI images were denoted as functional volumes, which quantified the volumes of brain regions that were actually functioning during fMRI imaging. We compared the predictive power of functional volumes with that of regional brain volumes measured from anatomical images, which were denoted as anatomical volumes. The former demonstrated higher discriminative power than the latter for the classification of ADHD patients vs. TDCs. Combined with our two-step feature selection approach which integrated prior knowledge with the recursive feature elimination (RFE) algorithm, our SVM classification model combining functional volumes and demographic characteristics achieved a balanced accuracy of 67.7%, which was 16.1% higher than that of a relevant model published previously in the work of Sato et al. Furthermore, our classifier highlighted 10 brain regions that were most discriminative in distinguishing between ADHD patients and TDCs. These 10 regions were mainly located in occipital lobe, cerebellum posterior lobe, parietal lobe, frontal lobe, and temporal lobe. Our present study using functional images will likely provide new perspectives about the brain regions affected by ADHD.

  10. Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: The impact of a local calibration

    PubMed Central

    2010-01-01

    Background In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system. Methods The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters. Results The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data. Conclusion Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs. PMID:20092654

  11. Diagnosis of Parkinson’s disease on the basis of clinical–genetic classification: a population-based modelling study

    PubMed Central

    Nalls, Mike A.; McLean, Cory Y.; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J.; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T. Ryan; Nicolas, Aude; Keller, Margaux F.; Molony, Cliona; Gibbs, J. Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S.; Mapstone, Mark; Federoff, Howard J.; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M.; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G.; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G.; Stone, David J.; Ioannidis, John P. A.; Singleton, Andrew B.

    2015-01-01

    Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model

  12. An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan

    PubMed Central

    2010-01-01

    Background Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme. Methods A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level. Results The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall. Conclusions Given the widespread availability of

  13. Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims.

    PubMed

    Kharrazi, Hadi; Chi, Winnie; Chang, Hsien-Yen; Richards, Thomas M; Gallagher, Jason M; Knudson, Susan M; Weiner, Jonathan P

    2017-08-01

    There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

  14. 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…

  15. 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…

  16. An observer based approach for achieving fault diagnosis and fault tolerant control of systems modeled as hybrid Petri nets.

    PubMed

    Renganathan, K; Bhaskar, VidhyaCharan

    2011-07-01

    In this paper, we propose an approach for achieving detection and identification of faults, and provide fault tolerant control for systems that are modeled using timed hybrid Petri nets. For this purpose, an observer based technique is adopted which is useful in detection of faults, such as sensor faults, actuator faults, signal conditioning faults, etc. The concepts of estimation, reachability and diagnosability have been considered for analyzing faulty behaviors, and based on the detected faults, different schemes are proposed for achieving fault tolerant control using optimization techniques. These concepts are applied to a typical three tank system and numerical results are obtained.

  17. Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: a Monte-Carlo-model-based approach

    PubMed Central

    Zhu, Changfang; Palmer, Gregory M.; Breslin, Tara M.; Harter, Josephine; Ramanujam, Nirmala

    2009-01-01

    We explore the use of Monte-Carlo-model-based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from breast tissues. These models are used to extract the absorption, scattering, and fluorescence properties of malignant and nonmalignant tissues and to diagnose breast cancer based on these intrinsic tissue properties. Absorption and scattering properties, including β-carotene concentration, total hemoglobin concentration, hemoglobin saturation, and the mean reduced scattering coefficient are derived from diffuse reflectance spectra using a previously developed Monte Carlo model of diffuse reflectance. A Monte Carlo model of fluorescence described in an earlier manuscript was employed to retrieve the intrinsic fluorescence spectra. The intrinsic fluorescence spectra were decomposed into several contributing components, which we attribute to endogenous fluorophores that may present in breast tissues including collagen, NADH, and retinol/vitamin A. The model-based approaches removes any dependency on the instrument and probe geometry. The relative fluorescence contributions of individual fluorescing components, as well as β-carotene concentration, hemoglobin saturation, and the mean reduced scattering coefficient display statistically significant differences between malignant and adipose breast tissues. The hemoglobin saturation and the reduced scattering coefficient display statistically significant differences between malignant and fibrous/benign breast tissues. A linear support vector machine classification using (1) fluorescence properties alone, (2) absorption and scattering properties alone, and (3) the combination of all tissue properties achieves comparable classification accuracies of 81 to 84% in sensitivity and 75 to 89% in specificity for discriminating malignant from nonmalignant breast tissues, suggesting each set of tissue properties are diagnostically useful for the discrimination of breast malignancy. PMID

  18. Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

    NASA Astrophysics Data System (ADS)

    Polverino, Pierpaolo; Frisk, Erik; Jung, Daniel; Krysander, Mattias; Pianese, Cesare

    2017-07-01

    The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

  19. A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

    PubMed Central

    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

  20. Image-based micro-finite-element modeling for improved distal radius strength diagnosis: moving from bench to bedside.

    PubMed

    Pistoia, W; van Rietbergen, B; Lochmüller, E-M; Lill, C A; Eckstein, F; Rüegsegger, P

    2004-01-01

    Although osteoporosis is characterized by quantitative (mass) and qualitative (structural) changes, standard clinical techniques (dual-energy X-ray absorptiometry, DXA) only measure the former. Three-dimensional micro-finite-element (micro-FE) models based on high-resolution images can account for structural aspects as well, and it has recently been shown that an improved prediction of distal radius strength is possible with micro-FE analysis. A clinical application of this technique, however, is limited by its high imaging and computational demands. The objective of this study is to investigate if an improved prediction of bone strength can be obtained as well when only a small part of the radius is used for micro-FE modeling. Images of a 1-cm region of the metaphysis of the distal radius of 54 cadaver arms (mean age: 82 +/- 9 SD) made with a three-dimensional peripheral quantitative computed tomography (pQCT) device at 165- micro m resolution formed the basis for micro-FE models that were used to predict the bone failure load. Following imaging, specimens were experimentally compressed to failure to produce a Colles'-type fracture. Failure loads predicted from micro-FE analyses agreed well with those measured experimentally (R2 = 0.66, p < 0.001). Lower correlations were observed with bone mass (R2 = 0.48, p < 0.001) and microstructural parameters (R2 = 0.47, p < 0.001). Hence, even when only a small region is modeled, micro-FE analysis provides an improved prediction of radius strength.

  1. Application of the fault diagnosis strategy based on hierarchical information fusion in motors fault diagnosis

    NASA Astrophysics Data System (ADS)

    Xia, Li; Fei, Qi

    2006-03-01

    This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.

  2. 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.

  3. 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.

  4. 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)…

  5. 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)…

  6. Diagnosis code assignment: models and evaluation metrics.

    PubMed

    Perotte, Adler; Pivovarov, Rimma; Natarajan, Karthik; Weiskopf, Nicole; Wood, Frank; Elhadad, Noémie

    2014-01-01

    The volume of healthcare data is growing rapidly with the adoption of health information technology. We focus on automated ICD9 code assignment from discharge summary content and methods for evaluating such assignments. We study ICD9 diagnosis codes and discharge summaries from the publicly available Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC II) repository. We experiment with two coding approaches: one that treats each ICD9 code independently of each other (flat classifier), and one that leverages the hierarchical nature of ICD9 codes into its modeling (hierarchy-based classifier). We propose novel evaluation metrics, which reflect the distances among gold-standard and predicted codes and their locations in the ICD9 tree. Experimental setup, code for modeling, and evaluation scripts are made available to the research community. The hierarchy-based classifier outperforms the flat classifier with F-measures of 39.5% and 27.6%, respectively, when trained on 20,533 documents and tested on 2282 documents. While recall is improved at the expense of precision, our novel evaluation metrics show a more refined assessment: for instance, the hierarchy-based classifier identifies the correct sub-tree of gold-standard codes more often than the flat classifier. Error analysis reveals that gold-standard codes are not perfect, and as such the recall and precision are likely underestimated. Hierarchy-based classification yields better ICD9 coding than flat classification for MIMIC patients. Automated ICD9 coding is an example of a task for which data and tools can be shared and for which the research community can work together to build on shared models and advance the state of the art.

  7. Diagnosis code assignment: models and evaluation metrics

    PubMed Central

    Perotte, Adler; Pivovarov, Rimma; Natarajan, Karthik; Weiskopf, Nicole; Wood, Frank; Elhadad, Noémie

    2014-01-01

    Background and objective The volume of healthcare data is growing rapidly with the adoption of health information technology. We focus on automated ICD9 code assignment from discharge summary content and methods for evaluating such assignments. Methods We study ICD9 diagnosis codes and discharge summaries from the publicly available Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC II) repository. We experiment with two coding approaches: one that treats each ICD9 code independently of each other (flat classifier), and one that leverages the hierarchical nature of ICD9 codes into its modeling (hierarchy-based classifier). We propose novel evaluation metrics, which reflect the distances among gold-standard and predicted codes and their locations in the ICD9 tree. Experimental setup, code for modeling, and evaluation scripts are made available to the research community. Results The hierarchy-based classifier outperforms the flat classifier with F-measures of 39.5% and 27.6%, respectively, when trained on 20 533 documents and tested on 2282 documents. While recall is improved at the expense of precision, our novel evaluation metrics show a more refined assessment: for instance, the hierarchy-based classifier identifies the correct sub-tree of gold-standard codes more often than the flat classifier. Error analysis reveals that gold-standard codes are not perfect, and as such the recall and precision are likely underestimated. Conclusions Hierarchy-based classification yields better ICD9 coding than flat classification for MIMIC patients. Automated ICD9 coding is an example of a task for which data and tools can be shared and for which the research community can work together to build on shared models and advance the state of the art. PMID:24296907

  8. Case-based reasoning support for liver disease diagnosis.

    PubMed

    Chuang, Chun-Ling

    2011-09-01

    In Taiwan, as well as in the other countries around the world, liver disease has reigned over the list of leading causes of mortality, and its resistance to early detection renders the disease even more threatening. It is therefore crucial to develop an auxiliary system for diagnosing liver disease so as to enhance the efficiency of medical diagnosis and to expedite the delivery of proper medical treatment. The study accordingly integrated the case-based reasoning (CBR) model into several common classification methods of data mining techniques, including back-propagation neural network (BPN), classification and regression tree, logistic regression, and discriminatory analysis, in an attempt to develop a more efficient model for early diagnosis of liver disease and to enhance classification accuracy. To minimize possible bias, this study used a ten-fold cross-validation to select a best model for more precise diagnosis results and to reduce problems caused by false diagnosis. Through a comparison of five single models, BPN and CBR emerged to be the top two methods in terms of overall performance. For enhancing diagnosis performance, CBR was integrated with other methods, and the results indicated that the accuracy and sensitivity of each CBR-added hybrid model were higher than those of each single model. Of all the CBR-added hybrid models, the BPN-CBR method took the lead in terms of diagnosis capacity with an accuracy rate of 95%, a sensitivity of 98%, and a specificity of 94%. After comparing the five single and hybrid models, the study found BPN-CBR the best model capable of helping physicians to determine the existence of liver disease, achieve an accurate diagnosis, diminish the possibility of a false diagnosis being given to sick people, and avoid the delay of clinical treatment. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. 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.

  10. 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.

  11. Failure diagnosis using discrete event models

    SciTech Connect

    Sampath, M.; Sengupta, R.; Lafortune, S.; Teneketzis, D.; Sinnamohideen, K.

    1994-12-31

    We propose a Discrete Event Systems (DES) approach to the failure diagnosis problem. We present a methodology for modeling physical systems in a DES framework. We discuss the notion of diagnosability and present the construction procedure of the diagnoser. Finally, we illustrate our approach using a Heating, Ventilation and Air Conditioning (HVAC) system.

  12. 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

  13. 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

  14. 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),…

  15. 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),…

  16. Crystallographic Model Validation: from Diagnosis to Healing

    PubMed Central

    Richardson, Jane S.; Prisant, Michael G.; Richardson, David C.

    2013-01-01

    Model validation has evolved from a passive final gatekeeping step to an ongoing diagnosis and healing process that enables significant improvement of accuracy. A recent phase of active development was spurred by the worldwide Protein Data Bank requiring data deposition and establishing Validation Task Force committees, by strong growth in high-quality reference data, by new speed and ease of computations, and by an upswing of interest in large molecular machines and structural ensembles. Progress includes automated correction methods, concise and user-friendly validation reports for referees and on the PDB websites, extension of error correction to RNA and error diagnosis to ligands, carbohydrates, and membrane proteins, and a good start on better methods for low resolution and for multiple conformations. PMID:24064406

  17. Assessing Change in Latent Skills across Time with Longitudinal Cognitive Diagnosis Modeling: An Evaluation of Model Performance

    ERIC Educational Resources Information Center

    Kaya, Yasemin; Leite, Walter L.

    2017-01-01

    Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor…

  18. 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…

  19. 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…

  20. Fault Diagnosis Method of Fault Indicator Based on Maximum Probability

    NASA Astrophysics Data System (ADS)

    Yin, Zili; Zhang, Wei

    2017-05-01

    In order to solve the problem of distribution fault diagnosis in case of misreporting or failed-report of fault indicator information, the characteristics of the fault indicator are analyzed, and the concept of the minimum fault judgment area of the distribution network is developed. Based on which, the mathematical model of fault indicator fault diagnosis is evaluated. The characteristics of fault indicator signals are analyzed. Based on two-in-three principle, a probabilistic fault indicator combination signal processing method is proposed. Based on the combination of the minimum fault judgment area model, the fault indicator combination signal and the interdependence between the fault indicators, a fault diagnosis method based on maximum probability is proposed. The method is based on the similarity between the simulated fault signal and the real fault signal, and the detailed formula is given. The method has good fault-tolerance in the case of misreporting or failed-report of fault indicator information, which can more accurately determine the fault area. The probability of each area is given, and fault alternatives are provided. The proposed approach is feasible and valuable for the dispatching and maintenance personnel to deal with the fault.

  1. Wireless laptop-based phonocardiograph and diagnosis.

    PubMed

    Dao, Amy T

    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.

  2. 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

  3. Crack Diagnosis of Wind Turbine Blades Based on EMD Method

    NASA Astrophysics Data System (ADS)

    Hong-yu, CUI; Ning, DING; Ming, HONG

    2016-11-01

    Wind turbine blades are both the source of power and the core technology of wind generators. After long periods of time or in some extreme conditions, cracks or damage can occur on the surface of the blades. If the wind generators continue to work at this time, the crack will expand until the blade breaks, which can lead to incalculable losses. Therefore, a crack diagnosis method based on EMD for wind turbine blades is proposed in this paper. Based on aerodynamics and fluid-structure coupling theory, an aero-elastic analysis on wind turbine blades model is first made in ANSYS Workbench. Second, based on the aero-elastic analysis and EMD method, the blade cracks are diagnosed and identified in the time and frequency domains, respectively. Finally, the blade model, strain gauge, dynamic signal acquisition and other equipment are used in an experimental study of the aero-elastic analysis and crack damage diagnosis of wind turbine blades to verify the crack diagnosis method proposed in this paper.

  4. 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.

  5. Computational cognitive modeling for the diagnosis of Specific Language Impairment.

    PubMed

    Oliva, Jesus; Serrano, J Ignacio; del Castillo, M Dolores; Iglesias, Angel

    2013-01-01

    Specific Language Impairment (SLI), as many other cognitive deficits, is difficult to diagnose given its heterogeneous profile and its overlap with other impairments. Existing techniques are based on different criteria using behavioral variables on different tasks. In this paper we propose a methodology for the diagnosis of SLI that uses computational cognitive modeling in order to capture the internal mechanisms of the normal and impaired brain. We show that machine learning techniques that use the information of these models perform better than those that only use behavioral variables.

  6. 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…

  7. 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…

  8. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

    PubMed

    Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan

    2017-05-01

    Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

  9. Quad-phased data mining modeling for dementia diagnosis.

    PubMed

    Bang, Sunjoo; Son, Sangjoon; Roh, Hyunwoong; Lee, Jihye; Bae, Sungyun; Lee, Kyungwon; Hong, Changhyung; Shin, Hyunjung

    2017-05-18

    The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians. Therefore, in this paper we propose a quad-phased data mining modeling consisting of 4 modules. In Proposer Module, significant diagnostic criteria are selected that are effective for diagnostics. Then in Predictor Module, a model is constructed to predict and diagnose dementia based on a machine learning algorism. To help clinical physicians understand results of the predictive model better, in Descriptor Module, we interpret causes of diagnostics by profiling patient groups. Lastly, in Visualization Module, we provide visualization to effectively explore characteristics of patient groups. The proposed model is applied for CREDOS study which contains clinical data collected from 37 university-affiliated hospitals in republic of Korea from year 2005 to 2013. This research is an intelligent system enabling intuitive collaboration between CAD system and physicians. And also, improved evaluation process is able to effectively reduce time and cost consuming for clinicians and patients.

  10. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*

    PubMed Central

    Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan

    2017-01-01

    Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111

  11. 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

  12. Development of machine learning models for diagnosis of glaucoma.

    PubMed

    Kim, Seong Jae; Cho, Kyong Jin; Oh, Sejong

    2017-01-01

    The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset. To develop the glaucoma prediction model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset. Finally, we got the best learning model that produces the highest validation accuracy. We analyzed quality of the models using several measures. The random forest model shows best performance and C5.0, SVM, and KNN models show similar accuracy. In the random forest model, the classification accuracy is 0.98, sensitivity is 0.983, specificity is 0.975, and AUC is 0.979. The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes. It will be used for predicting glaucoma against unknown examination records. Clinicians may reference the prediction results and be able to make better decisions. We may combine multiple learning models to increase prediction accuracy. The C5.0 model includes decision rules for prediction. It can be used to explain the reasons for specific predictions.

  13. Teaching Evidence-Based Physical Diagnosis: Six Bedside Lessons.

    PubMed

    McGee, Steven

    2016-12-01

    Evidence-based physical diagnosis is an essential part of the bedside curriculum. By using the likelihood ratio, a simple measure of diagnostic accuracy, teachers can quickly adapt this approach to their bedside teaching. Six recurring themes in evidence-based physical diagnosis are fully reviewed, with examples, in this article.

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

    PubMed

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

    2015-08-01

    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.

  15. 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

  16. [Guideline-based diagnosis of dementia etiology].

    PubMed

    Hofmann, W

    2012-12-01

    Dementia encompasses a variety of underlying conditions among which Alzheimer's disease represents the most common cause and in addition, vascular and Parkinson's disease dementia, dementia with Lewy bodies and frontotemporal lobar degeneration. All the current guidelines specify a two-step procedure for the diagnostics of dementia. The first step entails performing a comprehensive description, diagnosis and confirmation of the syndrome. The presented article focuses on the second step: diagnosis of the etiology. This review gives an overview of the current diagnostic approaches including the new proposals of the biomarker in cerebrospinal fluid.

  17. 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…

  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. Computer aided diagnosis of prostate cancer: A texton based approach

    PubMed Central

    Rampun, Andrik; Tiddeman, Bernie; Zwiggelaar, Reyer; Malcolm, Paul

    2016-01-01

    Purpose: In this paper the authors propose a texton based prostate computer aided diagnosis approach which bypasses the typical feature extraction process such as filtering and convolution which can be computationally expensive. The study focuses the peripheral zone because 75% of prostate cancers start within this region and the majority of prostate cancers arising within this region are more aggressive than those arising in the transitional zone. Methods: For the model development, square patches were extracted at random locations from malignant and benign regions. Subsequently, extracted patches were aggregated and clustered using k-means clustering to generate textons that represent both regions. All textons together form a texton dictionary, which was used to construct a texton map for every peripheral zone in the training images. Based on the texton map, histogram models for each malignant and benign tissue samples were constructed and used as a feature vector to train our classifiers. In the testing phase, four machine learning algorithms were employed to classify each unknown sample tissue based on its corresponding feature vector. Results: The proposed method was tested on 418 T2-W MR images taken from 45 patients. Evaluation results show that the best three classifiers were Bayesian network (Az = 92.8% ± 5.9%), random forest (89.5% ± 7.1%), and k-NN (86.9% ± 7.5%). These results are comparable to the state-of-the-art in the literature. Conclusions: The authors have developed a prostate computer aided diagnosis method based on textons using a single modality of T2-W MRI without the need for the typical feature extraction methods, such as filtering and convolution. The proposed method could form a solid basis for a multimodality magnetic resonance imaging based systems. PMID:27782724

  20. Diagnosis of the hydrology of a small Arctic basin at the tundra-taiga transition using a physically based hydrological model

    NASA Astrophysics Data System (ADS)

    Krogh, Sebastian A.; Pomeroy, John W.; Marsh, Philip

    2017-07-01

    A better understanding of cold regions hydrological processes and regimes in transitional environments is critical for predicting future Arctic freshwater fluxes under climate and vegetation change. A physically based hydrological model using the Cold Regions Hydrological Model platform was created for a small Arctic basin in the tundra-taiga transition region. The model represents snow redistribution and sublimation by wind and vegetation, snowmelt energy budget, evapotranspiration, subsurface flow through organic terrain, infiltration to frozen soils, freezing and thawing of soils, permafrost and streamflow routing. The model was used to reconstruct the basin water cycle over 28 years to understand and quantify the mass fluxes controlling its hydrological regime. Model structure and parameters were set from the current understanding of Arctic hydrology, remote sensing, field research in the basin and region, and calibration against streamflow observations. Calibration was restricted to subsurface hydraulic and storage parameters. Multi-objective evaluation of the model using observed streamflow, snow accumulation and ground freeze/thaw state showed adequate simulation. Significant spatial variability in the winter mass fluxes was found between tundra, shrubs and forested sites, particularly due to the substantial blowing snow redistribution and sublimation from the wind-swept upper basin, as well as sublimation of canopy intercepted snow from the forest (about 17% of snowfall). At the basin scale, the model showed that evapotranspiration is the largest loss of water (47%), followed by streamflow (39%) and sublimation (14%). The models streamflow performance sensitivity to a set of parameter was analysed, as well as the mean annual mass balance uncertainty associated with these parameters.

  1. 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.

  2. 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.

  3. Histological diagnosis of gastritis based on Sydney System.

    PubMed

    Barwijuk-Machała, M; Sulik, M; Kemona, A; Sobaniec-Lotowska, M

    1998-01-01

    The review of literature concerning gastritis, especially the chronic form has been carried out. Based on published data and own authors experience an application of the Sydney System in differential diagnosis of gastritis was presented.

  4. 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

  5. Agent-based intelligent medical diagnosis system for patients.

    PubMed

    Zhang, Yingfeng; Liu, Sichao; Zhu, Zhenfei; Si, Shubin

    2015-01-01

    According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research. Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted. Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module. The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation. The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.

  6. Case-Mix Adjusting Performance Measures in a Veteran Population: Pharmacy- and Diagnosis-Based Approaches

    PubMed Central

    Liu, Chuan-Fen; Sales, Anne E; Sharp, Nancy D; Fishman, Paul; Sloan, Kevin L; Todd-Stenberg, Jeff; Nichol, W Paul; Rosen, Amy K; Loveland, Susan

    2003-01-01

    Objective To compare the rankings for health care utilization performance measures at the facility level in a Veterans Health Administration (VHA) health care delivery network using pharmacy- and diagnosis-based case-mix adjustment measures. Data Sources/Study Setting The study included veterans who used inpatient or outpatient services in Veterans Integrated Service Network (VISN) 20 during fiscal year 1998 (October 1997 to September 1998; N=126,076). Utilization and pharmacy data were extracted from VHA national databases and the VISN 20 data warehouse. Study Design We estimated concurrent regression models using pharmacy or diagnosis information in the base year (FY1998) to predict health service utilization in the same year. Utilization measures included bed days of care for inpatient care and provider visits for outpatient care. Principal Findings Rankings of predicted utilization measures across facilities vary by case-mix adjustment measure. There is greater consistency within the diagnosis-based models than between the diagnosis- and pharmacy-based models. The eight facilities were ranked differently by the diagnosis- and pharmacy-based models. Conclusions Choice of case-mix adjustment measure affects rankings of facilities on performance measures, raising concerns about the validity of profiling practices. Differences in rankings may reflect differences in comparability of data capture across facilities between pharmacy and diagnosis data sources, and unstable estimates due to small numbers of patients in a facility. PMID:14596393

  7. Multiplex-PCR-Based Screening and Computational Modeling of Virulence Factors and T-Cell Mediated Immunity in Helicobacter pylori Infections for Accurate Clinical Diagnosis

    PubMed Central

    Oktem-Okullu, Sinem; Tiftikci, Arzu; Saruc, Murat; Cicek, Bahattin; Vardareli, Eser; Tozun, Nurdan; Kocagoz, Tanil; Sezerman, Ugur; Yavuz, Ahmet Sinan; Sayi-Yazgan, Ayca

    2015-01-01

    The outcome of H. pylori infection is closely related with bacteria's virulence factors and host immune response. The association between T cells and H. pylori infection has been identified, but the effects of the nine major H. pylori specific virulence factors; cagA, vacA, oipA, babA, hpaA, napA, dupA, ureA, ureB on T cell response in H. pylori infected patients have not been fully elucidated. We developed a multiplex- PCR assay to detect nine H. pylori virulence genes with in a three PCR reactions. Also, the expression levels of Th1, Th17 and Treg cell specific cytokines and transcription factors were detected by using qRT-PCR assays. Furthermore, a novel expert derived model is developed to identify set of factors and rules that can distinguish the ulcer patients from gastritis patients. Within all virulence factors that we tested, we identified a correlation between the presence of napA virulence gene and ulcer disease as a first data. Additionally, a positive correlation between the H. pylori dupA virulence factor and IFN-γ, and H. pylori babA virulence factor and IL-17 was detected in gastritis and ulcer patients respectively. By using computer-based models, clinical outcomes of a patients infected with H. pylori can be predicted by screening the patient's H. pylori vacA m1/m2, ureA and cagA status and IFN-γ (Th1), IL-17 (Th17), and FOXP3 (Treg) expression levels. Herein, we report, for the first time, the relationship between H. pylori virulence factors and host immune responses for diagnostic prediction of gastric diseases using computer—based models. PMID:26287606

  8. Learning medical diagnosis models from multiple experts.

    PubMed

    Valizadegan, Hamed; Nguyen, Quang; Hauskrecht, Milos

    2012-01-01

    Building classification models from clinical data often requires labeling examples by human experts. However, it is difficult to obtain a perfect set of labels everyone agrees on because medical data are typically very complicated and it is quite common that different experts have different opinions on the same patient data. A solution that has been recently explored by the research community is learning from multiple experts/annotators. The objective of learning from multiple experts is to model different characteristics of the human experts and combine them to obtain a consensus model. In this work, we study and develop a new probabilistic approach for learning classification models from labels provided by multiple experts. Our method explicitly models and incorporates three characteristics of annotators into the learning process: their specific prediction model, consistency and bias. We show that in addition to building a superior classification model, our method also helps to model behavior of annotators. We applied the proposed method to learn different characteristics of Physicians labeling clinical records for Heparin Induced Thrombocytopenia (HIT) and combine them in order to obtain a final classifier.

  9. Analog filter diagnosis using the oscillation based method

    NASA Astrophysics Data System (ADS)

    Andrejević Stošović, Miona; Milić, Miljana; Litovski, Vanćo

    2012-12-01

    Oscillation Based Testing (OBT) is an effective and simple solution to the testing problem of continuous time analogue electronic filters. In this paper, diagnosis based on OBT is described for the first time. It will be referred to as OBD. A fault dictionary is created and used to perform diagnosis with artificial neural networks (ANNs) implemented as classifiers. The robustness of the ANN diagnostic concept is also demonstrated by the addition of white noise to the “measured” signals. The implementation of the new concept is demonstrated by testing and diagnosis of a second order notch cell realized with one operational amplifier. Single soft and catastrophic faults are considered in detail and an example of the diagnosis of double soft faults is also given.

  10. Measurement of psychological disorders using cognitive diagnosis models.

    PubMed

    Templin, Jonathan L; Henson, Robert A

    2006-09-01

    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 presents the development of a new cognitive diagnosis model for use in psychological assessment--the DINO (deterministic input; noisy "or" gate) model--which, as an illustrative example, is applied to evaluate and diagnose pathological gamblers. As part of this example, a demonstration of the estimates obtained by cognitive diagnosis models is provided. Such estimates include the probability an individual meets each of a set of dichotomous Diagnostic and Statistical Manual of Mental Disorders (text revision [DSM-IV-TR]; American Psychiatric Association, 2000) criteria, resulting in an estimate of the probability an individual meets the DSM-IV-TR definition for being a pathological gambler. Furthermore, a demonstration of how the hypothesized underlying factors contributing to pathological gambling can be measured with the DINO model is presented, through use of a covariance structure model for the tetrachoric correlation matrix of the dichotomous latent variables representing DSM-IV-TR criteria.

  11. 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…

  12. Probability-Based Inference in Cognitive Diagnosis

    DTIC Science & Technology

    1994-02-01

    of variables in the student model. In Siegler’s study , this corresponds to determining how a child with a given set of strategies at her disposal...programs are commercially available to carry out the number-crunching aspect. We used Andersen, Jensen, Olesen, and Jensen’s (1989) HUGIN program and Noetic ... studying how they are typically acquired (e.g., in mechanics, Clement, 1982; in ratio and proportional reasoning, Karplus, Pulos, & Stage, 1983), and

  13. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    PubMed

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2016-12-22

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  14. 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.

  15. 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.

  16. 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.

  17. TU-G-204-06: Correlation Between Texture Analysis-Based Model Observer and Human Observer in Diagnosis of Ischemic Infarct in Non-Contrast Head CT of Adults

    SciTech Connect

    Li, B; Fujita, A; Buch, K; Sakai, O

    2015-06-15

    Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIs over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast

  18. 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

  19. 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.

  20. 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

  1. Cell diagnosis based on optical forces

    NASA Astrophysics Data System (ADS)

    Sridharan, Meghna; Wei, Yanzhang; Peng, Xiang; Gao, Bruce Z.

    2005-08-01

    In this article, we report the development of a novel system for distinguishing various types of cell based on the differences in the values of optical forces experienced by them. The laser induced optical forces arise when molecules comprising the cell interact with the photons of the laser beam resulting in an exchange of momentum. The basic principle is based on the phenomenon of laser cell guidance: a cell, in proximity of a weakly focused laser beam experiences a net force which attracts it to the centre of the beam and propels it along the axis of the beam. The 'guidance' is thus a direct result of the optical forces experienced by the cell. The guidance force experienced by the cell is directly related to the amount of momentum exchange, which in turn depends on factors such as the cell shape and size, the refractive index difference between the materials composing the cell and the materials in the culture media, and the properties of the beam. Since different cell types have different physical properties, they experience different optical (guiding) forces when they interact with the same laser beam. Since guidance force causes the cells to be propelled along the axis of the beam, the force with which the cell is propelled will directly determine the velocity with which the cell will move along the beam. Hence different types of cells will move with different velocities. Thus by studying this motion of cell, we in turn study the forces experienced by the cell. We propose that by analyzing the velocity with which cells are being guided by a laser beam, we can develop a technique to distinguish various cell types. Extensive GLMT theories calculate and predict theoretically the forces experienced by particles of a given size and refractive index for a given set of beam parameters. These theories are used as the basis to make judgments about the validity of our velocity and force calculation. The velocity assessments are conducted for a statistically significant

  2. 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.

  3. Machine Learning Based Diagnosis of Lithium Batteries

    NASA Astrophysics Data System (ADS)

    Ibe-Ekeocha, Chinemerem Christopher

    The depletion of the world's current petroleum reserve, coupled with the negative effects of carbon monoxide and other harmful petrochemical by-products on the environment, is the driving force behind the movement towards renewable and sustainable energy sources. Furthermore, the growing transportation sector consumes a significant portion of the total energy used in the United States. A complete electrification of this sector would require a significant development in electric vehicles (EVs) and hybrid electric vehicles (HEVs), thus translating to a reduction in the carbon footprint. As the market for EVs and HEVs grows, their battery management systems (BMS) need to be improved accordingly. The BMS is not only responsible for optimally charging and discharging the battery, but also monitoring battery's state of charge (SOC) and state of health (SOH). SOC, similar to an energy gauge, is a representation of a battery's remaining charge level as a percentage of its total possible charge at full capacity. Similarly, SOH is a measure of deterioration of a battery; thus it is a representation of the battery's age. Both SOC and SOH are not measurable, so it is important that these quantities are estimated accurately. An inaccurate estimation could not only be inconvenient for EV consumers, but also potentially detrimental to battery's performance and life. Such estimations could be implemented either online, while battery is in use, or offline when battery is at rest. This thesis presents intelligent online SOC and SOH estimation methods using machine learning tools such as artificial neural network (ANN). ANNs are a powerful generalization tool if programmed and trained effectively. Unlike other estimation strategies, the techniques used require no battery modeling or knowledge of battery internal parameters but rather uses battery's voltage, charge/discharge current, and ambient temperature measurements to accurately estimate battery's SOC and SOH. The developed

  4. 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.

  5. 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.

  6. Fitting data to model: structural equation modeling diagnosis using two scatter plots.

    PubMed

    Yuan, Ke-Hai; Hayashi, Kentaro

    2010-12-01

    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 the residual-based M-distance with the quantile of a chi distribution. It allows the researcher to visually identify clusters of potential outliers. The article further studies the effect of the potential outliers on the overall model evaluation when they are removed according to the order of the clusters exhibited in the plot. Suggestions are provided on determining the outlier status of outstanding cases in real data analysis. Recommendations are also made on the choice of robust methods and maximum likelihood following outlier removal.

  7. 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.

  8. 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.

  9. Evidence-Based Diagnosis: Incorporating Diagnostic Instruments into Clinical Practice

    ERIC Educational Resources Information Center

    Doss, Amanda Jensen

    2005-01-01

    This article is intended to serve as a practical guide for practitioners interested in incorporating evidence-based diagnosis (EBD) instruments into their clinical practices to refine the diagnostic process. Three measures are used to illustrate this process, the DISC-IV (Shaffer et al., 2000), the Schedule for Affective Disorders and…

  10. The research of elevator health diagnosis method based on Bayesian network

    NASA Astrophysics Data System (ADS)

    Liu, Chang; Zhang, Xinzheng; Liu, Xindong; Chen, Can

    2017-08-01

    Elevator, as a complex mechanical system, is hard to determine the factors that affect components’ status. In accordance with this special characteristic, the Elevator Fault Diagnosis Model is proposed based on Bayesian Network in this paper. The method uses different samples of the elevator and adopts Monte Carlo inference mechanism for Bayesian Network Model structure and parameter learning. Eventually, an elevator fault diagnosis model based on Bayesian network is established, which accords with the theory of elevator operation. In this paper, we use different kinds of fault data samples to test the method. Experimental results demonstrate the higher accuracy of our method. This paper provides a good assistant method by means of Fault prediction and Health diagnosis of elevator system at present.

  11. Pattern-Based Medical Diagnosis on a Microcomputer*

    PubMed Central

    Fisher, Paul R.; Kurlander, David J.

    1980-01-01

    A differential diagnosis microcomputer program has been written that utilizes both pattern-recognition and logical analysis in its algorithm. Together with auxilliary routines, the program (called DX) performs medical diagnosis, stores and retrieves patient information, creates new model symptom sets using information from the patient pool, and trains its own data matrices. Designed to be user oriented, DX can communicate the reasoning behind its decisions, thereby complementing the physician's own skills. This microcomputer system is geared towards community practices because the matrices are trained by statistics DX gathers from the local patient population. Although the program was written to be used in any area of medical practice, the arrays are presently being trained for the diagnosis of presentations of the acute abdomen.

  12. 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.

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

    PubMed

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

    2010-08-01

    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/mum), 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.

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

    PubMed

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

    2015-01-01

    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. 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. Initiation of antidepressive treatment prior to cancer diagnosis is common and is associated with an increased mortality.

  15. Breast cancer early diagnosis based on hybrid strategy.

    PubMed

    Li, Peng; Bi, Tingting; Huang, Jiuling; Li, Siben

    2014-01-01

    The frequent occurrence of breast cancer and its serious consequences have attracted worldwide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an AdaBoost-SVM classification algorithm, combined with the cluster boundary sampling preprocessing techniques (CBS-AdaBoost-SVM), is proposed in this paper for the early diagnosis of breast cancer. The algorithm uses machine learning method to diagnose the unknown image data. Moreover, not all of the characteristics play positive roles for classification. To address this issue the paper delete redundant features by using Rough set attribute reduction algorithm based on the genetic algorithm (GA). The effectiveness of the proposed methods are examined on DDSM by calculating its accuracy, confusion matrix, and receiver operating characteristic curves, which give important clues to the physicians for early diagnosis of breast cancer.

  16. A Low Frequency Uni-variate Model for the Effective Diagnosis and Prognosis of Bearing Signals Based Upon High Frequency Data

    DTIC Science & Technology

    2014-10-02

    discovered and used for diagnostic and prognostic analysis (Lin & Zuo (2003), Peng & Chu (2004), Jardine et al., 2006). Recently, the use of time...12591269. Jardine , A. K., Lin, D., Banjevic, D., (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance

  17. 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

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

    PubMed

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

    2015-07-06

    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.

  19. Learning in the model space for cognitive fault diagnosis.

    PubMed

    Chen, Huanhuan; Tino, Peter; Rodan, Ali; Yao, Xin

    2014-01-01

    The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.

  20. 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.

  1. Model-supported diagnosis for integrated vehicle health management of space systems

    NASA Astrophysics Data System (ADS)

    Dannenmann, Peter; Busch, Wolfgang

    2003-08-01

    In this paper we present a new architecture for integrating system health monitoring tasks into the development- and life cycle of space systems. On the basis of model-supported diagnosis technology the presented method uses information for diagnosis purposes that is already gathered during the development of a technical system. This information is extracted from simulation models used for design-studies and what-if-analyses during the design- and development phase. For building up these simulation models easily, we developed a library of generic models of spacecraft components. These models cover the components' nominal and off-nominal behavior as it is specified in the component FMECAs. By combining and parametrizing the components a system model is built up. Since due to the limited resources on board of a spacecraft we can not use the model directly for model-based diagnosis, we use a model-supported approach: By systematically simulating possible component faults within the system's operational modes, we retrieve a set of measurement data that serve as symptoms to the failure modes. By classifying these data we get a knowledge-base for a symptom-based on-board diagnosis system. In order to cope with the uncertainty in the measurement data, this diagnosis system has been realized as a fuzzy system that on the basis of the given knowledge-base computes the most probable diagnoses from the given symptoms. The described system has been implemented within Astrium's Columbus Simulation System (CSS) and has been evaluated on several aerospace systems ranging from an unmanned aerial robot on the basis of an airship to the Propulsion and Reboost Subsystem of the Automated Transfer Vehicle (ATV), a supply spacecraft for the International Space Station.

  2. Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

    NASA Astrophysics Data System (ADS)

    Sharifi, Reza; Langari, Reza

    2017-02-01

    This paper presents a methodology for sensor fault diagnosis in nonlinear systems using a Mixture of Probabilistic Principal Component Analysis (MPPCA) models. This methodology separates the measurement space into several locally linear regions, each of which is associated with a Probabilistic PCA (PPCA) model. Using the transformation associated with each PPCA model, a parity relation scheme is used to construct a residual vector. Bayesian analysis of the residuals forms the basis for detection and isolation of sensor faults across the entire range of operation of the system. The resulting method is demonstrated in its application to sensor fault diagnosis of a fully instrumented HVAC system. The results show accurate detection of sensor faults under the assumption that a single sensor is faulty.

  3. 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

  4. Reflectance spectroscopy for diagnosis of epithelial precancer: model-based analysis of fiber-optic probe designs to resolve spectral information from epithelium and stroma

    PubMed Central

    Arifler, Dizem; Schwarz, Richard A.; Chang, Sung K.; Richards-Kortum, Rebecca

    2009-01-01

    Reflectance spectroscopy is a promising technology for detection of epithelial precancer. Fiber-optic probes that selectively collect scattered light from both the epithelium and the underlying stroma are likely to improve diagnostic performance of in vivo reflectance spectroscopy by revealing diagnostic features unique to each layer. We present Monte Carlo models with which to evaluate fiber-optic probe geometries with respect to sampling depth and depth resolution. We propose a probe design that utilizes half-ball lens coupled source and detector fibers to isolate epithelial scattering from stromal scattering and hence to resolve spectral information from the two layers. The probe is extremely compact and can provide easy access to different organ sites. PMID:16045217

  5. 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…

  6. Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

    ERIC Educational Resources Information Center

    Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

    2009-01-01

    This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

  7. Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

    ERIC Educational Resources Information Center

    Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

    2009-01-01

    This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

  8. Studies in knowledge-based diagnosis of failures in robotic assembly

    NASA Technical Reports Server (NTRS)

    Lam, Raymond K.; Pollard, Nancy S.; Desai, Rajiv S.

    1990-01-01

    The telerobot diagnostic system (TDS) is a knowledge-based system that is being developed for identification and diagnosis of failures in the space robotic domain. The system is able to isolate the symptoms of the failure, generate failure hypotheses based on these symptoms, and test their validity at various levels by interpreting or simulating the effects of the hypotheses on results of plan execution. The implementation of the TDS is outlined. The classification of failures and the types of system models used by the TDS are discussed. A detailed example of the TDS approach to failure diagnosis is provided.

  9. Reporting Test Outcomes Using Models for Cognitive Diagnosis. Research Report. ETS RR-06-28

    ERIC Educational Resources Information Center

    von Davier, Matthias; DiBello, Lou; Yamamoto, Kentaro

    2006-01-01

    Models for cognitive diagnosis have been developed as an attempt to provide more than a single test score from item response data. Most approaches are based on a hypothesis that relates items to underlying skills. This relation takes the form of a design matrix that specifies for each cognitive item which skills are required to solve the item and…

  10. 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.

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

    PubMed Central

    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

  12. Diagnosis and treatment of simple acid-base disorders.

    PubMed

    Ayers, Phil; Warrington, Laurie

    2008-01-01

    The ability to diagnose and treat acid-base disorders is an important component in the practice of the nutrition support clinician. A complete understanding of the basic principles of metabolic and respiratory disorders allows the practitioner to formulate educated decisions regarding fluids, parenteral nutrition salts, and the management of electrolytes. This review will discuss the diagnosis and treatment of common metabolic and respiratory disorders encountered in nutrition support practice.

  13. Diagnosis of Compartment Syndrome Based on Tissue Oxygenation

    DTIC Science & Technology

    2013-10-01

    measured with microprobes has been shown to be highly correlated with tissue oxygenation and the extent of ischemia reperfusion injury .3 Near-infrared...Tissue Oxygenation PRINCIPAL INVESTIGATOR: Hubert Kim, M.D., Ph.D. CONTRACTING ORGANIZATION: Northern California Institute for...SUBTITLE Diagnosis of Compartment Syndrome based on Tissue Oxygenation 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-10-1-1024 5c. PROGRAM ELEMENT

  14. Decision tree and PCA-based fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Sun, Weixiang; Chen, Jin; Li, Jiaqing

    2007-04-01

    After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

  15. Early diagnosis model for meningitis supports public health decision making.

    PubMed

    Close, Rebecca M; Ejidokun, Oluwatoyin O; Verlander, Neville Q; Fraser, Graham; Meltzer, Margie; Rehman, Yasmin; Muir, Peter; Ninis, Nelly; Stuart, James M

    2011-07-01

    To develop a predictive model for rapid differential diagnosis of meningitis and meningococcal septicaemia to support public health decisions on chemoprophylaxis for contacts. Prospective study of suspected cases of acute meningitis and meningococcal septicaemia admitted to hospitals in the South West, West Midlands and London Regions of England from July 2008 to June 2009. Epidemiological, clinical and laboratory variables on admission were recorded. Logistic regression was used to derive a predictive model. Of the 719 suspect cases reported, 385 confirmed cases were included in analysis. Peripheral blood polymorphonuclear count of >16 × 10(9)/l, serum C-reactive protein of >100 mg/l and haemorrhagic rash were strongly and independently associated with diagnosis of bacterial meningitis and meningococcal septicaemia. Using a simple scoring system, the presence of any one of these factors gave a probability of >95% in predicting the final diagnosis. We have developed a model using laboratory and clinical factors, but not dependent on availability of CSF, for differentiating acute bacterial from viral meningitis within a few hours of admission to hospital. This scoring system is recommended in public health management of suspected cases of meningitis and meningococcal septicaemia to inform decisions on chemoprophylaxis. Copyright © 2011 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

  16. 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.

  17. Integrated Knowledge Based Expert System for Disease Diagnosis System

    NASA Astrophysics Data System (ADS)

    Arbaiy, Nureize; Sulaiman, Shafiza Eliza; Hassan, Norlida; Afizah Afip, Zehan

    2017-08-01

    The role and importance of healthcare systems to improve quality of life and social welfare in a society have been well recognized. Attention should be given to raise awareness and implementing appropriate measures to improve health care. Therefore, a computer based system is developed to serve as an alternative for people to self-diagnose their health status based on given symptoms. This strategy should be emphasized so that people can utilize the information correctly as a reference to enjoy healthier life. Hence, a Web-based Community Center for Healthcare Diagnosis system is developed based on expert system technique. Expert system reasoning technique is employed in the system to enable information about treatment and prevention of the diseases based on given symptoms. At present, three diseases are included which are arthritis, thalassemia and pneumococcal. Sets of rule and fact are managed in the knowledge based system. Web based technology is used as a platform to disseminate the information to users in order for them to optimize the information appropriately. This system will benefit people who wish to increase health awareness and seek expert knowledge on the diseases by performing self-diagnosis for early disease detection.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Vibration signal models for fault diagnosis of planetary gearboxes

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Zuo, Ming J.

    2012-10-01

    A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to fault diagnosis of planetary gearboxes. Considering both the amplitude modulation and the frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for fault diagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. [Rapid diagnosis of TCM syndrome based on spectrometry].

    PubMed

    Lin, Ling; Zhang, Jing; Zhao, Jing; Li, Gang; Zhang, Bao-ju; Tong, Yin

    2011-03-01

    Tongue color provides important information in traditional Chinese medicine (TCM) diagnosis, but in the process of TCM inspection, the various surrounding environment and subjective effect of doctors will influence the correctness of diagnosis. The present article put forward a brand new thought to study TCM objectivity, that is to research the essence message of tongue from perspective of spectrum, study the continuous spectrum to replace the observation of tongue color. The experiment used near infrared spectrum to collect reflection spectrum data of tongue among 53 exterior cold and interior heat patients, 37 healthy people and 21 wind chill suffers. Used matlab for data pretreatment, minitab for statistics modeling and prediction used partial least squares, the accuracy of prediction is 85.6%, but however, the spectrum of near infrared fail to distinguish exterior cold and interior heat patients from wind chill sufferers. The article provides a brand new way to implement the objectivity of tongue diagnosis in TCM, and also offers data support for the study of TCM syndrome.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Sialendoscopy-based diagnosis and treatment of salivary ductal obstructions.

    PubMed

    Liao, Gui Qing; Su, Yu Xiong; Zheng, Guang Sen; Liang, Li Zhong

    2010-01-01

    Salivary gland ductal obstruction is traditionally treated by sialoadenectomy when conservative measures fail. During the last decade, sialendoscopy has become the preferred approach in the management of salivary ductal obstructions. Sialendoscopy can provide direct, accurate and reliable visualisation of the salivary duct lumen and ductal pathologies, and can eliminate pathologies with miniaturised instrumentation. Now, sialendoscopic surgery is a promising option for patients who can be offered a satisfactory clinical outcome while avoiding sialoadenectomy. The present article briefly outlines sialendoscopy-based diagnosis and treatment of salivary ductal obstructions.

  15. 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.

  16. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    PubMed

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Intelligence system based classification approach for medical disease diagnosis

    NASA Astrophysics Data System (ADS)

    Sagir, Abdu Masanawa; Sathasivam, Saratha

    2017-08-01

    The prediction of breast cancer in women who have no signs or symptoms of the disease as well as survivability after undergone certain surgery has been a challenging problem for medical researchers. The decision about presence or absence of diseases depends on the physician's intuition, experience and skill for comparing current indicators with previous one than on knowledge rich data hidden in a database. This measure is a very crucial and challenging task. The goal is to predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system. A framework describes methodology for designing and evaluation of classification performances of two discrete ANFIS systems of hybrid learning algorithms least square estimates with Modified Levenberg-Marquardt and Gradient descent algorithms that can be used by physicians to accelerate diagnosis process. The proposed method's performance was evaluated based on training and test datasets with mammographic mass and Haberman's survival Datasets obtained from benchmarked datasets of University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity is examined. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

  18. 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

  19. 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.

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

    DOE PAGES

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

    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

  1. 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.

  2. A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis.

    PubMed

    Serpen, G; Tekkedil, D K; Orra, M

    2008-02-01

    This paper aims to demonstrate that knowledge-based hybrid learning algorithms are positioned to offer better performance in comparison with purely empirical machine learning algorithms for the automatic classification task associated with the diagnosis of a medical condition described as pulmonary embolism (PE). The main premise is that there exists substantial and significant specialized knowledge in the domain of PE, which can readily be leveraged for bootstrapping a knowledge-based hybrid classifier that employs both the explanation-based and the empirical learning. The modified prospective investigation of pulmonary embolism diagnosis (PIOPED) criteria, which represent the pre-eminent collective experiential knowledge base among nuclear radiologists as a diagnosis procedure for PE, are conveniently defined in terms of a set of if-then rules. As such, it lends itself to being captured into a knowledge base through instantiating a knowledge-based hybrid learning algorithm. This study shows the instantiation of a knowledge-based artificial neural network (KBANN) classifier through the modified PIOPED criteria for the diagnosis of PE. The development effort for the KBANN that captures the rule base associated with the PIOPED criteria as well as further refinement of the same rule base through highly specialized domain expertise is presented. Through a testing dataset generated with the help of nuclear radiologists, performance of the instantiated KBANN is profiled. Performances of a set of empirical machine learning algorithms, which are configured as classifiers and include the nai ve Bayes, the Bayesian Belief network, the multilayer perceptron neural network, the C4.5 decision tree algorithm, and two meta learners with boosting and bagging, are also profiled on the same dataset for the purpose of comparison with that of the KBANN. Simulation results indicate that the KBANN can effectively model and leverage the PIOPED knowledge base and its further refinements

  3. 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

  4. Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model.

    PubMed

    Khodayari-Rostamabad, Ahmad; Reilly, James P; Hasey, Gary; Debruin, Hubert; Maccrimmon, Duncan

    2010-01-01

    An automated diagnosis procedure based on a statistical machine learning methodology using electroencephalograph (EEG) data is proposed for diagnosis of psychiatric illness. First, a large collection of candidate features, mostly consisting of various statistical quantities, are calculated from the subject's EEG. This large set of candidate features is then reduced into a much smaller set of most relevant features using a feature selection procedure. The selected features are then used to evaluate the class likelihoods, through the use of a mixture of factor analysis (MFA) statistical model [7]. In a training set of 207 subjects, including 64 subjects with major depressive disorder (MDD), 40 subjects with chronic schizophrenia, 12 subjects with bipolar depression and 91 normal or healthy subjects, the average correct diagnosis rate attained using the proposed method is over 85%, as determined by various cross-validation experiments. The promise is that, with further development, the proposed methodology could serve as a valuable adjunctive tool for the medical practitioner.

  5. Predictive models in the diagnosis and treatment of autoimmune epilepsy.

    PubMed

    Dubey, Divyanshu; Singh, Jaysingh; Britton, Jeffrey W; Pittock, Sean J; Flanagan, Eoin P; Lennon, Vanda A; Tillema, Jan-Mendelt; Wirrell, Elaine; Shin, Cheolsu; So, Elson; Cascino, Gregory D; Wingerchuk, Dean M; Hoerth, Matthew T; Shih, Jerry J; Nickels, Katherine C; McKeon, Andrew

    2017-07-01

    To validate predictive models for neural antibody positivity and immunotherapy response in epilepsy. We conducted a retrospective study of epilepsy cases at Mayo Clinic (Rochester-MN; Scottsdale-AZ, and Jacksonville-FL) in whom autoimmune encephalopathy/epilepsy/dementia autoantibody testing profiles were requested (06/30/2014-06/30/2016). An Antibody Prevalence in Epilepsy (APE) score, based on clinical characteristics, was assigned to each patient. Among patients who received immunotherapy, a Response to Immunotherapy in Epilepsy (RITE) score was assigned. Favorable seizure outcome was defined as >50% reduction of seizure frequency at the first follow-up. Serum and cerebrospinal fluid (CSF) from 1,736 patients were sent to the Mayo Clinic Neuroimmunology Laboratory for neural autoantibody evaluation. Three hundred eighty-seven of these patients met the diagnostic criteria for epilepsy. Central nervous system (CNS)-specific antibodies were detected in 44 patients. Certain clinical features such as new-onset epilepsy, autonomic dysfunction, viral prodrome, faciobrachial dystonic seizures/oral dyskinesia, inflammatory CSF profile, and mesial temporal magnetic resonance imaging (MRI) abnormalities had a significant association with positive antibody results. A significantly higher proportion of antibody-positive patients had an APE score ≥4 (97.7% vs. 21.6%, p < 0.01). Sensitivity and specificity of an APE score ≥4 to predict presence of specific neural auto-antibody were 97.7% and 77.9%, respectively. In the subset of patients who received immunotherapy (77), autonomic dysfunction, faciobrachial dystonic seizures/oral dyskinesia, early initiation of immunotherapy, and presence of antibodies targeting plasma membrane proteins (cell-surface antigens) were associated with favorable seizure outcome. Sensitivity and specificity of a RITE score ≥7 to predict favorable seizure outcome were 87.5% and 83.8%, respectively. APE and RITE scores can aid diagnosis

  6. Integration of Anatomic and Pathogenetic Bases for Early Lung Cancer Diagnosis

    DTIC Science & Technology

    2007-03-01

    universal computer -aided diagnosis (UCAD) system combining anatomical knowledge based CAD (AK-CAD) and computer -aided pathological diagnosis (CAPD...of the project is to design a new universal computer -aided diagnosis system for analyzing the anatomic and pathogenetic bases of various radiologic...structures classified by our universal computer -aided diagnosis (UCAD) system based on helical CT images. The proposed novel UCAD system is named as

  7. Utilizing DMAIC six sigma and evidence-based medicine to streamline diagnosis in chest pain.

    PubMed

    Kumar, Sameer; Thomas, Kory M

    2010-01-01

    The purpose of this study was to quantify the difference between the current process flow model for a typical patient workup for chest pain and development of a new process flow model that incorporates DMAIC (define, measure, analyze, improve, control) Six Sigma and evidence-based medicine in a best practices model for diagnosis and treatment. The first stage, DMAIC Six Sigma, is used to highlight areas of variability and unnecessary tests in the current process flow for a patient presenting to the emergency department or physician's clinic with chest pain (also known as angina). The next stage, patient process flow, utilizes DMAIC results in the development of a simulated model that represents real-world variability in the diagnosis and treatment of a patient presenting with angina. The third and final stage is used to analyze the evidence-based output and quantify the factors that drive physician diagnosis accuracy and treatment, as well as review the potential for a broad national evidence-based database. Because of the collective expertise captured within the computer-oriented evidence-based model, the study has introduced an innovative approach to health care delivery by bringing expert-level care to any physician triaging a patient for chest pain anywhere in the world. Similar models can be created for other ailments as well, such as headache, gastrointestinal upset, and back pain. This updated way of looking at diagnosing patients stemming from an evidence-based best practice decision support model may improve workflow processes and cost savings across the health care continuum.

  8. 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

  9. Texture-based computer-assisted diagnosis for fiberscopic images.

    PubMed

    Munzenmayer, Christian; Winter, Christian; Rupp, Stephan; Kage, Andreas; Wittenberg, Thomas

    2009-01-01

    Flexible endoscopes based on fiber bundles are still widely used despite the recent success of so-called tipchip endoscopes. This is partly due to the costs and that for extremely thin diameters (below 3 mm) there are still only fiberscopes available. Due to the inevitable artifacts caused by the transition from the fiber bundles to the sensor chip, image and texture analysis algorithms are severely handicapped. Therefore, texture-based computer-assisted diagnosis (CAD) systems could not be used in such domains without image preprocessing. We describe a CAD system approach that includes an image filtering algorithm to remove the fiber image artifacts first and then applies conventional color texture algorithms that have been applied to other endoscopic disciplines in the past. The concept is evaluated on an image database with artificially rendered fiber artifacts so that ground truth information is available.

  10. 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.

  11. 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.

  12. SVM-based automatic diagnosis method for keratoconus

    NASA Astrophysics Data System (ADS)

    Gao, Yuhong; Wu, Qiang; Li, Jing; Sun, Jiande; Wan, Wenbo

    2017-06-01

    Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.

  13. Knowledge-based and integrated monitoring and diagnosis in autonomous power systems

    NASA Technical Reports Server (NTRS)

    Momoh, J. A.; Zhang, Z. Z.

    1990-01-01

    A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.

  14. 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.

  15. A failure diagnosis system based on a neural network classifier for the space shuttle main engine

    NASA Technical Reports Server (NTRS)

    Duyar, Ahmet; Merrill, Walter

    1990-01-01

    A conceptual design of a model based failure detection and diagnosis system is developed for the space shuttle main engine. This design relies on the accurate and reliable identification of the parameters of the highly nonlinear and very complex engine. The design approach is presented in some detail and results for a failed valve are presented. These preliminary results verify that the developed parameter identification technique together with a neural network classifier can be used for this purpose.

  16. 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.

  17. Compound fault diagnosis of gearboxes based on GFT component extraction

    NASA Astrophysics Data System (ADS)

    Ou, Lu; Yu, Dejie

    2016-11-01

    Compound fault diagnosis of gearboxes is of great importance to the long-term safe operation of rotating machines, and the key is to separate different fault components. In this paper, the path graph is introduced into the vibration signal analysis and the graph Fourier transform (GFT) of vibration signals are investigated from the graph spectrum domain. To better extract the fault components in gearboxes, a new adjacency weight matrix is defined and then the GFT of simulation signals of the gear and the bearing with localized faults are analyzed. Further, since the GFT graph spectrum of the gear fault component and the bearing fault component are mainly distributed in the low-order region and the high-order region, respectively, a novel method for the compound fault diagnosis of gearboxes based on GFT component extraction is proposed. In this method, the nonzero ratios, which are introduced to analyze the eigenvectors auxiliary, and the GFT of a gearbox vibration signal, are firstly calculated. Then, the order thresholds for reconstructed fault components are determined and the fault components are extracted. Finally, the Hilbert demodulation analyses are conducted. According to the envelope spectra of the fault components, the faults of the gear and the bearing can be diagnosed respectively. The performance of the proposed method is validated by the simulation data and the experiment signals from a gearbox with compound faults.

  18. Instrument for bearing fault diagnosis based on demodulated resonance technology

    NASA Astrophysics Data System (ADS)

    Lu, Yi; Hu, Xiao-feng; Zheng, Yong-jun

    2010-08-01

    Rolling bearing is a very important comment for mechanical system. The traditional measurement method is using time domain analysis and frequency domain analysis with mechanical vibration theory. However, these methods are very difficult to precisely diagnose the rolling bearing fault. Demodulated resonance technology is an effective method to diagnose bearing fault. In this paper, the realization of demodulated resonance technology is described in detail. It includes three important aspects: design of filter, envelope demodulation and spectrum zooming. Hilbert transform is an effective solution to envelope demodulation. Based on this theory, the PC controlled experimental device for fault diagnosis was built up. The ball bearing of type 6201 was measured on the experimental device, and the applications software was programmed by VC++, which had the functions of real-time monitoring, spectral analysis (FFT), digital filtering and zoom-FFT etc. So this system can get fault characteristic from the vibration speed signal with big noise and automatically identify the type of fault. The types included the fault of inner ring, outer ring and rolling element. Results showed that it has a remarkable effect on bearing fault diagnosis, and it had many advantages such as high precision, high degree of automatic measurement.

  19. Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

    NASA Technical Reports Server (NTRS)

    Warner, James E.; Zubair, Mohammad; Ranjan, Desh

    2017-01-01

    This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

  20. 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.

  1. 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

  2. NGS-based Molecular diagnosis of 105 eyeGENE(®) probands with Retinitis Pigmentosa.

    PubMed

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

    2015-12-15

    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(®).

  3. 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

  4. Image standards in tissue-based diagnosis (diagnostic surgical pathology).

    PubMed

    Kayser, Klaus; Görtler, Jürgen; Goldmann, Torsten; Vollmer, Ekkehard; Hufnagl, Peter; Kayser, Gian

    2008-04-18

    Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. THEORY AND EXPERIENCES: Images used in tissue-based diagnosis present with pathology-specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease-image combination, human-diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image acquisition systems (resolution, colour temperature, focus

  5. 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.

  6. Plantar fasciitis: evidence-based review of diagnosis and therapy.

    PubMed

    Cole, Charles; Seto, Craig; Gazewood, John

    2005-12-01

    Plantar fasciitis causes heel pain in active as well as sedentary adults of all ages. The condition is more likely to occur in persons who are obese or in those who are on their feet most of the day. A diagnosis of plantar fasciitis is based on the patient's history and physical findings. The accuracy of radiologic studies in diagnosing plantar heel pain is unknown. Most interventions used to manage plantar fasciitis have not been studied adequately; however, shoe inserts, stretching exercises, steroid injection, and custom-made night splints may be beneficial. Extracorporeal shock wave therapy may effectively treat runners with chronic heel pain but is ineffective in other patients. Limited evidence suggests that casting or surgery may be beneficial when conservative measures fail.

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

    PubMed

    Parkash, Om; Shueb, Rafidah Hanim

    2015-10-19

    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.

  8. Telematics techniques for image based diagnosis, therapy planning and monitoring.

    PubMed

    Bidaut, L M; Scherrer, J R

    1998-01-01

    This paper is intended to describe and illustrate some of the actual use of telematics related techniques together with modern biomedical imaging capabilities for helping in diagnosis, as well as for the planning and monitoring of therapy. To this end, most current imaging modalities are initially introduced. Then it is shown how telematics related techniques are necessary to improve the outcome of current image-based protocols. Such techniques allow data, means, or competencies--which may intrinsically be of a complementary nature or distributed at many different locations--to be integrated together and transcend the simple sum of individual expectations. Examples of actual implementations are given in the fields of radio-oncology, neurosurgery and orthopedics. To conclude, the papers and posters presented in the corresponding session of the MIE'97 symposium are summarized to provide further telematics references for the reader.

  9. 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

  10. [Diagnosis and treatment of disordered acid-base balance].

    PubMed

    Kosch, M; Schaefer, R M

    2005-01-20

    Differential diagnosis in disordered acid-base homeostasis is usually possible by measuring the pH, pCO2, pO2 and bicarbonate concentration, and enables differentiation between respiratory alkalosis and acidosis, and metabolic alkalosis and acidosis. Compensatory counter-regulation (respiratory or renal) can make correct assessment of the primary disorder problematic. Treatment of the underlying disease, in particular the provision of adequate oxygenation in respiratory disorders is of the essence. In chronic forms of metabolic acidosis, for example in chronic renal insufficiency and elderly patients, bicarbonate substitution should be initiated in order to prevent the negative effects on various organ systems. Sodium bicarbonate formulations that can be assimilated from the small bowel are especially tolerable and suitable.

  11. Deterministic versus evidence-based attitude towards clinical diagnosis.

    PubMed

    Soltani, Akbar; Moayyeri, Alireza

    2007-08-01

    Generally, two basic classes have been proposed for scientific explanation of events. Deductive reasoning emphasizes on reaching conclusions about a hypothesis based on verification of universal laws pertinent to that hypothesis, while inductive or probabilistic reasoning explains an event by calculation of some probabilities for that event to be related to a given hypothesis. Although both types of reasoning are used in clinical practice, evidence-based medicine stresses on the advantages of the second approach for most instances in medical decision making. While 'probabilistic or evidence-based' reasoning seems to involve more mathematical formulas at the first look, this attitude is more dynamic and less imprisoned by the rigidity of mathematics comparing with 'deterministic or mathematical attitude'. In the field of medical diagnosis, appreciation of uncertainty in clinical encounters and utilization of likelihood ratio as measure of accuracy seem to be the most important characteristics of evidence-based doctors. Other characteristics include use of series of tests for refining probability, changing diagnostic thresholds considering external evidences and nature of the disease, and attention to confidence intervals to estimate uncertainty of research-derived parameters.

  12. 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…

  13. 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…

  14. Physically based diagnosis and prognosis of cracked rotor shafts

    NASA Astrophysics Data System (ADS)

    Oppenheimer, Charles H.; Loparo, Kenneth A.

    2002-07-01

    A physics-based approach for diagnostics and prognostics using integrated observers and life models is presented. Observers are filters based on physical models of machine- fault combinations and use measured machine signatures to identify and characterize the state of a machine. Observers are adaptively deployed as a machine wears and can be coupled with one another to handle interacting conditions and faults. The scheme is detailed using the fault of a cracked rotor shaft that interacts with gravity and imbalance. Observers for shaft cracking and imbalance are presented. The observers provide machine condition and fault strengths to life models used to determine remaining machine life. A life model based on the Forman crack growth law of linear elastic fracture mechanics is developed to determine the number of machine cycles remaining until catastrophic failure.

  15. 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.

  16. Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

    NASA Astrophysics Data System (ADS)

    Ben Rabah, N.; Saddem, R.; Ben Hmida, F.; Carre-Menetrier, V.; Tagina, M.

    2017-01-01

    Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach.

  17. Modeling power systems for diagnosis - How good is good enough?

    NASA Astrophysics Data System (ADS)

    Fesq, Lorraine M.; Stephan, Amy; McNamee, Lawrence

    Space power system automation requires comprehensive fault detection, isolation and recovery. Extending current approaches to on-board power management to isolate and resolve (as well as detect) failures would greatly increase the size and complexity of the flight software. Instead, we identified a technique called model-based constraint suspension which can isolate a large percentage of failures, including unforeseen failures, with minimal increases in code size. Adapting this digital technique to an electrical power system required us to address several key issues including model construction and fidelity. This paper contrasts several approaches to model-based diagnostics to our diagnostic system, Marple, which has been demonstrated on a power testbed.

  18. Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters

    NASA Astrophysics Data System (ADS)

    Lu, Yunsong; Wang, Fuli; Jia, Mingxing; Qi, Yuanchen

    2016-12-01

    This paper concerns fault diagnosis of centrifugal compressor based on thermal parameters. An improved qualitative simulation (QSIM) based fault diagnosis method is proposed to diagnose the faults of centrifugal compressor in a gas-steam combined-cycle power plant (CCPP). The qualitative models under normal and two faulty conditions have been built through the analysis of the principle of centrifugal compressor. To solve the problem of qualitative description of the observations of system variables, a qualitative trend extraction algorithm is applied to extract the trends of the observations. For qualitative states matching, a sliding window based matching strategy which consists of variables operating ranges constraints and qualitative constraints is proposed. The matching results are used to determine which QSIM model is more consistent with the running state of system. The correct diagnosis of two typical faults: seal leakage and valve stuck in the centrifugal compressor has validated the targeted performance of the proposed method, showing the advantages of fault roots containing in thermal parameters.

  19. The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

    PubMed

    Roh, S D; Kim, S W; Cho, W S

    2001-10-01

    The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

  20. 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.

  1. Blood-based protein biomarkers for diagnosis of Alzheimer disease.

    PubMed

    Doecke, James D; Laws, Simon M; Faux, Noel G; Wilson, William; Burnham, Samantha C; Lam, Chiou-Peng; Mondal, Alinda; Bedo, Justin; Bush, Ashley I; Brown, Belinda; De Ruyck, Karl; Ellis, Kathryn A; Fowler, Christopher; Gupta, Veer B; Head, Richard; Macaulay, S Lance; Pertile, Kelly; Rowe, Christopher C; Rembach, Alan; Rodrigues, Mark; Rumble, Rebecca; Szoeke, Cassandra; Taddei, Kevin; Taddei, Tania; Trounson, Brett; Ames, David; Masters, Colin L; Martins, Ralph N

    2012-10-01

    To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD). Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data. General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve. This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.

  2. Design and implementation of virtual fault diagnosis system for photoelectric tracking devices based on OpenGL

    NASA Astrophysics Data System (ADS)

    Hou, MingLiang; Li, Cunhua; Zhang, Yong; Su, Liyun

    2009-10-01

    In view of the crucial deficiency of the traditional diagnosis approaches for photoelectric tracking devices and the output of more sufficient diagnosis information, in this paper, an virtual fault diagnosis system based on open graphic library(OpenGL) is proposed. Firstly, some interrelated key principles and technology of virtual reality, visualization and intelligent fault diagnosis technology are put forward. Then, the demand analysis and architecture of the system are elaborated. Next, details of interrelated essential implementation issues are also discussed, including the the 3D modeling of the related diagnosis equipments, key development process and design via OpenGL. Practical applications and experiments illuminate that the proposed approach is feasible and effective.

  3. 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

  4. Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform

    NASA Astrophysics Data System (ADS)

    Han, Jianping; Zheng, Peijuan; Wang, Hongtao

    2014-03-01

    Traditional modal parameter identification methods have many disadvantages, especially when used for processing nonlinear and non-stationary signals. In addition, they are usually not able to accurately identify the damping ratio and damage. In this study, methods based on the Hilbert-Huang transform (HHT) are investigated for structural modal parameter identification and damage diagnosis. First, mirror extension and prediction via a radial basis function (RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition (EMD), which is a crucial part of HHT. Then, the approaches based on HHT combined with other techniques, such as the random decrement technique (RDT), natural excitation technique (NExT) and stochastic subspace identification (SSI), are proposed to identify modal parameters of structures. Furthermore, a damage diagnosis method based on the HHT is also proposed. Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure. The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure. Finally, acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches. The results show that the proposed approaches based on HHT for modal parameter identification and damage diagnosis are reliable and practical.

  5. 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.

  6. Photoacoustic-based nanomedicine for cancer diagnosis and therapy.

    PubMed

    Sim, Changbeom; Kim, Haemin; Moon, Hyungwon; Lee, Hohyeon; Chang, Jin Ho; Kim, Hyuncheol

    2015-04-10

    Photoacoustic imaging is the latest promising diagnostic modality that has various advantages such as high spatial resolution, deep penetration depth, and use of non-ionizing radiation. It also employs a non-invasive imaging technique and optically functionalized imaging. The goal of this study was to develop a nanomedicine for simultaneous cancer therapy and diagnosis based on photoacoustic imaging. Human serum albumin nanoparticles loaded with melanin and paclitaxel (HMP-NPs) were developed using the desolvation technique. The photoacoustic-based diagnostic and chemotherapeutic properties of HMP-NPs were evaluated through in vitro and in vivo experiments. The size and zeta potential of the HMP-NPs were found to be 192.8±21.11nm and -22.2±4.39mV, respectively. In in vitro experiments, HMP-NPs produced increased photoacoustic signal intensity because of the loaded melanin and decreased cellular viability because of the encapsulated paclitaxel, compared to the free human serum albumin nanoparticles (the control). In vivo experiments showed that the HMP-NPs efficiently accumulated inside the tumor, resulting in the enhanced photoacoustic signal intensity in the tumor site, compared to the normal tissues. The in vivo chemotherapy study demonstrated that HMP-NPs had the capability to treat cancer for an extended period. In conclusion, HMP-NPs were simultaneously capable of photoacoustic diagnostic and chemotherapy against cancer.

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

    PubMed

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

    2014-04-01

    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.

  8. Assessment of the White Salmon watershed using the ecosystem diagnosis and treatment model

    USGS Publications Warehouse

    Allen, Brady; Connolly, Patrick J.

    2005-01-01

    Salmon habitat models provide managers the ability to identify habitat limitations and prioritize restoration activities. Ecosystem Diagnosis and Treatment (EDT) has become a widely used tool for salmonid habitat analysis in the Pacific Northwest. The EDT model is a rule-based habitat rating system that provides reach-level diagnosis of habitat conditions for the major salmonid species of the Pacific Northwest. The EDT process itself is a complex modeling program with defined data needs. The program is a product developed by Mobrand Biometrics Incorporated (MBI) largely through funding by the Northwest Power and Conservation Council (NPCC). The NPCC had provided a free version of the program accessible through a website that required user registration. The EDT model allows the user to rate the quality, quantity, and diversity of fish habitat along a waterway. The model uses diagnostic species such as steelhead and Chinook salmon to identify the most significant limiting factors in a river and to help identify reaches for protection and restoration. The model includes a set of tools to help organize environmental information and rate the habitat elements that pertain to specific life stages of the diagnostic species. A major benefit of EDT is that it can show the potential of a river under current conditions and possible future conditions. The result is a scientifically-based assessment of fish habitat and a prioritization of restoration needs.

  9. Model-Based Sensor Selection for Helicopter Gearbox Monitoring

    DTIC Science & Technology

    1996-04-01

    fault diagnosis of helicopter gearboxes is therefore necessary to prevent major breakdowns due to progression of undetected...in the gearbox . Once the presence of a fault is prompted by the fault detection network, fault diagnosis is performed by the Structure-Based...Components Figure 3: Overview of fault detection and diagnosis in the proposed model-based di- agnostic system for helicopter gearboxes . the OH-58A gearbox

  10. Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

    PubMed Central

    Scott, I; Youlden, D; Coory, M

    2004-01-01

    Background: Hospital performance reports based on administrative data should distinguish differences in quality of care between hospitals from case mix related variation and random error effects. A study was undertaken to determine which of 12 diagnosis-outcome indicators measured across all hospitals in one state had significant risk adjusted systematic (or special cause) variation (SV) suggesting differences in quality of care. For those that did, we determined whether SV persists within hospital peer groups, whether indicator results correlate at the individual hospital level, and how many adverse outcomes would be avoided if all hospitals achieved indicator values equal to the best performing 20% of hospitals. Methods: All patients admitted during a 12 month period to 180 acute care hospitals in Queensland, Australia with heart failure (n = 5745), acute myocardial infarction (AMI) (n = 3427), or stroke (n = 2955) were entered into the study. Outcomes comprised in-hospital deaths, long hospital stays, and 30 day readmissions. Regression models produced standardised, risk adjusted diagnosis specific outcome event ratios for each hospital. Systematic and random variation in ratio distributions for each indicator were then apportioned using hierarchical statistical models. Results: Only five of 12 (42%) diagnosis-outcome indicators showed significant SV across all hospitals (long stays and same diagnosis readmissions for heart failure; in-hospital deaths and same diagnosis readmissions for AMI; and in-hospital deaths for stroke). Significant SV was only seen for two indicators within hospital peer groups (same diagnosis readmissions for heart failure in tertiary hospitals and inhospital mortality for AMI in community hospitals). Only two pairs of indicators showed significant correlation. If all hospitals emulated the best performers, at least 20% of AMI and stroke deaths, heart failure long stays, and heart failure and AMI readmissions could be avoided

  11. 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.

  12. Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis.

    PubMed

    Lu, Chuan; Devos, Andy; Suykens, Johan A K; Arús, Carles; Van Huffel, Sabine

    2007-05-01

    This paper investigates variable selection (VS) and classification for biomedical datasets with a small sample size and a very high input dimension. The sequential sparse Bayesian learning methods with linear bases are used as the basic VS algorithm. Selected variables are fed to the kernel-based probabilistic classifiers: Bayesian least squares support vector machines (BayLS-SVMs) and relevance vector machines (RVMs). We employ the bagging techniques for both VS and model building in order to improve the reliability of the selected variables and the predictive performance. This modeling strategy is applied to real-life medical classification problems, including two binary cancer diagnosis problems based on microarray data and a brain tumor multiclass classification problem using spectra acquired via magnetic resonance spectroscopy. The work is experimentally compared to other VS methods. It is shown that the use of bagging can improve the reliability and stability of both VS and model prediction.

  13. Knowledge-based system for structured examination, diagnosis and therapy in treatment of traumatised teeth.

    PubMed

    Robertson, A; Norén, J G

    2001-02-01

    Dental trauma in children and adolescents is a common problem, and the prevalence of these injuries has increased in the last 10-20 years. A dental injury should always be considered an emergency and, thus, be treated immediately to relieve pain, facilitate reduction of displaced teeth, reconstruct lost hard tissue, and improve prognosis. Rational therapy depends upon a correct diagnosis, which can be achieved with the aid of various examination techniques. It must be understood that an incomplete examination can lead to inaccurate diagnosis and less successful treatment. Good knowledge of traumatology and models of treatments can also reduce stress and anxiety for both the patient and the dental team. Knowledge-based Systems (KBS) are a practical implementation of Artificial Intelligence. In complex domains which humans find difficult to understand, KBS can assist in making decisions and can also add knowledge. The aim of this paper is to describe the structure of a knowledge-based system for structured examination, diagnosis and therapy for traumatised primary and permanent teeth. A commercially available program was used as developmental tool for the programming (XpertRule, Attar, London, UK). The paper presents a model for a computerised decision support system for traumatology.

  14. [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.

  15. Metformin and breast cancer stage at diagnosis: a population-based study

    PubMed Central

    Lega, I.C.; Fung, K.; Austin, P.C.; Lipscombe, L.L.

    2017-01-01

    Purpose The objective of the present study was to use a large, population-based cohort to examine the association between metformin and breast cancer stage at diagnosis while accounting for mammography differences. Methods We used data from Ontario administrative health databases to identify women 68 years of age or older with diabetes and invasive breast cancer diagnosed from 1 January 2007 to 31 December 2012. Adjusted logistic regression models were used to compare breast cancer stage at diagnosis (stages i and ii vs. iii and iv) between the women exposed and not exposed to metformin. We also examined the association between metformin use and estrogen receptor status, tumour size, and lymph node status in the subset of women for whom those data were available. Results We identified 3125 women with diabetes and breast cancer; 1519 (48.6%) had been exposed to metformin before their cancer diagnosis. Median age at breast cancer diagnosis was 76 years (interquartile range: 72–82 years), and mean duration of diabetes was 8.8 ± 5.9 years. In multivariable analyses, metformin exposure was not associated with an earlier stage of breast cancer (odds ratio: 0.98; 95% confidence interval: 0.81 to 1.19). In secondary analyses, metformin exposure was not associated with estrogen receptor–positive breast cancer, tumours larger than 2 cm, or positive lymph nodes. Conclusions This population-based study did not show an association between metformin use and breast cancer stage or tumour characteristics at diagnosis. Our study considered older women with long-standing diabetes, and therefore further studies in younger patients could be warranted. PMID:28490930

  16. In vitro strain measurements in cerebral aneurysm models for cyber-physical diagnosis.

    PubMed

    Shi, Chaoyang; Kojima, Masahiro; Anzai, Hitomi; Tercero, Carlos; Ikeda, Seiichi; Ohta, Makoto; Fukuda, Toshio; Arai, Fumihito; Najdovski, Zoran; Negoro, Makoto; Irie, Keiko

    2013-06-01

    The development of new diagnostic technologies for cerebrovascular diseases requires an understanding of the mechanism behind the growth and rupture of cerebral aneurysms. To provide a comprehensive diagnosis and prognosis of this disease, it is desirable to evaluate wall shear stress, pressure, deformation and strain in the aneurysm region, based on information provided by medical imaging technologies. In this research, we propose a new cyber-physical system composed of in vitro dynamic strain experimental measurements and computational fluid dynamics (CFD) simulation for the diagnosis of cerebral aneurysms. A CFD simulation and a scaled-up membranous silicone model of a cerebral aneurysm were completed, based on patient-specific data recorded in August 2008. In vitro blood flow simulation was realized with the use of a specialized pump. A vision system was also developed to measure the strain at different regions on the model by way of pulsating blood flow circulating inside the model. Experimental results show that distance and area strain maxima were larger near the aneurysm neck (0.042 and 0.052), followed by the aneurysm dome (0.023 and 0.04) and finally the main blood vessel section (0.01 and 0.014). These results were complemented by a CFD simulation for the addition of wall shear stress, oscillatory shear index and aneurysm formation index. Diagnosis results using imaging obtained in August 2008 are consistent with the monitored aneurysm growth in 2011. The presented study demonstrates a new experimental platform for measuring dynamic strain within cerebral aneurysms. This platform is also complemented by a CFD simulation for advanced diagnosis and prediction of the growth tendency of an aneurysm in endovascular surgery. Copyright © 2013 John Wiley & Sons, Ltd.

  17. 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…

  18. 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…

  19. 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

    2016-08-22

    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.

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

    PubMed Central

    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

  1. 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.

  2. A new damage diagnosis approach for NC machine tools based on hybrid Stationary subspace analysis

    NASA Astrophysics Data System (ADS)

    Gao, Chen; Zhou, Yuqing; Ren, Yan

    2017-05-01

    This paper focused on the damage diagnosis for NC machine tools and put forward a damage diagnosis method based on hybrid Stationary subspace analysis (SSA), for improving the accuracy and visibility of damage identification. First, the observed single sensor signal was reconstructed to multi-dimensional signals by the phase space reconstruction technique, as the inputs of SSA. SSA method was introduced to separate the reconstructed data into stationary components and non-stationary components without the need for independency and prior information of the origin signals. Subsequently, the selected non-stationary components were analysed for training LS-SVM (Least Squares Support Vector Machine) classifier model, in which several statistic parameters in the time and frequency domains were exacted as the sample of LS-SVM. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy of the proposed approach.

  3. Evidence-based diagnosis and management of acute bronchitis.

    PubMed

    Hart, Ann Marie

    2014-09-18

    Acute bronchitis is a common respiratory infection seen in primary care settings. This article examines the current evidence for diagnosis and management of acute bronchitis in adults and provides recommendations for primary care clinical practice.

  4. 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.

  5. Targeted leukodystrophy diagnosis based on charges and yields for testing.

    PubMed

    Richards, Jackson; Korgenski, E Kent; Taft, Ryan J; Vanderver, Adeline; Bonkowsky, Joshua L

    2015-11-01

    Inherited leukodystrophies are a group of neurological disorders with significant morbidity and mortality. Children and their families can experience lengthy diagnostic odysseys; however, there is no data on the charges related to testing for diagnosis in leukodystrophy patients, compared to approaches using next-generation sequencing (NGS). Our objective was to determine charges related to the determination of diagnosis, and overall yield of diagnostic testing, for leukodystrophy patients. We determined and quantified all inpatient and outpatient lab testing, including brain MRIs, obtained for the purpose of diagnosis, in a retrospective population cohort of children with inherited leukodystrophies. Each patient had average charges of $8,231 (range $543-26,437) for diagnostic testing. Overall charges related to diagnosis for the entire cohort was $526,794. A final etiological diagnosis was determined in 34% of patients. In those in whom a specific diagnosis was determined, average time to diagnosis was 1.4 years. If NGS on the entire cohort had been performed instead, charges would have been ∼$359,600 (at $5,800/patient). Alternatively, a two-tier approach consisting of first, biochemical testing (serum very-long chain fatty acids and leukocyte lysosomal enzyme testing), and then with NGS for remaining undiagnosed patients, would have resulted in total cohort charges of $361,309. We have determined the charges directly associated with diagnostic testing in a population cohort of children with leukodystrophy. We conclude that appropriately incorporating NGS into diagnostic algorithms could lower charges; reduce time to diagnosis; and reduce amount of testing. © 2015 Wiley Periodicals, Inc.

  6. 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.

  7. Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems

    DTIC Science & Technology

    2008-04-01

    bearing fault diagnosis – their effectiveness and flexibilities. Journal of Vibration and Acoustics July 2001, ASME. 3. Staszewski, W. J. Structural...Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems by Hiralal Khatri, Kenneth Ranney, Kwok Tom, and Romeo...Laboratory Adelphi, MD 20783-1197 ARL-TR-4301 April 2008 Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems

  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. Cloud diagnosis impact on deposition modelling applied to the Fukushima accident

    NASA Astrophysics Data System (ADS)

    Quérel, Arnaud; Quélo, Denis; Roustan, Yelva; Mathieu, Anne

    2017-04-01

    The accident at the Fukushima Daiichi Nuclear Power Plant in Japan in March 2011 resulted in the release of several hundred PBq of activity into the environment. Most of the radioactivity was released in a time period of about 40 days. Radioactivity was dispersed in the atmosphere and the ocean and subsequently traces of radionuclides were detected all over Japan. At the Fukushima airport for instance, a deposit as large as 36 kBq/m2 of Cs-137 was measured resulting of an atmospheric deposition of the plume. Both dry and wet deposition were probably involved since a raining event occurred on the 15th of March when the plume was passing nearby. The accident scenario have given rise to a number of scientific investigations. Atmospheric deposition, for example, was studied by utilizing atmospheric transport models. In atmospheric transport models, some parameters, such as cloud diagnosis, are derived from meteorological data. This cloud diagnosis is a key issue for wet deposition modelling since it allows to distinguish between two processes: in-cloud scavenging which corresponds to the collection of radioactive particles into the cloud and below-cloud scavenging consequent to the removal of radioactive material due to the falling drops. Several parametrizations of cloud diagnosis exist in the literature, using different input data: relative humidity, liquid water content, also. All these diagnosis return a large range of cloud base heights and cloud top heights. In this study, computed cloud diagnostics are compared to the observations at the Fukushima airport. Atmospheric dispersion simulations at Japan scale are then performed utilizing the most reliable ones. Impact on results are discussed.

  10. Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging.

    PubMed

    Choi, Hongyoon; Ha, Seunggyun; Im, Hyung Jun; Paek, Sun Ha; Lee, Dong Soo

    2017-01-01

    Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD), an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies.

  11. Integration of Symptom Ratings from Multiple Informants in ADHD Diagnosis: A Psychometric Model with Clinical Utility

    PubMed Central

    Martel, Michelle M.; Schimmack, Ulrich; Nikolas, Molly; Nigg, Joel T.

    2015-01-01

    The Diagnostic and Statistical Manual of Mental Disorder—Fifth Edition explicitly requires that Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms should be apparent across settings, taking into account reports from multiple informants. Yet, it provides no guidelines how information from different raters should be combined in ADHD diagnosis. We examined the validity of different approaches using structural equation modeling (SEM) for multiple-informant data. Participants were 725 children, 6 to 17 years old, and their primary caregivers and teachers, recruited from the community and completing a thorough research-based diagnostic assessment, including a clinician-administered diagnostic interview, parent and teacher standardized rating scales and cognitive testing. A best-estimate ADHD diagnosis was generated by a diagnostic team. An SEM model demonstrated convergent validity among raters. We found relatively weak symptom-specific agreement among raters, suggesting that a general average scoring algorithm is preferable to symptom-specific scoring algorithms such as the “or” and “and” algorithms. Finally, to illustrate the validity of this approach, we show that averaging makes it possible to reduce the number of items from 18 items to 8 items without a significant decrease in validity. In conclusion, information from multiple raters increases the validity of ADHD diagnosis, and averaging appears to be the optimal way to integrate information from multiple raters. PMID:25730162

  12. Integration of symptom ratings from multiple informants in ADHD diagnosis: a psychometric model with clinical utility.

    PubMed

    Martel, Michelle M; Schimmack, Ulrich; Nikolas, Molly; Nigg, Joel T

    2015-09-01

    The Diagnostic and Statistical Manual of Mental Disorder-Fifth Edition explicitly requires that attention-deficit/hyperactivity disorder (ADHD) symptoms should be apparent across settings, taking into account reports from multiple informants. Yet, it provides no guidelines how information from different raters should be combined in ADHD diagnosis. We examined the validity of different approaches using structural equation modeling (SEM) for multiple-informant data. Participants were 725 children, 6 to 17 years old, and their primary caregivers and teachers, recruited from the community and completing a thorough research-based diagnostic assessment, including a clinician-administered diagnostic interview, parent and teacher standardized rating scales, and cognitive testing. A best-estimate ADHD diagnosis was generated by a diagnostic team. An SEM model demonstrated convergent validity among raters. We found relatively weak symptom-specific agreement among raters, suggesting that a general average scoring algorithm is preferable to symptom-specific scoring algorithms such as the "or" and "and" algorithms. Finally, to illustrate the validity of this approach, we show that averaging makes it possible to reduce the number of items from 18 items to 8 items without a significant decrease in validity. In conclusion, information from multiple raters increases the validity of ADHD diagnosis, and averaging appears to be the optimal way to integrate information from multiple raters. (c) 2015 APA, all rights reserved.

  13. 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…

  14. A general proof of consistency of heuristic classification for cognitive diagnosis models.

    PubMed

    Chiu, Chia-Yi; Köhn, Hans-Friedrich

    2015-11-01

    The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633-665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non-parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non-parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.

  15. Saliency based ulcer detection for wireless capsule endoscopy diagnosis.

    PubMed

    Yuan, Yixuan; Wang, Jiaole; Li, Baopu; Meng, Max Q-H

    2015-10-01

    Ulcer is one of the most common symptoms of many serious diseases in the human digestive tract. Especially for the ulcers in the small bowel where other procedures cannot adequately visualize, wireless capsule endoscopy (WCE) is increasingly being used in the diagnosis and clinical management. Because WCE generates large amount of images from the whole process of inspection, computer-aided detection of ulcer is considered an indispensable relief to clinicians. In this paper, a two-staged fully automated computer-aided detection system is proposed to detect ulcer from WCE images. In the first stage, we propose an effective saliency detection method based on multi-level superpixel representation to outline the ulcer candidates. To find the perceptually and semantically meaningful salient regions, we first segment the image into multi-level superpixel segmentations. Each level corresponds to different initial region sizes of the superpixels. Then we evaluate the corresponding saliency according to the color and texture features in superpixel region of each level. In the end, we fuse the saliency maps from all levels together to obtain the final saliency map. In the second stage, we apply the obtained saliency map to better encode the image features for the ulcer image recognition tasks. Because the ulcer mainly corresponds to the saliency region, we propose a saliency max-pooling method integrated with the Locality-constrained Linear Coding (LLC) method to characterize the images. Experiment results achieve promising 92.65% accuracy and 94.12% sensitivity, validating the effectiveness of the proposed method. Moreover, the comparison results show that our detection system outperforms the state-of-the-art methods on the ulcer classification task.

  16. 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

  17. 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

  18. Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction.

    PubMed

    Warner, Grace; Hoenig, Helen; Montez, Maria; Wang, Fei; Rosen, Amy

    2004-02-01

    To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction. Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2. Models were replicated in 3 populations. Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888). Not applicable. Inpatient, outpatient, and total days of care in FY97. The DCG models (R(2) range,.22-.38) performed better than ACG models (R(2) range,.04-.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model (R(2) range for ACG,.14-.34; R(2) range for DCG,.24-.38). Information on self-care function slightly improved performance (R(2) range increased from 0 to.04). The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.

  19. 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

    2015-01-01

    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.

  20. Modelling diagnosis in physical therapy: a blackboard framework and models of experts and novices.

    PubMed

    James, G A

    2007-03-01

    The primary objective of this study was to explore clinical reasoning in physical therapy and to highlight the similarities and differences by modelling the diagnostic phase of clinical reasoning. An experimental design comparing expert and novice physical therapists was utilized. Concurrent verbal protocols detailing the clinical reasoning about standardized case material were elicited. A framework for modelling diagnosis was specified and provided the parameters for analysis. The diagnostic utterances were classified as cues or hypotheses and the knowledge utilized was identified. The experts recruited significantly more knowledge than the novices (p = 0.01) and used more cues (p < 0.01). Their diagnoses were more accurate when compared to the original diagnosis. This difference between the experts and novices was reflected in the differences shown in the models (p < 0.01). The differences between these subjects focused upon the knowledge recruitment, which impacted on the accuracy of the diagnosis. The novices' inaccurate or non-existent diagnoses led to poor quality of treatment prescription. Modelling proved to be a useful way of representing these differences.

  1. Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

    PubMed

    Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting

    2014-01-01

    This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

  2. 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

  3. 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

  4. 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

  5. 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.

  6. Diagnosis of GLDAS LSM based aridity index and dryland identification.

    PubMed

    Ghazanfari, Sadegh; Pande, Saket; Hashemy, Mehdy; Sonneveld, Ben

    2013-04-15

    The identification of dryland areas is crucial for guiding policy aimed at intervening in water-stressed areas and addressing the perennial livelihood or food insecurity of these areas. However, the prevailing aridity indices (such as UNEP aridity index) have methodological limitations that restrict their use in delineating drylands and may be insufficient for decision-making frameworks. In this study, we propose a new aridity index based on based on 3 decades of soil moisture time series by accounting for site-specific soil and vegetation that partitions precipitation into the competing demands of evaporation and runoff. Our proposed aridity index is the frequency at which the dominant soil moisture value at a location is not exceeded by the dominant soil moisture values in all of the other locations. To represent the dominant spatial template of the soil moisture conditions, we extract the first eigenfunction from the empirical orthogonal function (EOF) analysis from 3 GLDAS land surface models (LSMs): VIC, MOSAIC and NOAH at 1 × 1 degree spatial resolution. The EOF analysis reveals that the first eigenfunction explains 33%, 43% and 47% of the VIC, NOAH and MOSAIC models, respectively. We compare each LSM aridity indices with the UNEP aridity index, which is created based on LSM data forcings. The VIC aridity index displays a pattern most closely resembling that of UNEP, although all of the LSM-based indices accurately isolate the dominant dryland areas. The UNEP classification identifies portions of south-central Africa, southeastern United States and eastern India as drier than predicted by all of the LSMs. The NOAH and MOSAIC LSMs categorize portions of southwestern Africa as drier than the other two classifications, while all of the LSMs classify portions of central India as wetter than the UNEP classification. We compare all aridity maps with the long-term average NDVI values. Results show that vegetation cover in areas that the UNEP index classifies as

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

    PubMed Central

    Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Augelli, Raffaele; Dallaserra, Chiara; Puntel, Gino; Rossi, Arianna; Sala, Giuseppe; Signorini, Manuel; Spezia, Laura; Zamboni, Federico; Montemezzi, Stefania

    2016-01-01

    The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters’ predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization. PMID:27648166

  12. 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…

  13. 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…

  14. 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.

  15. Sensor-based diagnosis using knowledge of structure and function

    NASA Technical Reports Server (NTRS)

    Scarl, Ethan A.; Jamieson, John R.; Delaune, Carl I.

    1987-01-01

    A system for fault detection and isolation called LES, developed at the Kennedy Space Center for the Space Shuttle's Launch Processing System, is a well-developed diagnostic system that is simultaneously model-based and sensor-based. This experiment has led to a surprising result: the failure of a sensor can not only be handled in precisely the same way as the failure of any other object, but may present an especially easy case. Classical rule-based diagnostic systems need to find out whether or not their sensors are telling them the truth before they can safely draw inferences from them. By contrast, while LES does treat sensors as a special case, it does so only because there may exist a short cut that allows them to be handled more simply than other objects. LES uses both structural and functional knowledge, and has found cases in which the structural knowledge can be economically replaced by the judicious use of functional relationships; LES' functional relationships are stored in exactly one place, so they must be inverted to determine hypothetical values for possibly faulty objects. The inversion process has been extended to include conditional functions not normally considered to have inverses.

  16. A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

    NASA Technical Reports Server (NTRS)

    Warner, James E.; Hochhalter, Jacob D.; Leser, William P.; Leser, Patrick E.; Newman, John A

    2016-01-01

    This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

  17. Design and Diagnosis Problem Solving with Multifunctional Technical Knowledge Bases

    DTIC Science & Technology

    1992-09-29

    to disease processes to etiologies. In speech comprehension, for example, the layers might be acoustic signals to phonetics to grammar and clauses etc...Computational Linguistics , Buffalo, NY, pp. 95-103. Josephson, J. R.,1982, Explanation and Induction, PhD thesis, The Ohio State University, Columbus, Ohio...Joint Conference on Arti- ficial Intelligence. 1989 17. Harvey, A. M., Bordley, 3. 111: Differential Diagnosis, the Interpretation of Clinical Evidence

  18. Fault Diagnosis and Prognosis Based on Lebesgue Sampling

    DTIC Science & Technology

    2014-10-02

    Vachtsevanos, 2001; Jardine, Lin, & Ban- jevic, 2006), prognosis is more challenging (Schwabacher & Goebel, 2007; Vachtsevanos et al., 2006; Edwards, Orchard ...Goebel, 2009; Orchard & Vachtse- vanos, 2009). For diagnosis, the matrices are often related to false alarm rate, probability of detection, etc. For...Freitas, & Poole, 2002; de Freitas, 2002; Zhang et al., 2010; Zhang, Sconyers, et al., 2009; Orchard , Hevia-Koch, Zhang, & Tang, 2013). The output of

  19. Analyzing reliability of seizure diagnosis based on semiology.

    PubMed

    Jin, Bo; Wu, Han; Xu, Jiahui; Yan, Jianwei; Ding, Yao; Wang, Z Irene; Guo, Yi; Wang, Zhongjin; Shen, Chunhong; Chen, Zhong; Ding, Meiping; Wang, Shuang

    2014-12-01

    This study aimed to determine the accuracy of seizure diagnosis by semiological analysis and to assess the factors that affect diagnostic reliability. A total of 150 video clips of seizures from 50 patients (each with three seizures of the same type) were observed by eight epileptologists, 12 neurologists, and 20 physicians (internists). The videos included 37 series of epileptic seizures, eight series of physiologic nonepileptic events (PNEEs), and five series of psychogenic nonepileptic seizures (PNESs). After observing each video, the doctors chose the diagnosis of epileptic seizures or nonepileptic events for the patient; if the latter was chosen, they further chose the diagnosis of PNESs or PNEEs. The overall diagnostic accuracy rate for epileptic seizures and nonepileptic events increased from 0.614 to 0.660 after observations of all three seizures (p < 0.001). The diagnostic sensitivity and specificity of epileptic seizures were 0.770 and 0.808, respectively, for the epileptologists. These values were significantly higher than those for the neurologists (0.660 and 0.699) and physicians (0.588 and 0.658). A wide range of diagnostic accuracy was found across the various seizures types. An accuracy rate of 0.895 for generalized tonic-clonic seizures was the highest, followed by 0.800 for dialeptic seizures and then 0.760 for automotor seizures. The accuracy rates for myoclonic seizures (0.530), hypermotor seizures (0.481), gelastic/dacrystic seizures (0.438), and PNESs (0.430) were poor. The reliability of semiological diagnosis of seizures is greatly affected by the seizure type as well as the doctor's experience. Although the overall reliability is limited, it can be improved by observing more seizures.

  20. 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…

  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. Molecular diagnosis of Acanthamoeba keratitis: evaluation in rat model and application in suspected human cases.

    PubMed

    Costa, Adriana Oliveira; Furst, Cinthia; Rocha, Lucas Oliveira; Cirelli, Cecília; Cardoso, Carolina Neris; Neiva, Fagner Salmazo; Possamai, Cynara Oliveira; de Assis Santos, Daniel; Thomaz-Soccol, Vanete

    2017-04-01

    Acanthamoeba keratitis (AK) is a progressive corneal infection that demands rapid and sensitive techniques for diagnosis to avoid risk of visual impairment. We evaluated two DNA extraction techniques and a semi-nested-PCR (snPCR) targeting the 18S rRNA gene to detect Acanthamoeba cysts and trophozoites. The most effective protocol was evaluated in samples of corneal scrapings and biopsies from an AK rat model and applied to diagnosis of human cases of AK. DNA extraction performed with a commercial kit based on DNA binding to magnetic beads was more efficient than a method based on alkaline lysis, allowing the detection of one trophozoite and one cyst of Acanthamoeba in samples prepared from cultures. This technique and sn-PCR were applied in corneal scrapings of rats experimentally infected with Acanthamoeba (n = 6), resulting in 100% of positivity, against 16.7% (n = 6) of positive identification in culture method using non-nutrient agar (NNA) with Escherichia coli. Corneal biopsies from rats were also tested (n = 6) and resulted in positivity in all samples in both molecular and culture methods. Eight out of ten presumptive human cases of Acanthamoeba keratitis were also confirmed by sn-PCR of scrapping samples, while the culture method was positive in only four cases. We discuss that animal model of AK can be an efficient tool to validate diagnostic methods and conclude that DNA extraction with the kit and snPCR protocol described here is an effective alternative for diagnosis of AK.

  3. Kinetic analyses and performance of a colloidal magnetic nanoparticle based immunoassay dedicated to allergy diagnosis.

    PubMed

    Teste, Bruno; Kanoufi, Frédéric; Descroix, Stéphanie; Poncet, Pascal; Georgelin, Thomas; Siaugue, Jean-Michel; Petr, Jan; Varenne, Anne; Hennion, Marie-Claire

    2011-07-01

    In this paper, we demonstrate the possibility to use magnetic nanoparticles as immunosupports for allergy diagnosis. Most immunoassays used for immunosupports and clinical diagnosis are based on a heterogeneous solid-phase system and suffer from mass-transfer limitation. The nanoparticles' colloidal behavior and magnetic properties bring the advantages of homogeneous immunoassay, i.e., species diffusion, and of heterogeneous immunoassay, i.e., easy separation of the immunocomplex and free forms, as well as analyte preconcentration. We thus developed a colloidal, non-competitive, indirect immunoassay using magnetic core-shell nanoparticles (MCSNP) as immunosupports. The feasibility of such an immunoassay was first demonstrated with a model antibody and described by comparing the immunocapture kinetics using macro (standard microtiter plate), micro (microparticles) and nanosupports (MCSNP). The influence of the nanosupport properties (surface chemistry, antigen density) and of the medium (ionic strength, counter ion nature) on the immunocapture efficiency and specificity was then investigated. The performances of this original MCSNP-based immunoassay were compared with a gold standard enzyme-linked immunosorbent assay (ELISA) using a microtiter plate. The capture rate of target IgG was accelerated 200-fold and a tenfold lower limit of detection was achieved. Finally, the MCSNP-based immunoassay was successfully applied to the detection of specific IgE from milk-allergic patient's sera with a lower LOD and a good agreement (CV < 6%) with the microtiter plate, confirming the great potential of this analytical platform in the field of immunodiagnosis.

  4. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis

    PubMed Central

    Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L.; Hwang, Jae Youn

    2016-01-01

    We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis. PMID:28018743

  5. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis.

    PubMed

    Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L; Hwang, Jae Youn

    2016-12-01

    We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.

  6. Convex Models of Malfunction Diagnosis in High Performance Aircraft

    DTIC Science & Technology

    1989-05-01

    initiated as in the open-loop mode: with one fixed non -zero control function. The time-dependent controller is actuated as soon as any of the state ... controllers ) the diagnosis algorithm is designed by solving 8 CONCLUI)ING REMARKS AND FUTURE RESEARCH 70 a sequence of linear optimization problems . For...Automatic Controller ............... 8 3.3 Numerical Demonstration of the Normal Dynamics ............ 8 4 Representing Control - Actuator Failure 16 5 Convex

  7. 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.

  8. A novel excitation-emission wavelength model to facilitate the diagnosis of urinary bladder diseases

    NASA Astrophysics Data System (ADS)

    Rafailov, Ilya; Palmer, Scott; Litvinova, Karina; Dremin, Victor; Dunaev, Andrey V.; Nabi, Ghulam

    2015-02-01

    Diseases of urinary bladder are a common healthcare problem world over. Diagnostic precision and predicting response to treatment are major issues. This study aims to create an optical cross-sectionional model of a bladder, capable of visually representing the passage of photons through the tissue layers. The absorption, transmission and reflectance data, along with the derived transmission coefficients (of scattering and absorption) were obtained from literature analysis and were used in the creation of a "generic" cross-section optical property model simulating the passage of thousands of photons through the tissue at different wavelengths. Fluorescence spectra of diagnostically relevant biomarkers excited by the UV and blue wavelengths were modelled on the basis of the Monte-Carlo method. Further to this, fluorescence data gathered by the "LAKK-M" system from pig bladders was applied to the model for a specific representation of the photon passage through the tissues. The ultimate goal of this study is to employ this model to simulate the effects of different laser wavelength and energy inputs to bladder tissue and to determine the effectiveness of potential photonics based devices for the diagnosis of bladder pathologies. The model will aid in observing differences between healthy and pathological bladder tissues registered by photonics based devices.

  9. 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.

  10. Personalized Clinical Diagnosis in Data Bases for Treatment Support in Phthisiology.

    PubMed

    Lugovkina, T K; Skornyakov, S N; Golubev, D N; Egorov, E A; Medvinsky, I D

    2016-01-01

    The decision-making is a key event in the clinical practice. The program products with clinical decision support models in electronic data-base as well as with fixed decision moments of the real clinical practice and treatment results are very actual instruments for improving phthisiological practice and may be useful in the severe cases caused by the resistant strains of Mycobacterium tuberculosis. The methodology for gathering and structuring of useful information (critical clinical signals for decisions) is described. Additional coding of clinical diagnosis characteristics was implemented for numeric reflection of the personal situations. The created methodology for systematization and coding Clinical Events allowed to improve the clinical decision models for better clinical results.

  11. A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis

    PubMed Central

    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

  12. 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.

  13. Bayesian network based on a fault tree and its application in diesel engine fault diagnosis

    NASA Astrophysics Data System (ADS)

    Qian, Gang; Zheng, Shengguo; Cao, Longhan

    2005-12-01

    This paper discusses the faults diagnosis of diesel engine systems. This research aims at the optimization of the diagnosis results. Inspired by Bayesian Network (BN) possessing good performance in solving uncertainty problems, a new method was proposed for establishing a BN of diesel engine faults quickly, and diagnosing faults exactly. This method consisted of two stages,namely the establishment of a BN model, and a faults diagnosis of the diesel engine system using that BN mode. For the purpose of establishing the BN, a new algorithm, which can establish a BN quickly and easily, is presented. The Fault Tree (FT) diagnosis model of the diesel engine system was established first. Then it was transformed it into a BN by using our algorithm. Finally, the BN was used to diagnose the faults of a diesel engine system. Experimental results show that the diagnosis speed is increased and the accuracy is improved.

  14. 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

    2015-09-18

    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%.

  15. 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

  16. A three-stage expert system based on support vector machines for thyroid disease diagnosis.

    PubMed

    Chen, Hui-Ling; Yang, Bo; Wang, Gang; Liu, Jie; Chen, Yi-Dong; Liu, Da-You

    2012-06-01

    In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

  17. 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.

  18. Dynamic Prediction of Motor Diagnosis in Huntington's Disease Using a Joint Modeling Approach.

    PubMed

    Li, Kan; Furr-Stimming, Erin; Paulsen, Jane S; Luo, Sheng

    2017-01-01

    Prediction of motor diagnosis in Huntington's disease (HD) can be improved by incorporating other phenotypic and biological clinical measures in addition to cytosine-adenine-guanine (CAG) repeat length and age. The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion. Participants were from the PREDICT-HD study. We constructed a mixed-effect model to describe the change of measures while jointly modeling the process with time to HD diagnosis. The model was then used for subject-specific prediction. We employed the time-dependent receiver operating characteristic (ROC) method to assess the discriminating capability of the measures to identify high and low risk patients. The strongest predictor was used to illustrate the dynamic prediction of the disease risk and future trajectories of biomarkers for three hypothetical patients. 1078 individuals were included in this analysis. Five longitudinal clinical and imaging measures were compared. The putamen volume had the best discrimination performance with area under the curve (AUC) ranging from 0.74 to 0.82 over time. The total motor score showed a comparable discriminative ability with AUC ranging from 0.69 to 0.78 over time. The model showed that decreasing putamen volume was a significant predictor of motor conversion. A web-based calculator was developed for implementing the methods. By jointly modeling longitudinal data with time-to-event outcomes, it is possible to construct an individualized dynamic event prediction model that renews over time with accumulating evidence. If validated, this could be a valuable tool to guide the clinician in predicting age of onset and potentially rate of progression.

  19. Molecular diagnosis of Chagas' disease and use of an animal model to study parasite tropism.

    PubMed

    Vera-Cruz, J M; Magallón-Gastelum, E; Grijalva, G; Rincón, A R; Ramos-García, C; Armendáriz-Borunda, J

    2003-04-01

    Chagas' disease, which is an important health problem in humans, is caused by the protozoan Trypanosoma cruzi. The cellular and molecular mechanisms, involved in the selective tropism of T. cruzi to different organs remain largely unknown. In this study we designed a PCR-based molecular diagnosis method in order to study the tropism and growth kinetics of T. cruzi in a murine model infected with parasites isolated from an endemic area of Mexico. The growth kinetics and parasite tropism of T. cruzi were also evaluated in the blood and other tissues. We observed that T. cruzi isolates from the Western Mexico showed a major tropism to mouse heart and skeletal muscles in this murine model.

  20. Aided diagnosis methods of breast cancer based on machine learning

    NASA Astrophysics Data System (ADS)

    Zhao, Yue; Wang, Nian; Cui, Xiaoyu

    2017-08-01

    In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.

  1. Epilepsy: current evidence-based paradigms for diagnosis and treatment.

    PubMed

    Bates, Kimberly

    2015-06-01

    Although epilepsy has a prevalence of 5 to 7 per 1000 persons in the United States, it continues to be a poorly understood condition. Given the number of patients in the United States with epilepsy, it is very likely that primary care physicians will continue to provide care for these patients. This article refreshes some of the knowledge around the diagnosis of epilepsy, discusses special populations that may require additional management considerations, highlights the association of epilepsy with multiple comorbidities, and discusses antiepileptic drug (AED) treatment, including issues surrounding adherence and safety of AED therapy.

  2. Skull base tumor model.

    PubMed

    Gragnaniello, Cristian; Nader, Remi; van Doormaal, Tristan; Kamel, Mahmoud; Voormolen, Eduard H J; Lasio, Giovanni; Aboud, Emad; Regli, Luca; Tulleken, Cornelius A F; Al-Mefty, Ossama

    2010-11-01

    Resident duty-hours restrictions have now been instituted in many countries worldwide. Shortened training times and increased public scrutiny of surgical competency have led to a move away from the traditional apprenticeship model of training. The development of educational models for brain anatomy is a fascinating innovation allowing neurosurgeons to train without the need to practice on real patients and it may be a solution to achieve competency within a shortened training period. The authors describe the use of Stratathane resin ST-504 polymer (SRSP), which is inserted at different intracranial locations to closely mimic meningiomas and other pathological entities of the skull base, in a cadaveric model, for use in neurosurgical training. Silicone-injected and pressurized cadaveric heads were used for studying the SRSP model. The SRSP presents unique intrinsic metamorphic characteristics: liquid at first, it expands and foams when injected into the desired area of the brain, forming a solid tumorlike structure. The authors injected SRSP via different passages that did not influence routes used for the surgical approach for resection of the simulated lesion. For example, SRSP injection routes included endonasal transsphenoidal or transoral approaches if lesions were to be removed through standard skull base approach, or, alternatively, SRSP was injected via a cranial approach if the removal was planned to be via the transsphenoidal or transoral route. The model was set in place in 3 countries (US, Italy, and The Netherlands), and a pool of 13 physicians from 4 different institutions (all surgeons and surgeons in training) participated in evaluating it and provided feedback. All 13 evaluating physicians had overall positive impressions of the model. The overall score on 9 components evaluated--including comparison between the tumor model and real tumor cases, perioperative requirements, general impression, and applicability--was 88% (100% being the best possible

  3. Tracking false-negative results in molecular diagnosis: proposal of a triplex-PCR based method for leishmaniasis diagnosis

    PubMed Central

    2014-01-01

    Background Molecular biological methods have become increasingly relevant to the diagnosis and control of infectious diseases, such as leishmaniasis. Since various factors may affect the sensitivity of PCR assays, including DNA yield and purity, an optimal extraction method is pivotal. Losses of a parasite’s DNA during extraction may significantly impair its detection by PCR and lead to false-negative results. This study proposes a triplex PCR assay targeting the parasite’s DNA, an external control (pUC18) and an internal control (G3PD) for accurate diagnosis of leishmaniasis. Results Two primer pairs were designed to detect the plasmid pUC18 and a triplex PCR assay targeting the Leishmania braziliensis kinetoplast DNA, the external control and the internal control was standardized. The triplex PCR assay was assessed for its ability to detect the three target DNA fragments simultaneously. PCR products from pUC18 DNA resulted in bands of 368 (P1) and 316 (P2) base pairs (bp). The triplex PCR optimized with the chosen external control system (P1) allowed the simultaneous detection of the internal control (G3PD – 567 bp) as well as of small quantities (10 pg) of the target parasite’s DNA, detected by amplification of a 138 bp product. Conclusions The new tool standardized herein enables a more reliable interpretation of PCR results, mainly by contributing to quality assurance of leishmaniasis diagnosis. Furthermore, after simple standardization steps, this protocol could be applied to the diagnosis of other infectious diseases in reference laboratories. This triplex PCR enables the assessment of small losses during the DNA extraction process, problems concerning DNA degradation (sample quality) and the detection of L. braziliensis kDNA. PMID:24808911

  4. Common-path and compact wavefront diagnosis system based on cross grating lateral shearing interferometer.

    PubMed

    Ling, Tong; Yang, Yongying; Yue, Xiumei; Liu, Dong; Ma, Yifang; Bai, Jian; Wang, Kaiwei

    2014-10-20

    A common-path and compact wavefront diagnosis system for both continuous and transient wavefronts measurement is proposed based on cross grating lateral shearing interferometer (CGLSI). Derived from the basic CGLSI configuration, this system employs an aplanatic lens to convert the wavefront under test into a convergent beam, which makes it possible for CGLSI to test the wavefront of collimated beams. A geometrical optics model for grating pitch determination and a Fresnel diffraction model for order selection mask design are presented. Then a detailed analysis about the influence of the grating pitch, the distance from the cross grating to the order selection mask and the numerical aperture of the aplanatic lens on the system error is made, and a calibration method is proposed to eliminate the system error. In addition, the differential Zernike polynomials fitting method is introduced for wavefront retrieval. Before our experiment, we have designed several grating pitches and their corresponding order selection mask parameters. In the final comparative experiment with ZYGO interferometer, the wavefront diagnosis system exhibits both high precision and repeatability.

  5. Model-Based Improvement

    DTIC Science & Technology

    2006-10-01

    2006 4. TITLE AND SUBTITLE Model-Based Improvement 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR( S ) 5d. PROJECT...NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) Carnegie Mellon University ,Software Engineering...Institute (SEI),Pittsburgh,PA,15213 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME( S ) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S

  6. Fault diagnosis algorithm based on switching function for boost converters

    NASA Astrophysics Data System (ADS)

    Cho, H.-K.; Kwak, S.-S.; Lee, S.-H.

    2015-07-01

    A fault diagnosis algorithm, which is necessary for constructing a reliable power conversion system, should detect fault occurrences as soon as possible to protect the entire system from fatal damages resulting from system malfunction. In this paper, a fault diagnosis algorithm is proposed to detect open- and short-circuit faults that occur in a boost converter switch. The inductor voltage is abnormally kept at a positive DC value during a short-circuit fault in the switch or at a negative DC value during an open-circuit fault condition until the inductor current becomes zero. By employing these abnormal properties during faulty conditions, the inductor voltage is compared with the switching function to detect each fault type by generating fault alarms when a fault occurs. As a result, from the fault alarm, a decision is made in response to the fault occurrence and the fault type in less than two switching time periods using the proposed algorithm constructed in analogue circuits. In addition, the proposed algorithm has good resistivity to discontinuous current-mode operation. As a result, this algorithm features the advantages of low cost and simplicity because of its simple analogue circuit configuration.

  7. 2D nanomaterials based electrochemical biosensors for cancer diagnosis.

    PubMed

    Wang, Lu; Xiong, Qirong; Xiao, Fei; Duan, Hongwei

    2017-03-15

    Cancer is a leading cause of death in the world. Increasing evidence has demonstrated that early diagnosis holds the key towards effective treatment outcome. Cancer biomarkers are extensively used in oncology for cancer diagnosis and prognosis. Electrochemical sensors play key roles in current laboratory and clinical analysis of diverse chemical and biological targets. Recent development of functional nanomaterials offers new possibilities of improving the performance of electrochemical sensors. In particular, 2D nanomaterials have stimulated intense research due to their unique array of structural and chemical properties. The 2D materials of interest cover broadly across graphene, graphene derivatives (i.e., graphene oxide and reduced graphene oxide), and graphene-like nanomaterials (i.e., 2D layered transition metal dichalcogenides, graphite carbon nitride and boron nitride nanomaterials). In this review, we summarize recent advances in the synthesis of 2D nanomaterials and their applications in electrochemical biosensing of cancer biomarkers (nucleic acids, proteins and some small molecules), and present a personal perspective on the future direction of this area.

  8. [PCR-based diagnosis of mucormycosis in tissue samples].

    PubMed

    Bialek, R; Zelck, U E

    2013-11-01

    Mucormycosis is characterized by a rapid, often fatal progression. Early diagnosis of invasive mucormycosis is the key for timely therapeutic intervention and improved survival. Contrary to the more prevalent aspergillosis, effective antifungal therapy of mucormycosis is mainly limited to amphotericin B. Given the importance to guide the timely initiation of amphotericin B and possible surgical intervention, rapid and specific identification of fungal hyphae is essential. Conventional histopathology depends on abundance and morphology of the fungi as well as on the skills of the personnel, and usually shows an accuracy of 80 %. PCR assays targeting fungal ribosomal genes to identify Mucorales at least at genus level increase sensitivity, allow a rapid identification as well as detection of double mold infections. Thus, PCR assays are beneficial to complement existing approaches. They are recommended to rapidly specify tissue diagnosis and accurate identification of fungi. This will help to guide effective therapy and thereby, survival will increase. Retrospective analyses of mucormycosis by PCR help to evaluate therapeutic interventions and will optimize treatment options.

  9. GNAR-GARCH model and its application in feature extraction for rolling bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Ma, Jiaxin; Xu, Feiyun; Huang, Kai; Huang, Ren

    2017-09-01

    Given its simplicity of modeling and sensitivity to condition variations, time series model is widely used in feature extraction to realize fault classification and diagnosis. However, nonlinear and nonstationary characteristics common in fault signals of rolling bearing bring challenges to the diagnosis. In this paper, a hybrid model, the combination of a general expression for linear and nonlinear autoregressive (GNAR) model and a generalized autoregressive conditional heteroscedasticity (GARCH) model, (i.e., GNAR-GARCH), is proposed and applied to rolling bearing fault diagnosis. An exact expression of GNAR-GARCH model is given. Maximum likelihood method is used for parameter estimation and modified Akaike Information Criterion is adopted for structure identification of GNAR-GARCH model. The main advantage of this novel model over other models is that the combination makes the model suitable for nonlinear and nonstationary signals. It is verified with statistical tests that contain comparisons among the different time series models. Finally, GNAR-GARCH model is applied to fault diagnosis by modeling mechanical vibration signals including simulation and real data. With the parameters estimated and taken as feature vectors, k-nearest neighbor algorithm is utilized to realize the classification of fault status. The results show that GNAR-GARCH model exhibits higher accuracy and better performance than do other models.

  10. 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).

  11. 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).

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

    SciTech Connect

    Bhateja, Vikrant E-mail: nhuongld@hus.edu.vn; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong E-mail: nhuongld@hus.edu.vn

    2016-07-15

    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).

  13. 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.

  14. 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.

  15. [On the importance of the "decision-making model" view of diagnosis as a clinical framework in psychiatry].

    PubMed

    Ota, T

    2000-01-01

    After the advent of DSM-III, operational diagnostic criteria, along with the classification of disorders using such criteria, received considerable attention, and many studies on the reliability and validity of psychiatric diagnosis were conducted worldwide. Operational methodology was applied to diagnosis and classification, especially, in the area of research, and has contributed greatly to advances in reliable and refined clinical research. Such methodology, however, has not necessarily been accepted as a guiding principle in the area of clinical practice by all psychiatrists. Rather, some psychiatrists, especially more experienced psychiatrists, took a somewhat negative attitude toward the use of operational methodology. The author contends that one of the causes for the relatively poor acceptance of operational methodology in the area of clinical practice lies in the "classification model" view of diagnosis that forms the implicit background for the methodology. From a clinical perspective, it is not from the "classification model" basis but rather, from the "decision-making model" basis that the actual process of clinical diagnosis in psychiatry is explained properly. This is a very important point, because the latter model is potentially more useful both to psychiatric patients and to researchers in psychiatry than the former model. There have been however, few reports in psychiatry that highlight the importance of this model as the clinical framework. The author analyzes the limitations of the "classification model" view, and then, based on this analysis, lists prerequisites that a model for the framework of clinical practice should possess. The prerequisites listed are: that clinical information not sufficient to meet the disease criteria should be used as effectively as possible, that diseases low in probability but high in seriousness should be considered by clinicians in the differential diagnoses; that diagnosis should be readily changed when necessary

  16. Hypergraph Based Feature Selection Technique for Medical Diagnosis.

    PubMed

    Somu, Nivethitha; Raman, M R Gauthama; Kirthivasan, Kannan; Sriram, V S Shankar

    2016-11-01

    The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K - Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K - Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.

  17. Test designs and modeling under the general nominal diagnosis model framework.

    PubMed

    Chen, Jinsong; Zhou, Hui

    2017-01-01

    Most psychological questionnaires face issues of response bias in respondent-reported scales, inadequacy for criterion-reference testing, or difficulty in estimating a large number of latent traits. Situational tests together with the general nominal diagnosis model framework provide a viable alternative to alleviate these concerns. Under this framework, there are different ways to design situationally nominal items, which can offer more flexibility for test development. Any response bias remaining with respondent-reported questionnaires may be addressed with appropriate test designs. The saturated model subsumes different reduced forms that can help inform whether the test is designed as expected. Two simulation studies are presented to demonstrate the effectiveness of the models and designs.

  18. Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

    PubMed Central

    Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

    1996-01-01

    Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

  19. Resonance-Based Sparse Signal Decomposition and Its Application in Mechanical Fault Diagnosis: A Review

    PubMed Central

    Huang, Wentao; Sun, Hongjian; Wang, Weijie

    2017-01-01

    Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis. PMID:28587198

  20. Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

    PubMed

    Huang, Wentao; Sun, Hongjian; Wang, Weijie

    2017-06-03

    Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology.

    PubMed

    Maurice, P; Dhombres, F; Blondiaux, E; Friszer, S; Guilbaud, L; Lelong, N; Khoshnood, B; Charlet, J; Perrot, N; Jauniaux, E; Jurkovic, D; Jouannic, J-M

    2017-05-01

    We have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue. The ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term "Pregnancy, Ectopic") and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system. The resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64-42.05]. The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology. Copyright © 2017. Published by Elsevier Masson SAS.

  6. Malfunction diagnosis of sensors based on correlation of measurements

    NASA Astrophysics Data System (ADS)

    Lu, Wei; Teng, Jun; Wen, Runfa; Zhu, Jiayi; Li, Chao

    2017-02-01

    Structural health monitoring (SHM) is a type of on-site characterization of a real-world full-scale structure that is subjected to the real-world load cases. The fundamental element of SHM is the structural response measurements by sensors, the reliability of which is significant for safety assessment and other SHM applications. The paper proposed a method to diagnosis the fault in sensors using the correlation of measurements. The correlation of the variations of the measurements is examined using the sliding time windows, which is the principle to determine the fault in the sensors. The strain measurements from the SHM system of a real world structure, Shenzhen Bay Stadium, are performed to simulate the faults in sensors and to verify the effectiveness of the proposed method.

  7. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

    PubMed

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-02-21

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

  8. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    PubMed Central

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  9. 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.

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

    PubMed

    Bhavani, Selvaraj Rani; Senthilkumar, Jagatheesan; Chilambuchelvan, Arul Gnanaprakasam; Manjula, Dhanabalachandran; Krishnamoorthy, Ramasamy; Kannan, Arputharaj

    2015-03-27

    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. 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). 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. 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 from 150 breast cancer survivors from hospitals

  11. 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

  12. 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

  13. Diagnosis of delay-deadline failures in real time discrete event models.

    PubMed

    Biswas, Santosh; Sarkar, Dipankar; Bhowal, Prodip; Mukhopadhyay, Siddhartha

    2007-10-01

    In this paper a method for fault detection and diagnosis (FDD) of real time systems has been developed. A modeling framework termed as real time discrete event system (RTDES) model is presented and a mechanism for FDD of the same has been developed. The use of RTDES framework for FDD is an extension of the works reported in the discrete event system (DES) literature, which are based on finite state machines (FSM). FDD of RTDES models are suited for real time systems because of their capability of representing timing faults leading to failures in terms of erroneous delays and deadlines, which FSM-based ones cannot address. The concept of measurement restriction of variables is introduced for RTDES and the consequent equivalence of states and indistinguishability of transitions have been characterized. Faults are modeled in terms of an unmeasurable condition variable in the state map. Diagnosability is defined and the procedure of constructing a diagnoser is provided. A checkable property of the diagnoser is shown to be a necessary and sufficient condition for diagnosability. The methodology is illustrated with an example of a hydraulic cylinder.

  14. Color model comparative analysis for breast cancer diagnosis using H and E stained images

    NASA Astrophysics Data System (ADS)

    Li, Xingyu; Plataniotis, Konstantinos N.

    2015-03-01

    Digital cancer diagnosis is a research realm where signal processing techniques are used to analyze and to classify color histopathology images. Different from grayscale image analysis of magnetic resonance imaging or X-ray, colors in histopathology images convey large amount of histological information and thus play significant role in cancer diagnosis. Though color information is widely used in histopathology works, as today, there is few study on color model selections for feature extraction in cancer diagnosis schemes. This paper addresses the problem of color space selection for digital cancer classification using H and E stained images, and investigates the effectiveness of various color models (RGB, HSV, CIE L*a*b*, and stain-dependent H and E decomposition model) in breast cancer diagnosis. Particularly, we build a diagnosis framework as a comparison benchmark and take specific concerns of medical decision systems into account in evaluation. The evaluation methodologies include feature discriminate power evaluation and final diagnosis performance comparison. Experimentation on a publicly accessible histopathology image set suggests that the H and E decomposition model outperforms other assessed color spaces. For reasons behind various performance of color spaces, our analysis via mutual information estimation demonstrates that color components in the H and E model are less dependent, and thus most feature discriminate power is collected in one channel instead of spreading out among channels in other color spaces.

  15. A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.

    PubMed

    Seixas, Flávio Luiz; Zadrozny, Bianca; Laks, Jerson; Conci, Aura; Muchaluat Saade, Débora Christina

    2014-08-01

    Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.

  16. Application of Three Cognitive Diagnosis Models to ESL Reading and Listening Assessments

    ERIC Educational Resources Information Center

    Lee, Yong-Won; Sawaki, Yasuyo

    2009-01-01

    The present study investigated the functioning of three psychometric models for cognitive diagnosis--the general diagnostic model, the fusion model, and latent class analysis--when applied to large-scale English as a second language listening and reading comprehension assessments. Data used in this study were scored item responses and incidence…

  17. 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

    2016-05-02

    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.

  18. Digoxin use after diagnosis of colorectal cancer and survival: a population-based cohort study.

    PubMed

    Karasneh, Reema A; Murray, Liam J; Hughes, Carmel M; Cardwell, Chris R

    2015-11-01

    Digoxin has been shown to affect a number of pathways that are of relevance to cancer, and its use has been associated with increased risks of breast and uterus cancer and, more recently, a 40% increase in colorectal cancer risk. These findings raise questions about the safety of digoxin use in colorectal cancer patients, and, therefore, we investigated whether digoxin use after colorectal cancer diagnosis increased the risk of colorectal cancer-specific mortality. A cohort of 10,357 colorectal cancer patients newly diagnosed from 1998 to 2009 was identified from English cancer registries and linked to the UK Clinical Practice Research Datalink (to provide digoxin and other prescription records) and to the Office of National Statistics mortality data (to identify 2,724 colorectal cancer-specific deaths). Using time-dependent Cox regression models, unadjusted and adjusted HRs and 95% confidence intervals (CI) were calculated for the association between postdiagnostic exposure to digoxin and colorectal cancer-specific mortality. Overall, 682 (6%) colorectal cancer patients used digoxin after diagnosis. Digoxin use was associated with a small increase in colorectal cancer-specific mortality before adjustment (HR, 1.25; 95% CI, 1.07-1.46), but after adjustment for confounders, the association was attenuated (adjusted HR, 1.10; 95% CI, 0.91-1.34) and there was no evidence of a dose response. In this large population-based colorectal cancer cohort, there was little evidence of an increase in colorectal cancer-specific mortality with digoxin use after diagnosis. These results provide some reassurance that digoxin use is safe in colorectal cancer patients. ©2015 American Association for Cancer Research.

  19. Diagnosis and care of irritable bowel syndrome in a community-based population.

    PubMed

    Yawn, B P; Locke, G R; Lydick, E; Wollan, P C; Bertram, S L; Kurland, M J

    2001-06-01

    To identify the healthcare utilization and evaluation in a community-based population with an incident diagnosis of irritable bowel syndrome (IBS). Retrospective cohort. A randomly selected cohort of 149 Olmsted County, MN, adults with an incident diagnosis of IBS between June 1, 1992, and December 31, 1994. Retrospective medical record review of each medical encounter for the 10 years before and the 3 years after the incident IBS diagnosis. Specific attention was given to healthcare visits, tests, and treatment associated with documented gastrointestinal (GI) tract symptoms and diagnosis of IBS. Of 149 patients, 98 (66%) were women, and the mean patient age was 46.6 years. Patients averaged 4.7 healthcare visits annually, including 0.5 related to GI tract symptoms. Two thirds of patients had GI tract symptom-related visits at least 2 years before IBS diagnosis. Only 5% of IBS diagnoses were made by a gastroenterologist. Colon examinations (colonoscopy or barium enema) were performed on 47% of patients and were more common in those aged > or = 55 years (58% vs 36%; P = .02). One third of patients had no GI tract testing proximal to the IBS diagnosis. Following the IBS diagnosis, only about half the patients made any IBS or GI tract symptom-related visits per year. The path to diagnosis of IBS is through the primary care physician and includes minimal testing and limited follow-up care.

  20. Waiting time disparities in breast cancer diagnosis and treatment: a population-based study in France.

    PubMed

    Molinié, F; Leux, C; Delafosse, P; Ayrault-Piault, S; Arveux, P; Woronoff, A S; Guizard, A V; Velten, M; Ganry, O; Bara, S; Daubisse-Marliac, L; Tretarre, B

    2013-10-01

    Waiting times are key indicators of a health's system performance, but are not routinely available in France. We studied waiting times for diagnosis and treatment according to patients' characteristics, tumours' characteristics and medical management options in a sample of 1494 breast cancers recorded in population-based registries. The median waiting time from the first imaging detection to the treatment initiation was 34 days. Older age, co-morbidity, smaller size of tumour, detection by organised screening, biopsy, increasing number of specimens removed, multidisciplinary consulting meetings and surgery as initial treatment were related to increased waiting times in multivariate models. Many of these factors were related to good practices guidelines. However, the strong influence of organised screening programme and the disparity of waiting times according to geographical areas were of concern. Better scheduling of diagnostic tests and treatment propositions should improve waiting times in the management of breast cancer in France. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. 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

    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.

  2. Stress echocardiography with contrast for the diagnosis of coronary artery disease: an evidence-based analysis.

    PubMed

    2010-01-01

    In July 2009, the Medical Advisory Secretariat (MAS) began work on Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease (CAD), an evidence-based review of the literature surrounding different cardiac imaging modalities to ensure that appropriate technologies are accessed by patients suspected of having CAD. This project came about when the Health Services Branch at the Ministry of Health and Long-Term Care asked MAS to provide an evidentiary platform on effectiveness and cost-effectiveness of non-invasive cardiac imaging modalities.After an initial review of the strategy and consultation with experts, MAS identified five key non-invasive cardiac imaging technologies for the diagnosis of CAD. Evidence-based analyses have been prepared for each of these five imaging modalities: cardiac magnetic resonance imaging, single photon emission computed tomography, 64-slice computed tomographic angiography, stress echocardiography, and stress echocardiography with contrast. For each technology, an economic analysis was also completed (where appropriate). A summary decision analytic model was then developed to encapsulate the data from each of these reports (available on the OHTAC and MAS website).The Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease series is made up of the following reports, which can be publicly accessed at the MAS website at: www.health.gov.on.ca/mas or at www.health.gov.on.ca/english/providers/program/mas/mas_about.htmlSINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based AnalysisSTRESS ECHOCARDIOGRAPHY FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based AnalysisSTRESS ECHOCARDIOGRAPHY WITH CONTRAST FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based Analysis64-Slice Computed Tomographic Angiography for the Diagnosis of Coronary Artery Disease: An Evidence-Based AnalysisCARDIAC MAGNETIC RESONANCE IMAGING FOR

  3. Successful polar body-based preimplantation genetic diagnosis for achondroplasia.

    PubMed

    Altarescu, G; Renbaum, P; Brooks P, B; Margalioth, E J; Ben Chetrit, A; Munter, G; Levy-Lahad, E; Eldar-Geva, T

    2008-02-01

    Achondroplasia, the most common form of dwarfism, is a candidate for preimplantation genetic diagnosis (PGD) because a single mutation accounts for almost all cases. Multiplex fluorescent assay including the common G380R mutation in the FGFR3 gene and eight close polymorphic markers was developed. First and second polar bodies (PB) were used for PGD analysis. An affected woman was treated with routine long-protocol ovarian stimulation and puncture. In the first PGD cycle, out of four fertilized oocytes, PB analysis revealed two mutant oocytes, one with total amplification failure of the maternal allele and one with inconclusive results. In the second PGD cycle, 14 oocytes were retrieved following a higher FSH dose and by performing oocyte retrieval and by placing the patient in the anti-Trendelenburg position using abdominal pressure to allow all follicles to be drained. Following PB analysis, two embryos containing the wild-type FGFR3 allele were transferred. This led to an uncomplicated pregnancy and delivery by Caesarean section at week 38 of a healthy boy, carrying the FGFR3 wild-type maternal allele. In conclusion, oocyte retrieval, while difficult in patients with achondroplasia, can be successfully performed. PB analysis is a reliable and sensitive method for PGD for maternal achondroplasia.

  4. Imaging-Based Diagnosis of Autosomal Dominant Polycystic Kidney Disease

    PubMed Central

    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.

    2015-01-01

    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

  5. Diagnosis of Breast Cancer Based on FT-IR Spectroscopy

    NASA Astrophysics Data System (ADS)

    Venkatachalam, P.; Rao, L. Lakshmana; Kumar, N. Krishna; Jose, Anupama; Nazeer, Shaiju S.

    2008-11-01

    Breast cancer is one of the most important malignant forms of cancer and a great threat to life for women. In the present study, the spectral characteristics of human breast tissues in normal and cancerous state have been investigated by Fourier transform infrared (FT-IR) absorption spectroscopy in the spectral region from 4000 to 400 cm-1. Several spectral differences were detected in the frequency regions N-H stretching, C-H vibrations, amide bands and 900-1300 cm-1. The ratio of intensities of the bands of A3300/A3015 & A1650/A1550, A2924/A2853, A1080/A1236, A1204/A1650, A1055/A1467 and A1045/A1467 provide conformational changes of protein, lipids, nucleic acids, collagen, carbohydrates and glycogen respectively in the human breast tissues. There are obvious differences in the spectral features between normal and cancerous tissues because of changes in molecular compositions and structures that accompany the transformation from a normal to a cancerous state. The differences suggest that the spectral information are useful for the diagnosis of breast cancer and may serve as a basis for conformational changes in tissue components during carcinogenesis.

  6. Diagnosis and treatment of TMA based on Japanese clinical guides.

    PubMed

    Wada, Hideo; Hasegawa, Kei; Watanabe, Maki

    Patients with thrombotic microangiopathies (TMAs), which include thrombotic thrombocytopenic purpura (TTP), atypical hemolytic uremic syndrome (aHUS), and Shiga toxin-producing Escherichia coli hemolytic uremic syndrome (STEC-HUS), exhibit multiple symptoms. These include thrombocytopenia (platelet count <15×10(4)/µl), microangiopathic hemolytic anemia (hemoglobin <10 g/dl), and organ damage, such as renal dysfunction or neurological abnormalities. Guidelines for the management of TTP, aHUS, and STEC-HUS have been published. Diagnosis of TTP, STEC-HUS, or aHUS requires ADAMTS13 activity <10%, the detection of STEC or Shiga toxins, or a genetic analysis to investigate the complement regulatory system, respectively. In the established management of TMAs, fresh frozen plasma is used for congenital TTP to supplement ADAMTS13. Plasma exchange (PE) is used in acquired TTP (ATTP) to remove inhibitors and replace ADAMTS13. Steroid therapy is administered in ATTP to suppress inhibitor production, and rituximab is administered in ATTP without response to PE or with high titers of ADAMTS13 inhibitors. Eculizumab, a monoclonal antibody for complement protein 5, is used for the treatment of aHUS. Supportive therapy is required for some TMAs, particularly STEC-HUS, but platelet transfusion is generally contraindicated for other TMAs, such as TTP. Treatments should be administered in accordance with these guidelines to improve the outcome of TMAs.

  7. 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.

  8. 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

  9. 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.

  10. Clinical correlates to assist with CTE diagnosis: Insights from a novel rodent repeat concussion model.

    PubMed

    Thomsen, Gretchen M; Ko, Ara; Harada, Megan Y; Ma, Annie; Wyss, Livia; Haro, Patricia; Vit, Jean-Philippe; Avalos, Pablo; Dhillon, Navpreet; Cho, Noell; Shelest, Oksana; Ley, Eric J

    2017-03-23

    Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease linked to repetitive head injuries. CTE symptoms include changes in mood, behavior, cognition and motor function, however CTE is only currently diagnosed post-mortem. Using a rat model of recurrent traumatic brain injury (TBI) we demonstrate rodent deficits that predict the severity of CTE-like brain pathology. Bilateral, closed skull, mild TBI was administered once per week to 35 WT rats, 8 rats received 2 injuries ('2xTBI'), 27 rats received 5 injuries ('5xTBI') and 13 rats were sham controls. To determine clinical correlates for CTE diagnosis, TBI rats were separated based on the severity of rotarod deficits and classified as "mild" or "severe" and further separated into "acute", "short" and "long" based on age at euthanasia (90, 144 and 235 days, respectively). Brain atrophy, phosphorylated tau (p-tau), and inflammation were assessed. All eight 2xTBI had mild rotorod deficiency, 11 5xTBI had mild deficiency and 16 had severe. In one cohort of rats, tested at ~235 days of age, balance, rearing, and grip strength were significantly worse in the severe group relative to both sham and mild groups. At the acute time period, cortical thinning, p-tau and inflammation were not observed in either TBI group, whereas corpus callosum thinning was observed in both TBI groups. At later time points, atrophy, tau pathology and inflammation were increased in mild and severe TBI groups in the cortex and corpus callosum, relative to sham controls. These injury effects were exacerbated over time in the severe TBI group in the corpus callosum. Our model of repeat mild TBI suggests that permanent deficits in specific motor function tests correlate with CTE-like brain pathology. Assessing balance and motor coordination over time may predict CTE diagnosis.

  11. Functional protease profiling for laboratory based diagnosis of invasive aspergillosis.

    PubMed

    Sabbagh, Bassel; Costina, Victor; Buchheidt, Dieter; Reinwald, Mark; Neumaier, Michael; Findeisen, Peter

    2015-07-01

    Invasive aspergillosis (IA) remains difficult to diagnose in immunocompromised patients, because diagnostic criteria according to EORTC/MSG guidelines are often not met and have low sensitivity. Hence there is an urgent need to improve diagnostic procedures by developing novel approaches. In the present study, we present a proof of concept experiment for the monitoring of Aspergillus associated protease activity in serum specimens for diagnostic purpose. Synthetic peptides that are selectively cleaved by proteases secreted from Aspergillus species were selected from our own experiments and published data. These so called reporter peptides (RP, n=5) were added to serum specimens from healthy controls (HC, n=101) and patients with proven (IA, n=9) and possible (PIA, n=144) invasive aspergillosis. Spiked samples were incubated ex vivo under strictly standardized conditions. Proteolytic fragments were analyzed using MALDI-TOF mass spectrometry. Spiked specimens of IA patients had highest concentrations of RP-fragments followed by PIA and HC. The median signal intensity was 116.546 (SD, 53.063) for IA and 5.009 (SD, 8.432) for HC. A cut-off >36.910 was chosen that performed with 100% specificity and sensitivity. Patients with PIA had either values above [53% (76/144)] or below [47% (67/144)] this chosen cut-off. The detection of respective reporter peptide fragments can easily be performed by MALDI TOF mass spectrometry. In this proof of concept study we were able to demonstrate that serum specimens of patients with IA have increased proteolytic activity towards selected reporter peptides. However, the diagnostic value of functional protease profiling has to be validated in further prospective studies. It is likely that a combination of existing and new methods will be required to achieve optimal performance for diagnosis of IA in the future.

  12. Paper-based point-of-care testing for diagnosis of dengue infections.

    PubMed

    Choi, Jane Ru; Hu, Jie; Wang, ShuQi; Yang, Hui; Wan Abas, Wan Abu Bakar; Pingguan-Murphy, Belinda; Xu, Feng

    2017-02-01

    Dengue endemic is a serious healthcare concern in tropical and subtropical countries. Although well-established laboratory tests can provide early diagnosis of acute dengue infections, access to these tests is limited in developing countries, presenting an urgent need to develop simple, rapid, and robust diagnostic tools. Point-of-care (POC) devices, particularly paper-based POC devices, are typically rapid, cost-effective and user-friendly, and they can be used as diagnostic tools for the prompt diagnosis of dengue at POC settings. Here, we review the importance of rapid dengue diagnosis, current dengue diagnostic methods, and the development of paper-based POC devices for diagnosis of dengue infections at the POC.

  13. 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)

  14. [Diagnosis and treatment of injuries to the frontal skull base].

    PubMed

    Lorenz, K J; Maier, H; Mauer, U M

    2011-08-01

    The role of ENT surgery in the management of anterior skull base defects has become increasingly important in recent years. Transnasal endoscopic surgical techniques and intraoperative navigation enable a minimally invasive approach in a large proportion of patients, thus helping to avoid morbidity typically associated with neurosurgical subfrontal approaches. Whereas traffic accidents and sport injuries are the main causes of anterior skull base trauma in the civilian setting, penetrating injuries caused by gunshots and improvised explosive devices (IEDs) play an increasing role in the military arena and terroristic attacks. Minor injuries to the anterior skull base are usually managed by ENT surgeons. Major injuries, involving the midface or neurocranium, require an interdisciplinary approach including maxillofacial surgeons and neurosurgeons. A centre for head and neck medicine and surgery is an ideal setting for such interdisciplinary teams to provide appropriate care for patients with complex skull base trauma in cooperation with ophthalmologists and interventional neuoradiologists. The present article describes concepts for the treatment of anterior skull base trauma established at the head, neck and skull base center at the Ulm military hospital in Germany.

  15. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps

    PubMed Central

    Zheng, Hong; Ji, Jiansong; Zhao, Liangcai; Chen, Minjiang; Shi, An; Pan, Linlin; Huang, Yiran; Zhang, Huajie; Dong, Baijun; Gao, Hongchang

    2016-01-01

    Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery. PMID:27463020

  16. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.

    PubMed

    Zheng, Hong; Ji, Jiansong; Zhao, Liangcai; Chen, Minjiang; Shi, An; Pan, Linlin; Huang, Yiran; Zhang, Huajie; Dong, Baijun; Gao, Hongchang

    2016-09-13

    Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.

  17. Diagnosis of cerebral cryptococcoma using a computerized analysis of 1H NMR spectra in an animal model.

    PubMed

    Dzendrowskyj, Theresa E; Dolenko, Brion; Sorrell, Tania C; Somorjai, Rajmund L; Malik, Richard; Mountford, Carolyn E; Himmelreich, Uwe

    2005-06-01

    Viable cryptococci load in biopsy material from an animal model of cerebral cryptococcoma were correlated with 1H NMR spectra and metabolite profiles. A statistical classification strategy was applied to distinguish among high-resolution 1H NMR spectra acquired from cryptococcomas, glioblastomas, and normal brain tissue. The overall classification accuracy was 100% when a genetic-algorithm-based optimal region selection preceded the development of linear discriminant analysis-based classifiers. The method remained robust despite differences in the microbial load of the cryptococcoma group when harvested at different time points. These results indicate the feasibility of the method for diagnosis without isolation of the pathogenic microorganism and its potential for in vivo diagnosis based on computerized analysis of magnetic resonance spectra.

  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.

  19. 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

  20. 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…

  1. 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…

  2. Conjunctive and Disjunctive Extensions of the Least Squares Distance Model of Cognitive Diagnosis

    ERIC Educational Resources Information Center

    Dimitrov, Dimiter M.; Atanasov, Dimitar V.

    2012-01-01

    Many models of cognitive diagnosis, including the "least squares distance model" (LSDM), work under the "conjunctive" assumption that a correct item response occurs when all latent attributes required by the item are correctly performed. This article proposes a "disjunctive" version of the LSDM under which the correct item response occurs when "at…

  3. [Diagnosis "risk of pneumonia" evidence-based evaluation and comparison of nursing interventions].

    PubMed

    Haslinger-Baumann, Elisabeth; Burns, Evelin

    2007-12-01

    In home nursing care, professionally qualified nurses make decisions on their own which must be based on the latest scientific research. The goal of this systematic literature review was to examine if the interventions of the diagnosis "Risk of Pneumonia" as used by a home care service provider in Austria are based on scientific evidence. Based on the research question "Can the interventions of the diagnosis 'risk of pneumonia' be found in the scientific nursing literature?", four evidence-based guidelines from various databases and institutes were identified and evaluated using a standardized checklist. The result of the analysis is that the majority of the Nursing diagnosis interventions can be found in the guidelines. It is also recommended in the guidelines to advise and support clients to stop smoking and inoculate against influenza/pneumonia. Similarly, assessment tools should be used to estimate the severity of pneumonia. The intervention of oral hygiene as a prophylactic intervention against pneumonia should get particular attention.

  4. The development of a theory-based intervention to promote appropriate disclosure of a diagnosis of dementia

    PubMed Central

    Foy, Robbie; Francis, Jillian J; Johnston, Marie; Eccles, Martin; Lecouturier, Jan; Bamford, Claire; Grimshaw, Jeremy

    2007-01-01

    Background The development and description of interventions to change professional practice are often limited by the lack of an explicit theoretical and empirical basis. We set out to develop an intervention to promote appropriate disclosure of a diagnosis of dementia based on theoretical and empirical work. Methods We identified three key disclosure behaviours: finding out what the patient already knows or suspects about their diagnosis; using the actual words 'dementia' or 'Alzheimer's disease' when talking to the patient; and exploring what the diagnosis means to the patient. We conducted a questionnaire survey of older peoples' mental health teams (MHTs) based upon theoretical constructs from the Theory of Planned Behaviour (TPB) and Social Cognitive Theory (SCT) and used the findings to identify factors that predicted mental health professionals' intentions to perform each behaviour. We selected behaviour change techniques likely to alter these factors. Results The change techniques selected were: persuasive communication to target subjective norm; behavioural modelling and graded tasks to target self-efficacy; persuasive communication to target attitude towards the use of explicit terminology when talking to the patient; and behavioural modelling by MHTs to target perceived behavioural control for finding out what the patient already knows or suspects and exploring what the diagnosis means to the patient. We operationalised these behaviour change techniques using an interactive 'pen and paper' intervention designed to increase intentions to perform the three target behaviours. Conclusion It is feasible to develop an intervention to change professional behaviour based upon theoretical models, empirical data and evidence based behaviour change techniques. The next step is to evaluate the effect of such an intervention on behavioural intention. We argue that this approach to development and reporting of interventions will contribute to the science of

  5. Validation of Antibody-Based Strategies for Diagnosis of Pediatric Celiac Disease Without Biopsy.

    PubMed

    Wolf, Johannes; Petroff, David; Richter, Thomas; Auth, Marcus K H; Uhlig, Holm H; Laass, Martin W; Lauenstein, Peter; Krahl, Andreas; Händel, Norman; de Laffolie, Jan; Hauer, Almuthe C; Kehler, Thomas; Flemming, Gunter; Schmidt, Frank; Rodrigues, Astor; Hasenclever, Dirk; Mothes, Thomas

    2017-08-01

    A diagnosis of celiac disease is made based on clinical, genetic, serologic, and duodenal morphology features. Recent pediatric guidelines, based largely on retrospective data, propose omitting biopsy analysis for patients with concentrations of IgA against tissue transglutaminase (IgA-TTG) >10-fold the upper limit of normal (ULN) and if further criteria are met. A retrospective study concluded that measurements of IgA-TTG and total IgA, or IgA-TTG and IgG against deamidated gliadin (IgG-DGL) could identify patients with and without celiac disease. Patients were assigned to categories of no celiac disease, celiac disease, or biopsy required, based entirely on antibody assays. We aimed to validate the positive and negative predictive values (PPV and NPV) of these diagnostic procedures. We performed a prospective study of 898 children undergoing duodenal biopsy analysis to confirm or rule out celiac disease at 13 centers in Europe. We compared findings from serologic analysis with findings from biopsy analyses, follow-up data, and diagnoses made by the pediatric gastroenterologists (celiac disease, no celiac disease, or no final diagnosis). Assays to measure IgA-TTG, IgG-DGL, and endomysium antibodies were performed by blinded researchers, and tissue sections were analyzed by local and blinded reference pathologists. We validated 2 procedures for diagnosis: total-IgA and IgA-TTG (the TTG-IgA procedure), as well as IgG-DGL with IgA-TTG (TTG-DGL procedure). Patients were assigned to categories of no celiac disease if all assays found antibody concentrations <1-fold the ULN, or celiac disease if at least 1 assay measured antibody concentrations >10-fold the ULN. All other cases were considered to require biopsy analysis. ULN values were calculated using the cutoff levels suggested by the test kit manufacturers. HLA typing was performed for 449 participants. We used models that considered how specificity values change with prevalence to extrapolate the PPV and NPV to

  6. Model compilation for embedded real-time planning and diagnosis

    NASA Technical Reports Server (NTRS)

    Barrett, Anthony

    2004-01-01

    This paper describes MEXEC, an implemented micro executive that compiles a device model into an interal structure. Not only does this structure facilitate computing the most likely current device mode from n sets of sensor measurements, but it also facilitates generating an n step reconfiguration plan that is most likely not to result in reaching a target mode - if such a plan exists.

  7. Diagnosis of precipitation variability in nested regional climate models

    NASA Astrophysics Data System (ADS)

    Arritt, R.; PIRCS Participants

    2003-04-01

    In order to assess reasons for model-to-model variability of precipitation in regional climate models (RCMs) we have evaluated 60-day simulations over the continental U.S. in June-July 1993 from thirteen simulations using different RCMs. The hydrologic cycles in the simulations were compared both to each other and to observations for a subregion of the upper Mississippi River Basin (UMRB), containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports. All RCMs produced positive precipitation (P) minus evaporation (E) and recycling ratios that were within the range estimated from observations. RCM E was sensitive to radiation parameterization, but inter-model variability of E was spread evenly about estimates of observed E. In contrast, most RCMs produced P that was below the range of P from observations, accounting for the low values of simulated P-E compared to observations. Nine of the 13 RCMs reproduced qualitatively the observed daily cycles of P and moisture flux convergence (C), with maximum P and C occurring simultaneously at night. Three of the four driest RCMs had maximum precipitation in the afternoon, suggesting that in these RCMs afternoon destabilization by insolation had excessive influence on production of precipitation. Thus a key indicator of the ability of RCMs in this collection to properly simulate P is their ability to simulate the observed nocturnal maximum of P, indicating that the failure to resolve the diurnal cycle is closely related to overall bias in precipitation.

  8. Model compilation for embedded real-time planning and diagnosis

    NASA Technical Reports Server (NTRS)

    Barrett, Anthony

    2004-01-01

    This paper describes MEXEC, an implemented micro executive that compiles a device model into an interal structure. Not only does this structure facilitate computing the most likely current device mode from n sets of sensor measurements, but it also facilitates generating an n step reconfiguration plan that is most likely not to result in reaching a target mode - if such a plan exists.

  9. Diagnosis for Covariance Structure Models by Analyzing the Path

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Kouros, Chrystyna D.; Kelley, Ken

    2008-01-01

    When a covariance structure model is misspecified, parameter estimates will be affected. It is important to know which estimates are systematically affected and which are not. The approach of analyzing the path is both intuitive and informative for such a purpose. Different from path analysis, analyzing the path uses path tracing and elementary…

  10. Model Organisms Facilitate Rare Disease Diagnosis and Therapeutic Research

    PubMed Central

    Wangler, Michael F.; Yamamoto, Shinya; Chao, Hsiao-Tuan; Posey, Jennifer E.; Westerfield, Monte; Postlethwait, John; Hieter, Philip; Boycott, Kym M.; Campeau, Philippe M.; Bellen, Hugo J.

    2017-01-01

    Efforts to identify the genetic underpinnings of rare undiagnosed diseases increasingly involve the use of next-generation sequencing and comparative genomic hybridization methods. These efforts are limited by a lack of knowledge regarding gene function, and an inability to predict the impact of genetic variation on the encoded protein function. Diagnostic challenges posed by undiagnosed diseases have solutions in model organism research, which provides a wealth of detailed biological information. Model organism geneticists are by necessity experts in particular genes, gene families, specific organs, and biological functions. Here, we review the current state of research into undiagnosed diseases, highlighting large efforts in North America and internationally, including the Undiagnosed Diseases Network (UDN) (Supplemental Material, File S1) and UDN International (UDNI), the Centers for Mendelian Genomics (CMG), and the Canadian Rare Diseases Models and Mechanisms Network (RDMM). We discuss how merging human genetics with model organism research guides experimental studies to solve these medical mysteries, gain new insights into disease pathogenesis, and uncover new therapeutic strategies. PMID:28874452

  11. Distributed Knowledge Base Systems for Diagnosis and Information Retrieval.

    DTIC Science & Technology

    1988-04-08

    Information Processing Model of Japanese Foreign and Energy Policy Decision Making: JESSE Donald A. Sylvan Department of Political Science The Ohio State...processing theory would expect. Our political science judgment is that Japanese supply security is such a domain. We, therefore, propose, explicate, and test...annd natural resource obstacles that Japan faces. In the political science community, students of both foreign policy decision making and of

  12. Traffic fatality indicators in Brazil: State diagnosis based on data envelopment analysis research.

    PubMed

    Bastos, Jorge Tiago; Shen, Yongjun; Hermans, Elke; Brijs, Tom; Wets, Geert; Ferraz, Antonio Clóvis Pinto

    2015-08-01

    The intense economic growth experienced by Brazil in recent decades and its consequent explosive motorization process have evidenced an undesirable impact: the increasing and unbroken trend in traffic fatality numbers. In order to contribute to road safety diagnosis on a national level, this study presents a research into two main indicators available in Brazil: mortality rate (represented by fatalities per capita) and fatality rate (represented by two sub-indicators, i.e., fatalities per vehicle and fatalities per vehicle kilometer traveled). These indicators were aggregated into a composite indicator or index through a multiple layer data envelopment analysis (DEA) composite indicator model, which looks for the optimum combination of indicators' weights for each decision-making unit, in this case 27 Brazilian states. The index score represents the road safety performance, based on which a ranking of states can be made. Since such a model has never been applied for road safety evaluation in Brazil, its parameters were calibrated based on the experience of more consolidated European Union research in ranking its member countries using DEA techniques. Secondly, cluster analysis was conducted aiming to provide more realistic performance comparisons and, finally, the sensitivity of the results was measured through a bootstrapping method application. It can be concluded that by combining fatality indicators, defining clusters and applying bootstrapping procedures a trustworthy ranking can be created, which is valuable for nationwide road safety planning.

  13. 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.

  14. 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.

  15. 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.

  16. Distributed Knowledge Base Systems for Diagnosis and Information Retrieval.

    DTIC Science & Technology

    1983-11-01

    progress came in the area of diagnostic reasoning and in the conceptual foundations of knowledge-based systems in general. We also developed an approach to a...have been experimenting with the application of this tool to the design and implementation of expert systems in the area of mechanical systems, since...itself or in the problem solving open area of research in the field, but the intuition is that processes that operate on it. Thus the range of

  17. Diagnosis of Compartment Syndrome Based on Tissue Oxygenation

    DTIC Science & Technology

    2015-06-01

    of technical problems obtaining accurate PmO2 and CP; results are based on the remaining 15 animals. We confirmed our hypothesis that tissue...additional problem with the probe was that left-to-right differences of PmO2 occurred within the same animal at baseline before any experimental procedures...also explain the lack of significant correlations between PmO2 and TVI in many parts of the study. CHANGES/ PROBLEMS :  Changes in approach and

  18. Cancer patients' awareness about their diagnosis: a population-based study.

    PubMed

    Nord, C; Mykletun, A; Fosså, S D

    2003-12-01

    The aim of the study was to evaluate Norwegian cancer patients' awareness of their prior cancer diagnosis in a general population-based study. A cross-sectional population-based study of cancer patients' responses to the index question: 'Do you have or have you had cancer?' was carried out. We assessed correctness of the response in relation to cancer site, date of diagnosis, marital status, age and education. Smoking was chosen as a marker of health awareness. A total of 65,330 persons participated in the Nord-Trøndelag Health Survey (HUNT-II), performed in 1995-1997. The database of HUNT-II was merged with the Cancer Registry of Norway (CRN), thus identifying each of the 2983 (4 percent) participants with an invasive cancer diagnosis. Excluding basal cell epithelioma, a total of 20 percent of the patients denied their prior cancer diagnosis. This group consisted mainly of men (54 percent) and those who were diagnosed as very young or as elderly. More smokers than non-smokers were unaware of their prior malignancy (24 percent versus 20 percent). A 20 percent rate of patients who denied their former malignancy is surprisingly and unacceptably high. Disclosure of a cancer diagnosis should help the patient to develop increased health awareness. It should enable a person to report his or her former cancer diagnosis when necessary.

  19. Computer-Aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

    PubMed

    Bharti, Puja; Mittal, Deepti; Ananthasivan, Rupa

    2016-04-19

    Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis. © The Author(s) 2016.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis.

    PubMed

    Inbarani, H Hannah; Azar, Ahmad Taher; Jothi, G

    2014-01-01

    Medical datasets are often classified by a large number of disease measurements and a relatively small number of patient records. All these measurements (features) are not important or irrelevant/noisy. These features may be especially harmful in the case of relatively small training sets, where this irrelevancy and redundancy is harder to evaluate. On the other hand, this extreme number of features carries the problem of memory usage in order to represent the dataset. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Thus, the learning model receives a concise structure without forfeiting the predictive accuracy built by using only the selected prominent features. Therefore, nowadays, FS is an essential part of knowledge discovery. In this study, new supervised feature selection methods based on hybridization of Particle Swarm Optimization (PSO), PSO based Relative Reduct (PSO-RR) and PSO based Quick Reduct (PSO-QR) are presented for the diseases diagnosis. The experimental result on several standard medical datasets proves the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques.

  5. 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.

  6. Chip-based Magnetic Resonance System for Medical Diagnosis

    NASA Astrophysics Data System (ADS)

    Lee, Hakho; Yoon, Tae-Jong; Weissleder, Ralph

    2009-03-01

    We have developed a chip-based, diagnostic magnetic resonance (DMR) system that can perform rapid, quantitative and multi-channeled detection of biological targets. The measurement is based on the effect of molecularly targeted magnetic nanoparticles on NMR (nuclear magnetic resonance) signals. With magnetic nanoparticles bound to their intended detection targets, the overall spin-spin relaxation time of bulk samples will be significantly shortened, as the particles efficiently dephase spins of surrounding water protons. Because the signal detection relies on NMR, the interference from media becomes negligible, making it possible to perform measurements in native biological samples (e.g., blood, sputum and urine). As proof of concept, we have developed a first DMR prototype by integrating microcoils, microfluidic channels and a permanent magnet. The microcoils, used as an NMR probe, are arranged in an array format for multiplexed, parallel detection. The microfluidic channels provide on-chip mixing between magnetic nanoparticles and biological samples and confine the mixture to microcoils for high filling factor. Here, we demonstrate clinical utility of the DMR system by measuring proteins at exquisite sensitivities (˜1 pM), identifying the disease condition of human sera, and profiling cancer cells according to their cell-surface markers.

  7. Knowledge-based control and case-based diagnosis based upon empirical knowledge and fuzzy logic for the SBR plant.

    PubMed

    Bae, H; Seo, H Y; Kim, S; Kim, Y

    2006-01-01

    Because biological wastewater treatment plants (WWTPs) involve a long time-delay and various disturbances, in general, skilled operators manually control the plant based on empirical knowledge. And operators usually diagnose the plant using similar cases experienced in the past. For the effective management of the plant, system automation has to be accomplished based upon operating recipes. This paper introduces automatic control and diagnosis based upon the operator's knowledge. Fuzzy logic was employed to design this knowledge-based controller because fuzzy logic can convert the linguistic information to rules. The controller can manage the influent and external carbon in considering the loading rate. The input of the controller is not the loading rate but the dissolved oxygen (DO) lag-time, which has a strong relation to the loading rate. This approach can replace an expensive sensor, which measures the loading rate and ammonia concentration in the reactor, with a cheaper DO sensor. The proposed controller can assure optimal operation and prevent the over-feeding problem. Case-based diagnosis was achieved by the analysis of profile patterns collected from the past. A new test profile was diagnosed by comparing it with template patterns containing normal and abnormal cases. The proposed control and diagnostic system will guarantee the effective and stable operation of WWTPs.

  8. Hyperspectral image-based analysis of weathering sensitivity for safety diagnosis of Seongsan Ilchulbong Peak

    NASA Astrophysics Data System (ADS)

    Kim, Sungho; Kim, Heekang

    2016-10-01

    This paper presents a weathering sensitivity analysis method for the safety diagnosis of Seongsan Ilchulbong Peak using hyperspectral images. Remote sensing-based safety diagnosis is important for preventing accidents in famous mountains. A hyperspectral correlation-based method is proposed to evaluate the weathering sensitivity. The three issues are how to reduce the illumination effect, how to remove camera motion while acquiring images on a boat, and how to define the weathering sensitivity index. A novel minimum subtraction and maximum normalization (MSM-norm) method is proposed to solve the shadow and specular illumination problem. Geometrically distorted hyperspectral images are corrected by estimating the borderline of the mountain and sea surface. The final issue is solved by proposing a weathering sensitivity index (WS-Index) based on a spectral angle mapper. Real experiments on the Seongsan Ilchulbong Peak (UNESCO, World Natural Heritage) highlighted the feasibility of the proposed method in safety diagnosis by the weathering sensitivity index.

  9. 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

  10. Towards a mechanism-based approach to pain diagnosis

    PubMed Central

    Vardeh, Daniel

    2016-01-01

    The last few decades have witnessed a huge leap forward in our understanding of the mechanistic underpinnings of pain, both in normal states where it helps protect from injury, and in pathological states where pain evolves from a symptom reflecting tissue injury to become the disease itself. However, despite these scientific advances, chronic pain remains extremely challenging to manage clinically. While the number of potential treatment targets has grown substantially and a strong case has been made for a mechanism-based and individualized approach to pain therapy, arguably clinicians are not much more advanced now than 20 years ago, in their capacity to either diagnose or effectively treat their patients. The gulf between pain research and pain management is as wide as ever. We are still currently unable to apply an evidence-based approach to chronic pain management that reflects mechanistic understanding, and instead, clinical practice remains an empirical and often unsatisfactory journey for patients, whose individual response to treatment cannot be predicted. Here we take a common and difficult to treat pain condition, chronic low back pain, and use its presentation in clinical practice as a framework to highlight what is known about pathophysiological pain mechanisms and how we could potentially detect these to drive rational treatment choice. We discuss how present methods of assessment and management still fall well short, however, of any mechanism-based or precision-medicine approach. Nevertheless, substantial improvements in chronic pain management could be possible if a more strategic and coordinated approach were to evolve, one designed to identify the specific mechanisms driving the presenting pain phenotype. We present an analysis of such an approach, highlighting the major problems in identifying mechanisms in patients, and develop a framework for a pain diagnostic ladder that may prove useful in the future, consisting of successive identification of

  11. 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).

  12. [Active crop canopy sensor-based nitrogen diagnosis for potato].

    PubMed

    Yu, Jing; Li, Fei; Qin, Yong-Lin; Fan, Ming-Shou

    2013-11-01

    In the present study, two potato experiments involving different N rates in 2011 were conducted in Wuchuan County and Linxi County, Inner Mongolia. Normalized difference vegetation index (NDVI) was collected by an active GreenSeeker crop canopy sensor to estimate N status of potato. The results show that the NDVI readings were poorly correlated with N nutrient indicators of potato at vegetative Growth stage due to the influence of soil background. With the advance of growth stages, NDVI values were exponentially related to plant N uptake (R2 = 0.665) before tuber bulking stage and were linearly related to plant N concentration (R2 = 0.699) when plant fully covered soil. In conclusion, GreenSeeker active crop sensor is a promising tool to estimate N status for potato plants. The findings from this study may be useful for developing N recommendation method based on active crop canopy sensor.

  13. Diagnosis of compartment syndrome using a microwave-based detector

    NASA Astrophysics Data System (ADS)

    Ling, Geoffrey S. F.; Riechers, Ronald G., Sr.; Pasala, Krishna M.; Blanchard, Jeremy; Rosner, Michael; Jarell, Abel; Yun, Catherine; Garcia-Pinto, Patricia; Song, Ki-Il; Day, Keith; Riechers, Ronald G., Jr.; Zeidman, Seth M.; Rhee, Peter; Ecklund, James M.; Fitzpatrick, Thomas; Lockhart, Stephen

    2002-07-01

    A novel method for identifying compartment syndrome 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 compartment syndrome, which is a clinical condition where blood or edema in the muscle compartment of the leg leads to critical sichemia of that exptremity. In anesthetized pigs, RAFT, can detect changes in the RF signature from a leg is due to 2cc or greater of either blood or slaine (a surrogate of edema). These results are compared to clinical examination. RAFT is superior to clinical examination in its ability to detect compartment syndrome in pgis.

  14. Photonics-based strategies for minimally invasive cancer diagnosis using endogenous and molecular contrast

    NASA Astrophysics Data System (ADS)

    Sun, Jiantang

    Optical- and spectroscopic-based screening and imaging strategies possess unique advantages for minimally invasive cancer diagnosis. In this dissertation, we investigated how diagnostic results based on such techniques can be improved by the utilization of both endogenous and nanotechnology-facilitated molecular contrast. First, a diffusion-theory-based inversion reflectance model was constructed for the extraction of intrinsic tissue optical properties from the shape of normalized tissue diffusion reflectance spectra. The accuracy of our diffusion-based inversion algorithm was systematically assessed against Monte Carlo simulation as a function of probe geometry and tissue optical property combinations. By using this method, the spectral absorption and scattering coefficients of normal and cancerous tissue were efficiently retrieved within the center-to-center source-detector fiber separation of 0.5 mm ˜3 mm, which is compatible with endoscopic specifications. The presented inversion approach is computationally viable for eventual real-time in vivo tissue diagnostic applications. Second, novel quantum dot nanoparticle-based contrast agents were developed for molecular and tissue imaging applications in the visible and near-infrared (NIR) spectral ranges. Specifically, lead sulfide quantum dot bioconjugates were explored as NIR contrast agents for targeted molecular imaging; a protease-activated quantum dot probe was developed to monitor specific molecular targets and pathways through optical strategies; and a phantom study was conducted to assess the utilization of lead sulfide NIR quantum dots as fluorescent contrast agents for deep tissue imaging applications. These nanoengineered exogenous probes were shown to have the potential to significantly improve the implementation of optical/spectroscopic cancer imaging techniques. Taken together, the goal of the combined projects in this dissertation was to demonstrate that photonics-based minimally invasive cancer

  15. 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.

  16. 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.

  17. A PSO-based rule extractor for medical diagnosis.

    PubMed

    Hsieh, Yi-Zeng; Su, Mu-Chun; Wang, Pa-Chun

    2014-06-01

    One of the major bottlenecks in applying conventional neural networks to the medical field is that it is very difficult to interpret, in a physically meaningful way, because the learned knowledge is numerically encoded in the trained synaptic weights. In one of our previous works, we proposed a class of Hyper-Rectangular Composite Neural Networks (HRCNNs) of which synaptic weights can be interpreted as a set of crisp If-Then rules; however, a trained HRCNN may result in some ineffective If-Then rules which can only justify very few positive examples (i.e., poor generalization). This motivated us to propose a PSO-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) which applies particle swarm optimization (PSO) to trim the rules generated by a trained HRCNN while the recognition performance will not be degraded or even be improved. The performance of the proposed PFHRCNN is demonstrated on three benchmark medical databases including liver disorders data set, the breast cancer data set and the Parkinson's disease data set. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. 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.

  19. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition

    PubMed Central

    Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao

    2017-01-01

    Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field. PMID:28772943

  20. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.

    PubMed

    Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao

    2017-05-25

    Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

  1. 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.

  2. Validity of a Claims-Based Diagnosis of Obesity Among Medicare Beneficiaries.

    PubMed

    Lloyd, Jennifer T; Blackwell, Steve A; Wei, Iris I; Howell, Benjamin L; Shrank, William H

    2015-12-01

    Population-level data on obesity are difficult to obtain. Claims-based data sets are useful for studying public health at a population level but lack physical measurements. The objective of this study was to determine the validity of a claims-based measure of obesity compared to obesity diagnosed with clinical data as well as the validity among older adults who suffer from chronic disease. This study used data from the National Health and Nutrition Examination Survey 1999-2004 for adults aged ≥ 65 successfully linked to 1999-2007 Medicare claims (N = 3,554). Sensitivity, specificity, positive and negative predictive values, κ statistics as well as logistic regression analyses were computed for the claims-based diagnosis of obesity versus obesity diagnosed with body mass index. The claims-based diagnosis of obesity underestimates the true prevalence in the older Medicare population with a low sensitivity (18.4%). However, this method has a high specificity (97.3%) and is accurate when it is present. Sensitivity was improved when comparing the claim-based diagnosis to Class II obesity (34.2%) and when used in combination with chronic conditions such as diabetes, congestive heart failure, chronic obstructive pulmonary disease, or depression. Understanding the validity of a claims-based obesity diagnosis could aid researchers in understanding the feasibility of conducting research on obesity using claims data.

  3. Work disability before and after diabetes diagnosis: a nationwide population-based register study in Sweden.

    PubMed

    Ervasti, Jenni; Virtanen, Marianna; Pentti, Jaana; Lallukka, Tea; Tinghög, Petter; Kjeldgard, Linnea; Mittendorfer-Rutz, Ellenor; Alexanderson, Kristina

    2015-06-01

    We evaluated the risk of work disability (sick leave and disability pension) before and after diabetes diagnosis relative to individuals without diabetes during the same time period, as well as the trajectory of work disability around the diagnosis. This Swedish population-based cohort study with register data included 14 428 individuals with incident diabetes in 2006 and 39 702 individuals without diabetes during 2003 to 2009. Work disability was substantially higher among people with diabetes (overall mean = 95 days per year over the 7 years, SD = 143) than among those without diabetes (mean = 35 days, SD = 95). The risk of work disability was slightly higher after diabetes diagnosis than before and compared with the risk of those without diabetes. The trajectory of work disability was already increasing before diagnosis, increased even more at the time of diagnosis, and leveled off after diagnosis. Individual sociodemographic characteristics and comorbid conditions contributed both to the risk and to the trajectory of work disability. Although diabetes has an independent effect on work disability, sex, age, education, and comorbid conditions play a significant role.

  4. Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

    PubMed

    Park, Jee Soo; Choi, Soo Beom; Kim, Hee Jung; Cho, Nam Hoon; Kim, Sang Wun; Kim, Young Tae; Nam, Eun Ji; Chung, Jai Won; Kim, Deok Won

    2016-01-01

    Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes. We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine. The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%. We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.

  5. 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

  6. Sexuality after a cancer diagnosis: A population-based study.

    PubMed

    Jackson, Sarah E; Wardle, Jane; Steptoe, Andrew; Fisher, Abigail

    2016-12-15

    This study explored differences in sexual activity, function, and concerns between cancer survivors and cancer-free controls in a population-based study. The data were from 2982 men and 3708 women who were 50 years old or older and were participating in the English Longitudinal Study of Ageing. Sexual well-being was assessed with the Sexual Relationships and Activities Questionnaire, and cancer diagnoses were self-reported. There were no differences between cancer survivors and controls in levels of sexual activity (76.0% vs 78.5% for men and 58.2% vs 55.5% for women) or sexual function. Men and women with cancer diagnoses were more dissatisfied with their sex lives than controls (age-adjusted percentages: 30.9% vs 19.8% for men [P = .023] and 18.2% vs 11.8% for women [P = .034]), and women with cancer were more concerned about levels of sexual desire (10.2% vs 7.1%; P = .006). Women diagnosed < 5 years ago were more likely to report difficulty with becoming aroused (55.4% vs 31.8%; P = .016) and achieving orgasm (60.6% vs 28.3%; P < .001) and were more concerned about sexual desire (14.8% vs 7.1%; P = .007) and orgasmic experience (17.6% vs 7.1%; P = .042) than controls, but there were no differences in men. Self-reports of sexual activity and functioning in older people with cancer are broadly comparable to age-matched, cancer-free controls. There is a need to identify the causes of sexual dissatisfaction among long-term cancer survivors despite apparently normal levels of sexual activity and function for their age. The development of interventions addressing low sexual desire and problems with sexual functioning in women is also important and may be particularly relevant for cancer survivors after treatment. Cancer 2016;122:3883-3891. © 2016 American Cancer Society. © 2016 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

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

    PubMed

    Donadieu, Jean; Fenneteau, Odile; Beaupain, Blandine; Mahlaoui, Nizar; Chantelot, Christine Bellanné

    2011-05-19

    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-hematopoietic 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

  8. Sexuality after a cancer diagnosis: A population‐based study

    PubMed Central

    Wardle, Jane; Steptoe, Andrew; Fisher, Abigail

    2016-01-01

    BACKGROUND This study explored differences in sexual activity, function, and concerns between cancer survivors and cancer‐free controls in a population‐based study. METHODS The data were from 2982 men and 3708 women who were 50 years old or older and were participating in the English Longitudinal Study of Ageing. Sexual well‐being was assessed with the Sexual Relationships and Activities Questionnaire, and cancer diagnoses were self‐reported. RESULTS There were no differences between cancer survivors and controls in levels of sexual activity (76.0% vs 78.5% for men and 58.2% vs 55.5% for women) or sexual function. Men and women with cancer diagnoses were more dissatisfied with their sex lives than controls (age‐adjusted percentages: 30.9% vs 19.8% for men [P = .023] and 18.2% vs 11.8% for women [P = .034]), and women with cancer were more concerned about levels of sexual desire (10.2% vs 7.1%; P = .006). Women diagnosed < 5 years ago were more likely to report difficulty with becoming aroused (55.4% vs 31.8%; P = .016) and achieving orgasm (60.6% vs 28.3%; P < .001) and were more concerned about sexual desire (14.8% vs 7.1%; P = .007) and orgasmic experience (17.6% vs 7.1%; P = .042) than controls, but there were no differences in men. CONCLUSIONS Self‐reports of sexual activity and functioning in older people with cancer are broadly comparable to age‐matched, cancer‐free controls. There is a need to identify the causes of sexual dissatisfaction among long‐term cancer survivors despite apparently normal levels of sexual activity and function for their age. The development of interventions addressing low sexual desire and problems with sexual functioning in women is also important and may be particularly relevant for cancer survivors after treatment. Cancer 2016;122:3883–3891. © 2016 American Cancer Society. PMID:27531631

  9. 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

  10. Digoxin use after diagnosis of breast cancer and survival: a population-based cohort study.

    PubMed

    Karasneh, Reema A; Murray, Liam J; Mc Menamin, Úna C; Hughes, Carmel M; Cardwell, Chris R

    2015-06-01

    Digoxin has been shown to have an estrogenic effect and is associated with increased risk of gynecomastia and estrogen-sensitive cancers such as breast and uterus cancer. These findings, particularly recent observations of increased breast cancer risk, raise questions about the safety of digoxin use in breast cancer patients. Therefore, we investigated whether digoxin use after breast cancer diagnosis increased the risk of breast cancer-specific mortality in breast cancer patients. A cohort of 17,842 breast cancer patients newly diagnosed from 1998 to 2009 was identified from English cancer registries (from the National Cancer Data Repository). This cohort was linked to the UK Clinical Practice Research Datalink (to provide digoxin and other prescription records) and to the Office of National Statistics mortality data (to identify breast cancer-specific deaths). Using time-dependent Cox regression models, unadjusted and adjusted hazard ratios (HR) and 95 % confidence intervals (CIs) were calculated for the association between post-diagnostic exposure to digoxin and breast cancer-specific and all-cause mortality. In 17,842 breast cancer patients, there were 2219 breast cancer-specific deaths. Digoxin users appeared to have increased breast cancer-specific mortality compared with non-users (HR 1.73; 95 % CI 1.39-2.15) but this association was entirely attenuated after adjustment for potential confounders (adjusted HR 0.91; 95 % CI 0.72-1.14). In this large population-based breast cancer cohort study, there was little evidence of an increase in breast cancer-specific mortality with digoxin use after diagnosis. These results provide some reassurance that digoxin use is safe in breast cancer patients.

  11. Computed tomography-based diagnosis of diffuse compensatory enlargement of coronary arteries using scaling power laws

    PubMed Central

    Huo, Yunlong; Choy, Jenny Susana; Wischgoll, Thomas; Luo, Tong; Teague, Shawn D.; Bhatt, Deepak L.; Kassab, Ghassan S.

    2013-01-01

    Glagov's positive remodelling in the early stages of coronary atherosclerosis often results in plaque rupture and acute events. Because positive remodelling is generally diffused along the epicardial coronary arterial tree, it is difficult to diagnose non-invasively. Hence, the objective of the study is to assess the use of scaling power law for the diagnosis of positive remodelling of coronary arteries based on computed tomography (CT) images. Epicardial coronary arterial trees were reconstructed from CT scans of six Ossabaw pigs fed on a high-fat, high-cholesterol, atherogenic diet for eight months as well as the same number of body-weight-matched farm pigs fed on a lean chow (101.9±16.1 versus 91.5±13.1 kg). The high-fat diet Ossabaw pig model showed diffuse positive remodelling of epicardial coronary arteries. Good fit of measured coronary data to the length–volume scaling power law ( where Lc and Vc are crown length and volume) were found for both the high-fat and control groups (R2 = 0.95±0.04 and 0.99±0.01, respectively). The coefficient, KLV, decreased significantly in the high-fat diet group when compared with the control (14.6±2.6 versus 40.9±5.6). The flow–length scaling power law, however, was nearly unaffected by the positive remodelling. The length–volume and flow–length scaling power laws were preserved in epicardial coronary arterial trees after positive remodelling. KLV < 18 in the length–volume scaling relation is a good index of positive remodelling of coronary arteries. These findings provide a clinical rationale for simple, accurate and non-invasive diagnosis of positive remodelling of coronary arteries, using conventional CT scans. PMID:23365197

  12. Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

    NASA Astrophysics Data System (ADS)

    Li, Dan; Yang, Haizhen; Liang, XiaoFeng

    2010-11-01

    Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

  13. 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.

  14. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.

    PubMed

    Faust, Oliver; Acharya, U Rajendra; Adeli, Hojjat; Adeli, Amir

    2015-03-01

    Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy. Copyright © 2015 British Epilepsy Association. All rights reserved.

  15. Application of PCR-based methods for diagnosis of intestinal parasitic infections in the clinical laboratory.

    PubMed

    Verweij, Jaco J

    2014-12-01

    For many years PCR- and other DNA-based methods of pathogen detection have been available in most clinical microbiology laboratories; however, until recently these tools were not routinely exploited for the diagnosis of parasitic infections. Laboratories were initially reluctant to implement PCR as incorporation of such assays within the algorithm of tools available for the most accurate diagnosis of a large variety of parasites was unclear. With regard to diagnosis of intestinal parasitic infections, the diversity of parasites that one can expect in most settings is far less than the parasitological textbooks would have you believe, hence developing a simplified diagnostic triage is feasible. Therefore the classical algorithm based on population, patient groups, use of immuno-suppressive drugs, travel history etc. is also applicable to decide when to perform and which additional techniques are to be used, if a multiplex PCR panel is used as a first-line screening diagnostic.

  16. Infrared thermography based on artificial intelligence as a screening method for carpal tunnel syndrome diagnosis.

    PubMed

    Jesensek Papez, B; Palfy, M; Mertik, M; Turk, Z

    2009-01-01

    This study further evaluated a computer-based infrared thermography (IRT) system, which employs artificial neural networks for the diagnosis of carpal tunnel syndrome (CTS) using a large database of 502 thermal images of the dorsal and palmar side of 132 healthy and 119 pathological hands. It confirmed the hypothesis that the dorsal side of the hand is of greater importance than the palmar side when diagnosing CTS thermographically. Using this method it was possible correctly to classify 72.2% of all hands (healthy and pathological) based on dorsal images and > 80% of hands when only severely affected and healthy hands were considered. Compared with the gold standard electromyographic diagnosis of CTS, IRT cannot be recommended as an adequate diagnostic tool when exact severity level diagnosis is required, however we conclude that IRT could be used as a screening tool for severe cases in populations with high ergonomic risk factors of CTS.

  17. 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.

  18. A blood-based, 7-metabolite signature for the early diagnosis of Alzheimer's disease.

    PubMed

    Olazarán, Javier; Gil-de-Gómez, Luis; Rodríguez-Martín, Andrés; Valentí-Soler, Meritxell; Frades-Payo, Belén; Marín-Muñoz, Juan; Antúnez, Carmen; Frank-García, Ana; Acedo-Jiménez, Carmen; Morlán-Gracia, Lorenzo; Petidier-Torregrossa, Roberto; Guisasola, María Concepción; Bermejo-Pareja, Félix; Sánchez-Ferro, Álvaro; Pérez-Martínez, David A; Manzano-Palomo, Sagrario; Farquhar, Ruth; Rábano, Alberto; Calero, Miguel

    2015-01-01

    Accurate blood-based biomarkers of Alzheimer's disease (AD) could constitute simple, inexpensive, and non-invasive tools for the early diagnosis and treatment of this devastating neurodegenerative disease. We sought to develop a robust AD biomarker panel by identifying alterations in plasma metabolites that persist throughout the continuum of AD pathophysiology. Using a multicenter, cross-sectional study design, we based our analysis on metabolites whose levels were altered both in AD patients and in patients with amnestic mild cognitive impairment (aMCI), the earliest identifiable stage of AD. UPLC coupled to mass spectrometry was used to independently compare the levels of 495 plasma metabolites in aMCI (n = 58) and AD (n = 100) patients with those of normal cognition controls (NC, n = 93). Metabolite alterations common to both aMCI and AD patients were used to generate a logistic regression model that accurately distinguished AD from NC patients. The final panel consisted of seven metabolites: three amino acids (glutamic acid, alanine, and aspartic acid), one non-esterified fatty acid (22:6n-3, DHA), one bile acid (deoxycholic acid), one phosphatidylethanolamine [PE(36:4)], and one sphingomyelin [SM(39:1)]. Detailed analysis ruled out the influence of potential confounding variables, including comorbidities and treatments, on each of the seven biomarkers. The final model accurately distinguished AD from NC patients (AUC, 0.918). Importantly, the model also distinguished aMCI from NC patients (AUC, 0.826), indicating its potential diagnostic utility in early disease stages. These findings describe a sensitive biomarker panel that may facilitate the specific detection of early-stage AD through the analysis of plasma samples.

  19. 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.

  20. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

    PubMed Central

    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. Life-Cycle-Management of CV Cables based on Degradation Diagnosis

    NASA Astrophysics Data System (ADS)

    Kato, Takeyoshi; Niwa, Mamoru; Suzuoki, Yasuo

    Life-cycle management combined with degradation diagnosis is useful for preventing an unexpected failure and extending service life of electric power apparatuses, minimizing life-cycle cost. We examined a method of life-cycle management based on TBM (Time-Based Maintenance) and CBM (Condition Based Maintenance), and evaluated the economic effect of degradation diagnosis. To carry out reliable life cycle management, however, accurate data on the relation between extent of degradation and failure probability or remaining life as well as a well-established diagnostic method are necessary. In this paper, we examine the influence of accuracy of the data used to determine the optimum diagnostic parameters and evaluate how the life-cycle cost is affected by the employment of inaccurate data. As more condition-oriented life-cycle management, we discuss the effects of a method of life-cycle management based on two diagnosis intervals in terms of reliable life-cycle management. We also discuss the measurement sensitivity necessary for an effective asset management based on diagnosis.

  2. An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

    NASA Astrophysics Data System (ADS)

    Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

    2017-03-01

    Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.

  3. 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.

  4. 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

  5. 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.

  6. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    PubMed

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

  7. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

    PubMed Central

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID

  8. Application of simulation models to the diagnosis of MSW landfills: an example.

    PubMed

    García de Cortázar, Amaya Lobo; Tejero Monzón, Iñaki

    2007-01-01

    Among the landfill simulation programs being developed by several research groups around the world as tools for the management of sanitary landfills is MODUELO, whose second version, MODUELO 2, has been presented elsewhere. It reproduces the operational history of the landfill and its hydrologic and biodegradation processes, allowing the estimation of the flow and pollutants emitted in the leachate and the generated landfill gas over time. This program has been used for a diagnosis study of an existing European MSW landfill. The construction and calibration of the facility's hydrologic model, based on the available data, allowed the detection and quantification of two sources increasing the flows reaching the leachate collection system: a small portion (6-7%) of the runoff over the landfill surface and the contribution of water coming from external hillsides of the same watershed that represent a total surface area of around 20ha. The contrast of the leachate quality (COD, BOD, NH(4)-N and TKN) simulation results and measured data showed the potential of these models for the assessment of other significant aspects in landfill operation such as the potential harnessing of the landfill gas. Nonetheless, in this case as in many others, the accuracy of the simulation results was limited by the scant quality of the available data, which highlights the need for implementing continuous monitoring and characterizing protocols to take advantage of these programs as a tool for landfill optimization.

  9. 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…

  10. 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…

  11. 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.

  12. 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…

  13. 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…

  14. Relations among system diagnosis models with three-valued test outcomes

    SciTech Connect

    Butler, J.T.

    1983-01-01

    Three models of multiprocessing systems are compared on the basis of the accuracy of the diagnosis of faulty processors. One model is the conventional system with binary-valued test outcomes (pass and fail). The other models have three-valued test outcomes, where the third value is either a missing or an incorrect test result. It is shown that, in general multiprocessing systems, there is a hierarchy among the three models with respect to diagnosability. However, in systems where no two processors test each other, the models are on par, and established criteria for diagnosability in binary systems can be used in both of the three-valued systems. 12 references.

  15. MYCIN: A Knowledge-Based Consultation Program for Infectious Disease Diagnosis.

    ERIC Educational Resources Information Center

    van Melle, William

    1978-01-01

    Describes the structure of MYCIN, a computer-based consultation system designed to assist physicians in the diagnosis of and therapy selection for patients with bacterial infections, and an explanation system which can answer simple English questions in order to justify its advice or educate the user. (Author/VT)

  16. MYCIN: A Knowledge-Based Consultation Program for Infectious Disease Diagnosis.

    ERIC Educational Resources Information Center

    van Melle, William

    1978-01-01

    Describes the structure of MYCIN, a computer-based consultation system designed to assist physicians in the diagnosis of and therapy selection for patients with bacterial infections, and an explanation system which can answer simple English questions in order to justify its advice or educate the user. (Author/VT)

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