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.

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.

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

EyeMIAS: a cloud-based ophthalmic image reading and auxiliary diagnosis system

NASA Astrophysics Data System (ADS)

Wu, Di; Zhao, Heming; Yu, Kai; Chen, Xinjian

2018-03-01

Relying solely on ophthalmic equipment is unable to meet the present health needs. It is urgent to find an efficient way to provide a quick screening and early diagnosis on diabetic retinopathy and other ophthalmic diseases. The purpose of this study is to develop a cloud-base system for medical image especially ophthalmic image to store, view and process and accelerate the screening and diagnosis. In this purpose the system with web application, upload client, storage dependency and algorithm support is implemented. After five alpha tests, the system bore the thousands of large traffic access and generated hundreds of reports with diagnosis.

Tuberculosis-Diagnostic Expert System: an architecture for translating patients information from the web for use in tuberculosis diagnosis.

PubMed

Osamor, Victor C; Azeta, Ambrose A; Ajulo, Oluseyi O

2014-12-01

Over 1.5-2 million tuberculosis deaths occur annually. Medical professionals are faced with a lot of challenges in delivering good health-care with unassisted automation in hospitals where there are several patients who need the doctor's attention. To automate the pre-laboratory screening process against tuberculosis infection to aid diagnosis and make it fast and accessible to the public via the Internet. The expert system we have built is designed to also take care of people who do not have access to medical experts, but would want to check their medical status. A rule-based approach has been used, and unified modeling language and the client-server architecture technique were applied to model the system and to develop it as a web-based expert system for tuberculosis diagnosis. Algorithmic rules in the Tuberculosis-Diagnosis Expert System necessitate decision coverage where tuberculosis is either suspected or not suspected. The architecture consists of a rule base, knowledge base, and patient database. These units interact with the inference engine, which receives patient' data through the Internet via a user interface. We present the architecture of the Tuberculosis-Diagnosis Expert System and its implementation. We evaluated it for usability to determine the level of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the system has a usability of 4.08 out of a scale of 5. This is an indication of a more-than-average system performance. Several existing expert systems have been developed for the purpose of supporting different medical diagnoses, but none is designed to translate tuberculosis patients' symptomatic data for online pre-laboratory screening. Our Tuberculosis-Diagnosis Expert System is an effective solution for the implementation of the needed web-based expert system diagnosis. © The Author(s) 2013.

Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets

NASA Technical Reports Server (NTRS)

Gaudel, Quentin; Ribot, Pauline; Chanthery, Elodie; Daigle, Matthew J.

2016-01-01

This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype.The diagnosis is performed by using a model-based method in the context of health management of hybrid systems.In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

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.

Deductive Error Diagnosis and Inductive Error Generalization for Intelligent Tutoring Systems.

ERIC Educational Resources Information Center

Hoppe, H. Ulrich

1994-01-01

Examines the deductive approach to error diagnosis for intelligent tutoring systems. Topics covered include the principles of the deductive approach to diagnosis; domain-specific heuristics to solve the problem of generalizing error patterns; and deductive diagnosis and the hypertext-based learning environment. (Contains 26 references.) (JLB)

Expert systems for diagnostic purposes, prospected applications to the radar field

NASA Astrophysics Data System (ADS)

Filippi, Riccardo

Expert systems applied to fault diagnosis, particularly electrical circuit troubleshooting, are introduced. Diagnostic systems consisting of sequences of rules of the symptom-disease type (rule based system) and systems based upon a physical and functional description of the unit subjected to fault diagnosis are treated. Application of such systems to radar equipment troubleshooting, in particular to the transmitter, is discussed.

Distributed Cooperation Solution Method of Complex System Based on MAS

NASA Astrophysics Data System (ADS)

Weijin, Jiang; Yuhui, Xu

To adapt the model in reconfiguring fault diagnosing to dynamic environment and the needs of solving the tasks of complex system fully, the paper introduced multi-Agent and related technology to the complicated fault diagnosis, an integrated intelligent control system is studied in this paper. Based on the thought of the structure of diagnostic decision and hierarchy in modeling, based on multi-layer decomposition strategy of diagnosis task, a multi-agent synchronous diagnosis federation integrated different knowledge expression modes and inference mechanisms are presented, the functions of management agent, diagnosis agent and decision agent are analyzed, the organization and evolution of agents in the system are proposed, and the corresponding conflict resolution algorithm in given, Layered structure of abstract agent with public attributes is build. System architecture is realized based on MAS distributed layered blackboard. The real world application shows that the proposed control structure successfully solves the fault diagnose problem of the complex plant, and the special advantage in the distributed domain.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

PubMed

Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R V Paul; Ostmo, Susan; Chiang, Michael F

2015-11-01

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Heartbeat-based error diagnosis framework for distributed embedded systems

NASA Astrophysics Data System (ADS)

Mishra, Swagat; Khilar, Pabitra Mohan

2012-01-01

Distributed Embedded Systems have significant applications in automobile industry as steer-by-wire, fly-by-wire and brake-by-wire systems. In this paper, we provide a general framework for fault detection in a distributed embedded real time system. We use heartbeat monitoring, check pointing and model based redundancy to design a scalable framework that takes care of task scheduling, temperature control and diagnosis of faulty nodes in a distributed embedded system. This helps in diagnosis and shutting down of faulty actuators before the system becomes unsafe. The framework is designed and tested using a new simulation model consisting of virtual nodes working on a message passing system.

Heartbeat-based error diagnosis framework for distributed embedded systems

NASA Astrophysics Data System (ADS)

Mishra, Swagat; Khilar, Pabitra Mohan

2011-12-01

Distributed Embedded Systems have significant applications in automobile industry as steer-by-wire, fly-by-wire and brake-by-wire systems. In this paper, we provide a general framework for fault detection in a distributed embedded real time system. We use heartbeat monitoring, check pointing and model based redundancy to design a scalable framework that takes care of task scheduling, temperature control and diagnosis of faulty nodes in a distributed embedded system. This helps in diagnosis and shutting down of faulty actuators before the system becomes unsafe. The framework is designed and tested using a new simulation model consisting of virtual nodes working on a message passing system.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Harmonic wavelet packet transform for on-line system health diagnosis

NASA Astrophysics Data System (ADS)

Yan, Ruqiang; Gao, Robert X.

2004-07-01

This paper presents a new approach to on-line health diagnosis of mechanical systems, based on the wavelet packet transform. Specifically, signals acquired from vibration sensors are decomposed into sub-bands by means of the discrete harmonic wavelet packet transform (DHWPT). Based on the Fisher linear discriminant criterion, features in the selected sub-bands are then used as inputs to three classifiers (Nearest Neighbor rule-based and two Neural Network-based), for system health condition assessment. Experimental results have confirmed that, comparing to the conventional approach where statistical parameters from raw signals are used, the presented approach enabled higher signal-to-noise ratio for more effective and intelligent use of the sensory information, thus leading to more accurate system health diagnosis.

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.

Autonomous Power System intelligent diagnosis and control

NASA Technical Reports Server (NTRS)

Ringer, Mark J.; Quinn, Todd M.; Merolla, Anthony

1991-01-01

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System.

Autonomous power system intelligent diagnosis and control

NASA Technical Reports Server (NTRS)

Ringer, Mark J.; Quinn, Todd M.; Merolla, Anthony

1991-01-01

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System.

An architecture for automated fault diagnosis. [Space Station Module/Power Management And Distribution

NASA Technical Reports Server (NTRS)

Ashworth, Barry R.

1989-01-01

A description is given of the SSM/PMAD power system automation testbed, which was developed using a systems engineering approach. The architecture includes a knowledge-based system and has been successfully used in power system management and fault diagnosis. Architectural issues which effect overall system activities and performance are examined. The knowledge-based system is discussed along with its associated automation implications, and interfaces throughout the system are presented.

Real-time diagnostics of the reusable rocket engine using on-line system identification

NASA Technical Reports Server (NTRS)

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

1990-01-01

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Fault management for data systems

NASA Technical Reports Server (NTRS)

Boyd, Mark A.; Iverson, David L.; Patterson-Hine, F. Ann

1993-01-01

Issues related to automating the process of fault management (fault diagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described.

PSG-EXPERT. An expert system for the diagnosis of sleep disorders.

PubMed

Fred, A; Filipe, J; Partinen, M; Paiva, T

2000-01-01

This paper describes PSG-EXPERT, an expert system in the domain of sleep disorders exploring polysomnographic data. The developed software tool is addressed from two points of view: (1)--as an integrated environment for the development of diagnosis-oriented expert systems; (2)--as an auxiliary diagnosis tool in the particular domain of sleep disorders. Developed over a Windows platform, this software tool extends one of the most popular shells--CLIPS (C Language Integrated Production System) with the following features: backward chaining engine; graph-based explanation facilities; knowledge editor including a fuzzy fact editor and a rules editor, with facts-rules integrity checking; belief revision mechanism; built-in case generator and validation module. It therefore provides graphical support for knowledge acquisition, edition, explanation and validation. From an application domain point of view, PSG-Expert is an auxiliary diagnosis system for sleep disorders based on polysomnographic data, that aims at assisting the medical expert in his diagnosis task by providing automatic analysis of polysomnographic data, summarising the results of this analysis in terms of a report of major findings and possible diagnosis consistent with the polysomnographic data. Sleep disorders classification follows the International Classification of Sleep Disorders. Major features of the system include: browsing on patients data records; structured navigation on Sleep Disorders descriptions according to ASDA definitions; internet links to related pages; diagnosis consistent with polysomnographic data; graphical user-interface including graph-based explanatory facilities; uncertainty modelling and belief revision; production of reports; connection to remote databases.

Computer-based rhythm diagnosis and its possible influence on nonexpert electrocardiogram readers.

PubMed

Hakacova, Nina; Trägårdh-Johansson, Elin; Wagner, Galen S; Maynard, Charles; Pahlm, Olle

2012-01-01

Systems providing computer-based analysis of the resting electrocardiogram (ECG) seek to improve the quality of health care by providing accurate and timely automatic diagnosis of, for example, cardiac rhythm to clinicians. The accuracy of these diagnoses, however, remains questionable. We tested the hypothesis that (a) 2 independent automated ECG systems have better accuracy in rhythm diagnosis than nonexpert clinicians and (b) both systems provide correct diagnostic suggestions in a large percentage of cases where the diagnosis of nonexpert clinicians is incorrect. Five hundred ECGs were manually analyzed by 2 senior experts, 3 nonexpert clinicians, and automatically by 2 automated systems. The accuracy of the nonexpert rhythm statements was compared with the accuracy of each system statement. The proportion of rhythm statements when the clinician's diagnoses were incorrect and the systems instead provided correct diagnosis was assessed. A total of 420 sinus rhythms and 156 rhythm disturbances were recognized by expert reading. Significance of the difference in accuracy between nonexperts and systems was P = .45 for system A and P = .11 for system B. The percentage of correct automated diagnoses in cases when the clinician was incorrect was 28% ± 10% for system A and 25% ± 11% for system B (P = .09). The rhythm diagnoses of automated systems did not reach better average accuracy than those of nonexpert readings. The computer diagnosis of rhythm can be incorrect in cases where the clinicians fail in reaching the correct ECG diagnosis. Copyright © 2012. Published by Elsevier Inc.

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.

[Development of expert diagnostic system for common respiratory diseases].

PubMed

Xu, Wei-hua; Chen, You-ling; Yan, Zheng

2014-03-01

To develop an internet-based expert diagnostic system for common respiratory diseases. SaaS system was used to build architecture; pattern of forward reasoning was applied for inference engine design; ASP.NET with C# from the tool pack of Microsoft Visual Studio 2005 was used for website-interview medical expert system.The database of the system was constructed with Microsoft SQL Server 2005. The developed expert system contained large data memory and high efficient function of data interview and data analysis for diagnosis of various diseases.The users were able to perform this system to obtain diagnosis for common respiratory diseases via internet. The developed expert system may be used for internet-based diagnosis of various respiratory diseases,particularly in telemedicine setting.

Formal Methods for Automated Diagnosis of Autosub 6000

NASA Technical Reports Server (NTRS)

Ernits, Juhan; Dearden, Richard; Pebody, Miles

2009-01-01

This is a progress report on applying formal methods in the context of building an automated diagnosis and recovery system for Autosub 6000, an Autonomous Underwater Vehicle (AUV). The diagnosis task involves building abstract models of the control system of the AUV. The diagnosis engine is based on Livingstone 2, a model-based diagnoser originally built for aerospace applications. Large parts of the diagnosis model can be built without concrete knowledge about each mission, but actual mission scripts and configuration parameters that carry important information for diagnosis are changed for every mission. Thus we use formal methods for generating the mission control part of the diagnosis model automatically from the mission script and perform a number of invariant checks to validate the configuration. After the diagnosis model is augmented with the generated mission control component model, it needs to be validated using verification techniques.

Artificial intelligence in hematology.

PubMed

Zini, Gina

2005-10-01

Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.

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

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

A new hybrid case-based reasoning approach for medical diagnosis systems.

PubMed

Sharaf-El-Deen, Dina A; Moawad, Ibrahim F; Khalifa, M E

2014-02-01

Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment.

PubMed

Li, Juanli; Xie, Jiacheng; Yang, Zhaojian; Li, Junjie

2018-06-13

To reduce the difficulty of acquiring and transmitting data in mining hoist fault diagnosis systems and to mitigate the low efficiency and unreasonable reasoning process problems, a fault diagnosis method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A fault diagnosis reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and fault diagnosis reasoning is performed. Based on interactive technology, a humanized and visualized fault diagnosis platform is created in the application layer. The method is then verified. A fault diagnosis test of the mine hoisting mechanism shows that the proposed diagnosis method obtains complete diagnostic data, and the diagnosis results have high accuracy and reliability.

A Knowledge-Based System for the Computer Assisted Diagnosis of Endoscopic Images

NASA Astrophysics Data System (ADS)

Kage, Andreas; Münzenmayer, Christian; Wittenberg, Thomas

Due to the actual demographic development the use of Computer-Assisted Diagnosis (CAD) systems becomes a more important part of clinical workflows and clinical decision making. Because changes on the mucosa of the esophagus can indicate the first stage of cancerous developments, there is a large interest to detect and correctly diagnose any such lesion. We present a knowledge-based system which is able to support a physician with the interpretation and diagnosis of endoscopic images of the esophagus. Our system is designed to support the physician directly during the examination of the patient, thus prodving diagnostic assistence at the point of care (POC). Based on an interactively marked region in an endoscopic image of interest, the system provides a diagnostic suggestion, based on an annotated reference image database. Furthermore, using relevant feedback mechanisms, the results can be enhanced interactively.

Visualization of suspicious lesions in breast MRI based on intelligent neural systems

NASA Astrophysics Data System (ADS)

Twellmann, Thorsten; Lange, Oliver; Nattkemper, Tim Wilhelm; Meyer-Bäse, Anke

2006-05-01

Intelligent medical systems based on supervised and unsupervised artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems represent an important component of future sophisticated computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

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.

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.

Towards a Framework for Evaluating and Comparing Diagnosis Algorithms

NASA Technical Reports Server (NTRS)

Kurtoglu, Tolga; Narasimhan, Sriram; Poll, Scott; Garcia,David; Kuhn, Lukas; deKleer, Johan; vanGemund, Arjan; Feldman, Alexander

2009-01-01

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.

A data structure and algorithm for fault diagnosis

NASA Technical Reports Server (NTRS)

Bosworth, Edward L., Jr.

1987-01-01

Results of preliminary research on the design of a knowledge based fault diagnosis system for use with on-orbit spacecraft such as the Hubble Space Telescope are presented. A candidate data structure and associated search algorithm from which the knowledge based system can evolve is discussed. This algorithmic approach will then be examined in view of its inability to diagnose certain common faults. From that critique, a design for the corresponding knowledge based system will be given.

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.

Intelligent model-based diagnostics for vehicle health management

NASA Astrophysics Data System (ADS)

Luo, Jianhui; Tu, Fang; Azam, Mohammad S.; Pattipati, Krishna R.; Willett, Peter K.; Qiao, Liu; Kawamoto, Masayuki

2003-08-01

The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.

A fuzzy system for helping medical diagnosis of malformations of cortical development.

PubMed

Alayón, Silvia; Robertson, Richard; Warfield, Simon K; Ruiz-Alzola, Juan

2007-06-01

Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper.

A Fuzzy System for Helping Medical Diagnosis of Malformations of Cortical Development

PubMed Central

Alayón, Silvia; Robertson, Richard; Warfield, Simon K.; Ruiz-Alzola, Juan

2007-01-01

Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper. PMID:17197247

A method for diagnosing time dependent faults using model-based reasoning systems

NASA Technical Reports Server (NTRS)

Goodrich, Charles H.

1995-01-01

This paper explores techniques to apply model-based reasoning to equipment and systems which exhibit dynamic behavior (that which changes as a function of time). The model-based system of interest is KATE-C (Knowledge based Autonomous Test Engineer) which is a C++ based system designed to perform monitoring and diagnosis of Space Shuttle electro-mechanical systems. Methods of model-based monitoring and diagnosis are well known and have been thoroughly explored by others. A short example is given which illustrates the principle of model-based reasoning and reveals some limitations of static, non-time-dependent simulation. This example is then extended to demonstrate representation of time-dependent behavior and testing of fault hypotheses in that environment.

Evaluation of the WHO criteria for the classification of patients with mastocytosis.

PubMed

Sánchez-Muñoz, Laura; Alvarez-Twose, Ivan; García-Montero, Andrés C; Teodosio, Cristina; Jara-Acevedo, María; Pedreira, Carlos E; Matito, Almudena; Morgado, Jose Mario T; Sánchez, Maria Luz; Mollejo, Manuela; Gonzalez-de-Olano, David; Orfao, Alberto; Escribano, Luis

2011-09-01

Diagnosis and classification of mastocytosis is currently based on the World Health Organization (WHO) criteria. Here, we evaluate the utility of the WHO criteria for the diagnosis and classification of a large series of mastocytosis patients (n=133), and propose a new algorithm that could be routinely applied for refined diagnosis and classification of the disease. Our results confirm the utility of the WHO criteria and provide evidence for the need of additional information for (1) a more precise diagnosis of mastocytosis, (2) specific identification of new forms of the disease, (3) the differential diagnosis between cutaneous mastocytosis vs systemic mastocytosis, and (4) improved distinction between indolent systemic mastocytosis and aggressive systemic mastocytosis. Based on our results, a new algorithm is proposed for a better diagnostic definition and prognostic classification of mastocytosis, as confirmed prospectively in an independent validation series of 117 mastocytosis patients.

Fault isolation detection expert (FIDEX). Part 1: Expert system diagnostics for a 30/20 Gigahertz satellite transponder

NASA Technical Reports Server (NTRS)

Durkin, John; Schlegelmilch, Richard; Tallo, Donald

1992-01-01

LeRC has recently completed the design of a Ka-band satellite transponder system, as part of the Advanced Communication Technology Satellite (ACTS) System. To enhance the reliability of this satellite, NASA funded the University of Akron to explore the application of an expert system to provide the transponder with an autonomous diagnosis capability. The results of this research was the development of a prototype diagnosis expert system called FIDEX (fault-isolation and diagnosis expert). FIDEX is a frame-based expert system that was developed in the NEXPERT Object development environment by Neuron Data, Inc. It is a MicroSoft Windows version 3.0 application, and was designed to operate on an Intel i80386 based personal computer system.

Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer.

PubMed

Safdari, Reza; Arpanahi, Hadi Kazemi; Langarizadeh, Mostafa; Ghazisaiedi, Marjan; Dargahi, Hossein; Zendehdel, Kazem

2018-01-01

Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer. to develop a fuzzy expert system that can identify gastric cancer risk levels in individuals. This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert's opinion. 50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert's diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%. The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.

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.

Methods and means of diagnostics of oncological diseases on the basis of pattern recognition: intelligent morphological systems - problems and solutions

NASA Astrophysics Data System (ADS)

Nikitaev, V. G.

2017-01-01

The development of methods of pattern recognition in modern intelligent systems of clinical cancer diagnosis are discussed. The histological (morphological) diagnosis - primary diagnosis for medical setting with cancer are investigated. There are proposed: interactive methods of recognition and structure of intellectual morphological complexes based on expert training-diagnostic and telemedicine systems. The proposed approach successfully implemented in clinical practice.

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

A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence

NASA Astrophysics Data System (ADS)

Jiang, Guodong; Fan, Ming; Li, Lihua

2016-03-01

Mammography is the gold standard for breast cancer screening, reducing mortality by about 30%. The application of a computer-aided detection (CAD) system to assist a single radiologist is important to further improve mammographic sensitivity for breast cancer detection. In this study, a design and realization of the prototype for remote diagnosis system in mammography based on cloud platform were proposed. To build this system, technologies were utilized including medical image information construction, cloud infrastructure and human-machine diagnosis model. Specifically, on one hand, web platform for remote diagnosis was established by J2EE web technology. Moreover, background design was realized through Hadoop open-source framework. On the other hand, storage system was built up with Hadoop distributed file system (HDFS) technology which enables users to easily develop and run on massive data application, and give full play to the advantages of cloud computing which is characterized by high efficiency, scalability and low cost. In addition, the CAD system was realized through MapReduce frame. The diagnosis module in this system implemented the algorithms of fusion of machine and human intelligence. Specifically, we combined results of diagnoses from doctors' experience and traditional CAD by using the man-machine intelligent fusion model based on Alpha-Integration and multi-agent algorithm. Finally, the applications on different levels of this system in the platform were also discussed. This diagnosis system will have great importance for the balanced health resource, lower medical expense and improvement of accuracy of diagnosis in basic medical institutes.

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…

Algebraic Approaches for Scalable End-to-End Monitoring and Diagnosis

NASA Astrophysics Data System (ADS)

Zhao, Yao; Chen, Yan

The rigidity of the Internet architecture led to flourish in the research of end-to-end based systems. In this chapter, we describe a linear algebra-based end-to-end monitoring and diagnosis system. We first propose a tomography-based overlay monitoring system (TOM). Given n end hosts, TOM selectively monitors a basis set of O(nlogn) paths out of all n(n - 1) end-to-end paths. Any end-to-end path can be written as a unique linear combination of paths in the basis set. Consequently, by monitoring loss rates for the paths in the basis set, TOM infers loss rates for all end-to-end paths. Furthermore, leveraging on the scalable measurements from the TOM system, we propose the Least-biased End-to-End Network Diagnosis (in short, LEND) system. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. LEND applies an algebraic approach to find out the MILSes and infers the properties of the MILSes efficiently. This also means LEND system achieves the finest diagnosis granularity under the least biased statistical assumptions.

Developing an interactive teleradiology system for SARS diagnosis

NASA Astrophysics Data System (ADS)

Sun, Jianyong; Zhang, Jianguo; Zhuang, Jun; Chen, Xiaomeng; Yong, Yuanyuan; Tan, Yongqiang; Chen, Liu; Lian, Ping; Meng, Lili; Huang, H. K.

2004-04-01

Severe acute respiratory syndrome (SARS) is a respiratory illness that had been reported in Asia, North America, and Europe in last spring. Most of the China cases of SARS have occurred by infection in hospitals or among travelers. To protect the physicians, experts and nurses from the SARS during the diagnosis and treatment procedures, the infection control mechanisms were built in SARS hospitals. We built a Web-based interactive teleradiology system to assist the radiologists and physicians both in side and out side control area to make image diagnosis. The system consists of three major components: DICOM gateway (GW), Web-based image repository server (Server), and Web-based DICOM viewer (Viewer). This system was installed and integrated with CR, CT and the hospital information system (HIS) in Shanghai Xinhua hospital to provide image-based ePR functions for SARS consultation between the radiologists, physicians and experts inside and out side control area. The both users inside and out side the control area can use the system to process and manipulate the DICOM images interactively, and the system provide the remote control mechanism to synchronize their operations on images and display.

DEVELOPMENT PLAN FOR THE CAUSAL ANALYSIS / DIAGNOSIS DECISION INFORMATION SYSTEM (CADDIS) 2001-2004

EPA Science Inventory

The Causal Analysis/Diagnosis Decision Information System (CADDIS) is a web-based system that provides technical support for states, tribes and other users of the Office of Water's Stressor Identification Guidance. The Stressor Identific...

Expert Systems: Implications for the Diagnosis and Treatment of Learning Disabilities.

ERIC Educational Resources Information Center

Hofmeister, Alan M.; Lubke, Margaret M.

1988-01-01

The article examines characteristics and present or potential applications of expert systems technology for diagnosis and treatment of learning disabilities. Preliminary findings indicate that expert systems can perform as well as humans in specific areas, and that the process of organizing knowledge bases for expert systems helps clarify existing…

[Computer-aided Diagnosis and New Electronic Stethoscope].

PubMed

Huang, Mei; Liu, Hongying; Pi, Xitian; Ao, Yilu; Wang, Zi

2017-05-30

Auscultation is an important method in early-diagnosis of cardiovascular disease and respiratory system disease. This paper presents a computer-aided diagnosis of new electronic auscultation system. It has developed an electronic stethoscope based on condenser microphone and the relevant intelligent analysis software. It has implemented many functions that combined with Bluetooth, OLED, SD card storage technologies, such as real-time heart and lung sounds auscultation in three modes, recording and playback, auscultation volume control, wireless transmission. The intelligent analysis software based on PC computer utilizes C# programming language and adopts SQL Server as the background database. It has realized play and waveform display of the auscultation sound. By calculating the heart rate, extracting the characteristic parameters of T1, T2, T12, T11, it can analyze whether the heart sound is normal, and then generate diagnosis report. Finally the auscultation sound and diagnosis report can be sent to mailbox of other doctors, which can carry out remote diagnosis. The whole system has features of fully function, high portability, good user experience, and it is beneficial to promote the use of electronic stethoscope in the hospital, at the same time, the system can also be applied to auscultate teaching and other occasions.

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 examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

A systems engineering approach to automated failure cause diagnosis in space power systems

NASA Technical Reports Server (NTRS)

Dolce, James L.; Faymon, Karl A.

1987-01-01

Automatic failure-cause diagnosis is a key element in autonomous operation of space power systems such as Space Station's. A rule-based diagnostic system has been developed for determining the cause of degraded performance. The knowledge required for such diagnosis is elicited from the system engineering process by using traditional failure analysis techniques. Symptoms, failures, causes, and detector information are represented with structured data; and diagnostic procedural knowledge is represented with rules. Detected symptoms instantiate failure modes and possible causes consistent with currently held beliefs about the likelihood of the cause. A diagnosis concludes with an explanation of the observed symptoms in terms of a chain of possible causes and subcauses.

A fault diagnosis system for PV power station based on global partitioned gradually approximation method

NASA Astrophysics Data System (ADS)

Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

2016-08-01

As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

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.

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.

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

PubMed Central

Yuan, Xianfeng; Song, Mumin; 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

Software For Fault-Tree Diagnosis Of A System

NASA Technical Reports Server (NTRS)

Iverson, Dave; Patterson-Hine, Ann; Liao, Jack

1993-01-01

Fault Tree Diagnosis System (FTDS) computer program is automated-diagnostic-system program identifying likely causes of specified failure on basis of information represented in system-reliability mathematical models known as fault trees. Is modified implementation of failure-cause-identification phase of Narayanan's and Viswanadham's methodology for acquisition of knowledge and reasoning in analyzing failures of systems. Knowledge base of if/then rules replaced with object-oriented fault-tree representation. Enhancement yields more-efficient identification of causes of failures and enables dynamic updating of knowledge base. Written in C language, C++, and Common LISP.

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.

Data and knowledge in medical distributed applications.

PubMed

Serban, Alexandru; Crişan-Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara

2014-01-01

Building a clinical decision support system (CDSS) capable to collect process and diagnose data from the patients automatically, based on information, symptoms and investigations is one of the current challenges for researchers and medical science. The purpose of the current study is to design a cloud-based CDSS to improve patient safety, quality of care and organizational efficiency. It presents the design of a cloud-based application system using a medical based approach, which covers different diseases to diagnosis, differentiated on most important pathologies. Using online questionnaires, traditional and new data will be collected from patients. After data input, the application will formulate a presumptive diagnosis and will direct patients to the correspondent department. A questionnaire will dynamically ask questions about the interface, and functionality improvements. Based on the answers, the functionality of the system and the user interface will be improved considering the real needs expressed by the end-users. The cloud-based CDSS, as a useful tool for patients, physicians and healthcare providers involves the computer support in the diagnosis of different pathologies and an accurate automatic differential diagnostic system.

Hybrid approach for robust diagnostics of cutting tools

NASA Astrophysics Data System (ADS)

Ramamurthi, K.; Hough, C. L., Jr.

1994-03-01

A new multisensor based hybrid technique has been developed for robust diagnosis of cutting tools. The technique combines the concepts of pattern classification and real-time knowledge based systems (RTKBS) and draws upon their strengths; learning facility in the case of pattern classification and a higher level of reasoning in the case of RTKBS. It eliminates some of their major drawbacks: false alarms or delayed/lack of diagnosis in case of pattern classification and tedious knowledge base generation in case of RTKBS. It utilizes a dynamic distance classifier, developed upon a new separability criterion and a new definition of robust diagnosis for achieving these benefits. The promise of this technique has been proven concretely through an on-line diagnosis of drill wear. Its suitability for practical implementation is substantiated by the use of practical, inexpensive, machine-mounted sensors and low-cost delivery systems.

Graph-based real-time fault diagnostics

NASA Technical Reports Server (NTRS)

Padalkar, S.; Karsai, G.; Sztipanovits, J.

1988-01-01

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

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.

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

Chang, Hsien-Yen; Weiner, Jonathan P

2010-01-18

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. 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. 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. Given the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.

Identifying Model-Based Reconfiguration Goals through Functional Deficiencies

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Trave-Massuyes, Louise

2004-01-01

Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.

Forward chaining method on diagnosis of diseases and pests corn crop

NASA Astrophysics Data System (ADS)

Nurlaeli, Subiyanto

2017-03-01

Integrated pest management should be done to control the explosion of plants pest and diseases due to climate change is uncertain. This paper is a present implementation of the forward chaining method in the diagnosis diseases and pests of corn crop to help farmers/agricultural facilitators in getting knowledge about disease and pest corn crop. Forward chaining method as inference engine is used to get a disease/pest that attacks the corn crop based on symptoms. The forward chaining method works based on the fact that there is to get a conclusion. Fact in this system derived from the symptoms of the selected user is matched with the premise on every rule in the knowledge base. A rule that matches the facts to be executed to be the conclusion in the form of diagnosis. This validation using 36 data test, 32 data showed the same diagnostic results between systems with an expert. So, the percentage accuracy of results of diagnosis using data test of 88%. Finally, it can be concluded that the diagnosis system of diseases and pests corn crop can be used to help farmers/agricultural facilitators to diagnose diseases and pests corn crop.

An automated diagnosis system of liver disease using artificial immune and genetic algorithms.

PubMed

Liang, Chunlin; Peng, Lingxi

2013-04-01

The rise of health care cost is one of the world's most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system's characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.

[Tumor Data Interacted System Design Based on Grid Platform].

PubMed

Liu, Ying; Cao, Jiaji; Zhang, Haowei; Zhang, Ke

2016-06-01

In order to satisfy demands of massive and heterogeneous tumor clinical data processing and the multi-center collaborative diagnosis and treatment for tumor diseases,a Tumor Data Interacted System(TDIS)was established based on grid platform,so that an implementing virtualization platform of tumor diagnosis service was realized,sharing tumor information in real time and carrying on standardized management.The system adopts Globus Toolkit 4.0tools to build the open grid service framework and encapsulats data resources based on Web Services Resource Framework(WSRF).The system uses the middleware technology to provide unified access interface for heterogeneous data interaction,which could optimize interactive process with virtualized service to query and call tumor information resources flexibly.For massive amounts of heterogeneous tumor data,the federated stored and multiple authorized mode is selected as security services mechanism,real-time monitoring and balancing load.The system can cooperatively manage multi-center heterogeneous tumor data to realize the tumor patient data query,sharing and analysis,and compare and match resources in typical clinical database or clinical information database in other service node,thus it can assist doctors in consulting similar case and making up multidisciplinary treatment plan for tumors.Consequently,the system can improve efficiency of diagnosis and treatment for tumor,and promote the development of collaborative tumor diagnosis model.

Image-based computer-assisted diagnosis system for benign paroxysmal positional vertigo

NASA Astrophysics Data System (ADS)

Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu

2005-04-01

We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.

Diagnosis and Management of Systemic Sclerosis: A Practical Approach.

PubMed

Lee, Jason J; Pope, Janet E

2016-02-01

Systemic sclerosis is a devastating multisystem rheumatologic condition that is characterized by autoimmunity, tissue fibrosis, obliterative vasculopathy and inflammation. Clinical presentation and course of the condition vary greatly, which complicates both diagnosis and corresponding treatment. In this regard, recent advances in disease understanding, both clinically and biochemically, have led to newer classification criteria for systemic sclerosis that are more inclusive than ever before. Still, significant disease modifying therapies do not yet exist for most patients. Therefore, organ-based management strategies are employed and research has been directed within this paradigm focusing on either the most debilitating symptoms, such as Raynaud's phenomenon, digital ulcers and cutaneous sclerosis, or life-threatening organ involvement such as interstitial lung disease and pulmonary arterial hypertension. The current trends in systemic sclerosis diagnosis, evidence-based treatment recommendations and potential future directions in systemic sclerosis treatment are discussed.

Systems biology-based diagnostic principles as pillars of the bridge between Chinese and Western medicine.

PubMed

van der Greef, Jan; van Wietmarschen, Herman; Schroën, Jan; Wang, Mei; Hankemeier, Thomas; Xu, Guowang

2010-12-01

Innovative systems approaches to develop medicine and health care are emerging from the integration of Chinese and Western medicine strategies, philosophies and practices. The two medical systems are highly complementary as the reductionist aspects of Western medicine are favourable in acute disease situations and the holistic aspects of Chinese medicine offer more opportunities in chronic conditions and for prevention. In this article we argue that diagnosis plays a key role in building the bridge between Chinese and Western medicine. Recent advances in the study of health, healing, placebo effects and patient-physician interactions will be discussed pointing out the development of a system-based diagnosis. Especially, a system biology-based diagnosis can be used to capture phenotype information, leading towards a scientific basis for a more refined patient characterization, new diagnostic tools and personalized heath strategies. Subtyping of rheumatoid arthritis patients based on Chinese diagnostic principles is discussed as an example. New insights from this process of integrating Western and Chinese medicine will pave the way for a patient-centred health care ecosystem. © Georg Thieme Verlag KG Stuttgart · New York.

Research into a distributed fault diagnosis system and its application

NASA Astrophysics Data System (ADS)

Qian, Suxiang; Jiao, Weidong; Lou, Yongjian; Shen, Xiaomei

2005-12-01

CORBA (Common Object Request Broker Architecture) is a solution to distributed computing methods over heterogeneity systems, which establishes a communication protocol between distributed objects. It takes great emphasis on realizing the interoperation between distributed objects. However, only after developing some application approaches and some practical technology in monitoring and diagnosis, can the customers share the monitoring and diagnosis information, so that the purpose of realizing remote multi-expert cooperation diagnosis online can be achieved. This paper aims at building an open fault monitoring and diagnosis platform combining CORBA, Web and agent. Heterogeneity diagnosis object interoperate in independent thread through the CORBA (soft-bus), realizing sharing resource and multi-expert cooperation diagnosis online, solving the disadvantage such as lack of diagnosis knowledge, oneness of diagnosis technique and imperfectness of analysis function, so that more complicated and further diagnosis can be carried on. Take high-speed centrifugal air compressor set for example, we demonstrate a distributed diagnosis based on CORBA. It proves that we can find out more efficient approaches to settle the problems such as real-time monitoring and diagnosis on the net and the break-up of complicated tasks, inosculating CORBA, Web technique and agent frame model to carry on complemental research. In this system, Multi-diagnosis Intelligent Agent helps improve diagnosis efficiency. Besides, this system offers an open circumstances, which is easy for the diagnosis objects to upgrade and for new diagnosis server objects to join in.

Knowledge acquisition for medical diagnosis using collective intelligence.

PubMed

Hernández-Chan, G; Rodríguez-González, A; Alor-Hernández, G; Gómez-Berbís, J M; Mayer-Pujadas, M A; Posada-Gómez, R

2012-11-01

The wisdom of the crowds (WOC) is the process of taking into account the collective opinion of a group of individuals rather than a single expert to answer a question. Based on this assumption, the use of processes based on WOC techniques to collect new biomedical knowledge represents a challenging and cutting-edge trend on biomedical knowledge acquisition. The work presented in this paper shows a new schema to collect diagnosis information in Diagnosis Decision Support Systems (DDSS) based on collective intelligence and consensus methods.

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.

A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests.

PubMed

Jabez Christopher, J; Khanna Nehemiah, H; Kannan, A

2015-10-01

Allergic Rhinitis is a universal common disease, especially in populated cities and urban areas. Diagnosis and treatment of Allergic Rhinitis will improve the quality of life of allergic patients. Though skin tests remain the gold standard test for diagnosis of allergic disorders, clinical experts are required for accurate interpretation of test outcomes. This work presents a clinical decision support system (CDSS) to assist junior clinicians in the diagnosis of Allergic Rhinitis. Intradermal Skin tests were performed on patients who had plausible allergic symptoms. Based on patient׳s history, 40 clinically relevant allergens were tested. 872 patients who had allergic symptoms were considered for this study. The rule based classification approach and the clinical test results were used to develop and validate the CDSS. Clinical relevance of the CDSS was compared with the Score for Allergic Rhinitis (SFAR). Tests were conducted for junior clinicians to assess their diagnostic capability in the absence of an expert. The class based Association rule generation approach provides a concise set of rules that is further validated by clinical experts. The interpretations of the experts are considered as the gold standard. The CDSS diagnoses the presence or absence of rhinitis with an accuracy of 88.31%. The allergy specialist and the junior clinicians prefer the rule based approach for its comprehendible knowledge model. The Clinical Decision Support Systems with rule based classification approach assists junior doctors and clinicians in the diagnosis of Allergic Rhinitis to make reliable decisions based on the reports of intradermal skin tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

Computer-Guided Diagnosis of Learning Disabilities: A Prototype.

ERIC Educational Resources Information Center

Colbourn, Marlene Jones

A computer based diagnostic system to assist educators in the assessment of learning disabled children aged 8 to 10 years in the area of reading is described and evaluated. The system is intended to guide the diagnosis of reading problems through step by step analysis of available data and requests for additional data. The system provides a…

Automated Consultation for the Diagnosis of Interplanetary Telecommunications

NASA Technical Reports Server (NTRS)

Quan, A. G.; Schwuttke, U. M.; Herstein, J. S.; Spagnuolo, J. S.; Burleigh, S.

1995-01-01

SHARP (Spacecraft Health Automated Reasoning Program) is a knowledge-based system for the diagnosis of problems in NASA's Deep Space Network (DSN) telecommunications system. This system provides the means of communication between a spacecraft and operations personnel at Jet Propulsion Laboratory. SHARP analyzes problems that occur in both the on-board spacecraft telecom subsystem, and the DSN.

Comparative Analysis.

DTIC Science & Technology

1987-11-01

differential qualita- tive (DQ) analysis, which solves the task, providing explanations suitable for use by design systems, automated diagnosis, intelligent...solves the task, providing explanations suitable for use by design systems, automated diagnosis, intelligent tutoring systems, and explanation based...comparative analysis as an important component; the explanation is used in many different ways. * One way method of automated design is the principlvd

Imperceptible watermarking for security of fundus images in tele-ophthalmology applications and computer-aided diagnosis of retina diseases.

PubMed

Singh, Anushikha; Dutta, Malay Kishore

2017-12-01

The authentication and integrity verification of medical images is a critical and growing issue for patients in e-health services. Accurate identification of medical images and patient verification is an essential requirement to prevent error in medical diagnosis. The proposed work presents an imperceptible watermarking system to address the security issue of medical fundus images for tele-ophthalmology applications and computer aided automated diagnosis of retinal diseases. In the proposed work, patient identity is embedded in fundus image in singular value decomposition domain with adaptive quantization parameter to maintain perceptual transparency for variety of fundus images like healthy fundus or disease affected image. In the proposed method insertion of watermark in fundus image does not affect the automatic image processing diagnosis of retinal objects & pathologies which ensure uncompromised computer-based diagnosis associated with fundus image. Patient ID is correctly recovered from watermarked fundus image for integrity verification of fundus image at the diagnosis centre. The proposed watermarking system is tested in a comprehensive database of fundus images and results are convincing. results indicate that proposed watermarking method is imperceptible and it does not affect computer vision based automated diagnosis of retinal diseases. Correct recovery of patient ID from watermarked fundus image makes the proposed watermarking system applicable for authentication of fundus images for computer aided diagnosis and Tele-ophthalmology applications. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Design of a modified adaptive neuro fuzzy inference system classifier for medical diagnosis of Pima Indians Diabetes

NASA Astrophysics Data System (ADS)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. 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.

Enormous knowledge base of disease diagnosis criteria.

PubMed

Xiao, Z H; Xiao, Y H; Pei, J H

1995-01-01

One of the problems in the development of the medical knowledge systems is the limitations of the system's knowledge. It is a common expectation to increase the number of diseases contained in a system. Using a high density knowledge representation method designed by us, we have developed the Enormous Knowledge Base of Disease Diagnosis Criteria (EKBDDC). It contains diagnostic criteria of 1,001 diagnostic entities and describes nearly 4,000 items of diagnostic indicators. It is the core of a huge medical project--the Electronic-Brain Medical Erudite (EBME). This enormous knowledge base was implemented initially on a low-cost popular microcomputer, which can aid in the prompting of typical disease and in teaching of diagnosis. The knowledge base is easy to expand. One of the main goals of EKBDDC is to increase the number of diseases included in it as far as possible using a low-cost computer with a comparatively small storage capacity. For this, we have designed a high density knowledge representation method. Criteria of various diagnostic entities are respectively stored in different records of the knowledge base. Each diagnostic entity corresponds to a diagnostic criterion data set; each data set consists of some diagnostic criterion data values (Table 1); each data is composed of two parts: integer and decimal; the integral part is the coding number of the given diagnostic information, and the decimal part is the diagnostic value of this information to the disease indicated by corresponding record number. For example, 75.02: the integer 75 is the coding number of "hemorrhagic skin rash"; the decimal 0.02 is the diagnostic value of this manifestation for diagnosing allergic purpura. TABULAR DATA, SEE PUBLISHED ABSTRACT. The algebraic sum method, a special form of the weighted summation, is adopted as mathematical model. In EKBDDC, the diagnostic values, which represent the significance of the disease manifestations for diagnosing corresponding diseases, were determined empirically. It is of a great economical, practical, and technical significance to realize enormous knowledge bases of disease diagnosis criteria on a low-cost popular microcomputer. This is beneficial for the developing countries to popularize medical informatics. To create the enormous international computer-aided diagnosis system, one may jointly develop the unified modules of disease diagnosis criteria used to "inlay" relevant computer-aided diagnosis systems. It is just like assembling a house using prefabricated panels.

Implementing a real time reasoning system for robust diagnosis

NASA Technical Reports Server (NTRS)

Hill, Tim; Morris, William; Robertson, Charlie

1993-01-01

The objective of the Thermal Control System Automation Project (TCSAP) is to develop an advanced fault detection, isolation, and recovery (FDIR) capability for use on the Space Station Freedom (SSF) External Active Thermal Control System (EATCS). Real-time monitoring, control, and diagnosis of the EATCS will be performed with a knowledge based system (KBS). Implementation issues for the current version of the KBS are discussed.

Failure Diagnosis for the Holdup Tank System via ISFA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Huijuan; Bragg-Sitton, Shannon; Smidts, Carol

This paper discusses the use of the integrated system failure analysis (ISFA) technique for fault diagnosis for the holdup tank system. ISFA is a simulation-based, qualitative and integrated approach used to study fault propagation in systems containing both hardware and software subsystems. The holdup tank system consists of a tank containing a fluid whose level is controlled by an inlet valve and an outlet valve. We introduce the component and functional models of the system, quantify the main parameters and simulate possible failure-propagation paths based on the fault propagation approach, ISFA. The results show that most component failures in themore » holdup tank system can be identified clearly and that ISFA is viable as a technique for fault diagnosis. Since ISFA is a qualitative technique that can be used in the very early stages of system design, this case study provides indications that it can be used early to study design aspects that relate to robustness and fault tolerance.« less

[A computer-aided image diagnosis and study system].

PubMed

Li, Zhangyong; Xie, Zhengxiang

2004-08-01

The revolution in information processing, particularly the digitizing of medicine, has changed the medical study, work and management. This paper reports a method to design a system for computer-aided image diagnosis and study. Combined with some good idea of graph-text system and picture archives communicate system (PACS), the system was realized and used for "prescription through computer", "managing images" and "reading images under computer and helping the diagnosis". Also typical examples were constructed in a database and used to teach the beginners. The system was developed by the visual developing tools based on object oriented programming (OOP) and was carried into operation on the Windows 9X platform. The system possesses friendly man-machine interface.

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.

Agent-based Decision Support System for the Third Generation Distributed Dynamic Decision-making (DDD-III) Simulator

DTIC Science & Technology

2004-06-01

suitable form of organizational adaptation is effective organizational diagnosis and analysis. The organizational diagnosis and analysis involve...related to the mission environment, organizational structure, and strategy is imperative for an effective and efficient organizational diagnosis . The...not easily articulated nor expressed otherwise. These displays are crucial to facilitate effective organizational diagnosis and analysis, and

A case-oriented web-based training system for breast cancer diagnosis.

PubMed

Huang, Qinghua; Huang, Xianhai; Liu, Longzhong; Lin, Yidi; Long, Xingzhang; Li, Xuelong

2018-03-01

Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor. We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists. This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value < .05); meanwhile the senior radiologists show little improvement (p-value > .05). The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis

PubMed Central

Campbell, J. Peter; Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir N.; Reynolds, James D.; Horowitz, Jason; Hutcheson, Kelly; Shapiro, Michael; Repka, Michael X.; Ferrone, Phillip; Drenser, Kimberly; Martinez-Castellanos, Maria Ana; Ostmo, Susan; Jonas, Karyn; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Importance Published definitions of “plus disease” in retinopathy of prematurity (ROP) reference arterial tortuosity and venous dilation within the posterior pole based on a standard published photograph. One possible explanation for limited inter-expert reliability for plus disease diagnosis is that experts deviate from the published definitions. Objective To identify vascular features used by experts for diagnosis of plus disease through quantitative image analysis. Design We developed a computer-based image analysis system (Imaging and Informatics in ROP, i-ROP), and trained the system to classify images compared to a reference standard diagnosis (RSD). System performance was analyzed as a function of the field of view (circular crops 1–6 disc diameters [DD] radius) and vessel subtype (arteries only, veins only, or all vessels). The RSD was compared to the majority diagnosis of experts. Setting Routine ROP screening in neonatal intensive care units at 8 academic institutions. Participants A set of 77 digital fundus images was used to develop the i-ROP system. A subset of 73 images was independently classified by 11 ROP experts for validation. Main Outcome Measures The primary outcome measure was the percentage accuracy of i-ROP system classification of plus disease with the RSD as a function of field-of-view and vessel type. Secondary outcome measures included the accuracy of the 11 experts compared to the RSD. Results Accuracy of plus disease diagnosis by the i-ROP computer based system was highest (95%, confidence interval [CI] 94 – 95%) when it incorporated vascular tortuosity from both arteries and veins, and with the widest field of view (6 disc diameter radius). Accuracy was ≤90% when using only arterial tortuosity (P<0.001), and ≤85% using a 2–3 disc diameter view similar to the standard published photograph (p<0.001). Diagnostic accuracy of the i-ROP system (95%) was comparable to that of 11 expert clinicians (79–99%). Conclusions and Relevance ROP experts appear to consider findings from beyond the posterior retina when diagnosing plus disease, and consider tortuosity of both arteries and veins, in contrast to published definitions. It is feasible for a computer-based image analysis system to perform comparably to ROP experts, using manually segmented images. PMID:27077667

On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification

NASA Astrophysics Data System (ADS)

Gao, Liang; Li, Fuhai; Thrall, Michael J.; Yang, Yaliang; Xing, Jiong; Hammoudi, Ahmad A.; Zhao, Hong; Massoud, Yehia; Cagle, Philip T.; Fan, Yubo; Wong, Kelvin K.; Wang, Zhiyong; Wong, Stephen T. C.

2011-09-01

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

Ontology based decision system for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

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

PubMed

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

2016-01-01

Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Direct costs of emergency medical care: a diagnosis-based case-mix classification system.

PubMed

Baraff, L J; Cameron, J M; Sekhon, R

1991-01-01

To develop a diagnosis-based case mix classification system for emergency department patient visits based on direct costs of care designed for an outpatient setting. Prospective provider time study with collection of financial data from each hospital's accounts receivable system and medical information, including discharge diagnosis, from hospital medical records. Three community hospital EDs in Los Angeles County during selected times in 1984. Only direct costs of care were included: health care provider time, ED management and clerical personnel excluding registration, nonlabor ED expense including supplies, and ancillary hospital services. Indirect costs for hospitals and physicians, including depreciation and amortization, debt service, utilities, malpractice insurance, administration, billing, registration, and medical records were not included. Costs were derived by valuing provider time based on a formula using annual income or salary and fringe benefits, productivity and direct care factors, and using hospital direct cost to charge ratios. Physician costs were based on a national study of emergency physician income and excluded practice costs. Patients were classified into one of 216 emergency department groups (EDGs) on the basis of the discharge diagnosis, patient disposition, age, and the presence of a limited number of physician procedures. Total mean direct costs ranged from $23 for follow-up visit to $936 for trauma, admitted, with critical care procedure. The mean total direct costs for the 16,771 nonadmitted patients was $69. Of this, 34% was for ED costs, 45% was for ancillary service costs, and 21% was for physician costs. The mean total direct costs for the 1,955 admitted patients was $259. Of this, 23% was for ED costs, 63% was for ancillary service costs, and 14% was for physician costs. Laboratory and radiographic services accounted for approximately 85% of all ancillary service costs and 38% of total direct costs for nonadmitted patients versus 80% of ancillary service costs and 51% of total direct costs for admitted patients. We have developed a diagnosis-based case mix classification system for ED patient visits based on direct costs of care designed for an outpatient setting which, unlike diagnosis-related groups, includes the measurement of time-based cost for physician and nonphysician services. This classification system helps to define direct costs of hospital and physician emergency services by type of patient.

A Man-Machine System for Contemporary Counseling Practice: Diagnosis and Prediction.

ERIC Educational Resources Information Center

Roach, Arthur J.

This paper looks at present and future capabilities for diagnosis and prediction in computer-based guidance efforts and reviews the problems and potentials which will accompany the implementation of such capabilities. In addition to necessary procedural refinement in prediction, future developments in computer-based educational and career…

The Influence of Chinese Character Handwriting Diagnosis and Remedial Instruction System on Learners of Chinese as a Foreign Language

ERIC Educational Resources Information Center

Hsiao, Hsien-Sheng; Chang, Cheng-Sian; Chen, Chiao-Jia; Wu, Chia-Hou; Lin, Chien-Yu

2015-01-01

This study designed and developed a Chinese character handwriting diagnosis and remedial instruction (CHDRI) system to improve Chinese as a foreign language (CFL) learners' ability to write Chinese characters. The CFL learners were given two tests based on the CHDRI system. One test focused on Chinese character handwriting to diagnose the CFL…

SSME fault monitoring and diagnosis expert system

NASA Technical Reports Server (NTRS)

Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

1989-01-01

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

Signal Analysis of Automotive Engine Spark Ignition System using Case-Based Reasoning (CBR) and Case-based Maintenance (CBM)

NASA Astrophysics Data System (ADS)

Huang, H.; Vong, C. M.; Wong, P. K.

2010-05-01

With the development of modern technology, modern vehicles adopt electronic control system for injection and ignition. In traditional way, whenever there is any malfunctioning in an automotive engine, an automotive mechanic usually performs a diagnosis in the ignition system of the engine to check any exceptional symptoms. In this paper, we present a case-based reasoning (CBR) approach to help solve human diagnosis problem. Nevertheless, one drawback of CBR system is that the case library will be expanded gradually after repeatedly running the system, which may cause inaccuracy and longer time for the CBR retrieval. To tackle this problem, case-based maintenance (CBM) framework is employed so that the case library of the CBR system will be compressed by clustering to produce a set of representative cases. As a result, the performance (in retrieval accuracy and time) of the whole CBR system can be improved.

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

NASA Astrophysics Data System (ADS)

Habbi, Hacene; Kidouche, Madjid; Kinnaert, Michel; Zelmat, Mimoun

2011-04-01

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.

A computer-aided detection (CAD) system with a 3D algorithm for small acute intracranial hemorrhage

NASA Astrophysics Data System (ADS)

Wang, Ximing; Fernandez, James; Deshpande, Ruchi; Lee, Joon K.; Chan, Tao; Liu, Brent

2012-02-01

Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae. However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed. The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.

Development of the tongue coating analyzer based on concave grating monochrometer and virtual instrument

NASA Astrophysics Data System (ADS)

Ren, Zhong; Liu, Guodong; Zeng, Lvming; Huang, Zhen; Zeng, Wenping

2010-10-01

The tongue coating diagnosis is an important part in tongue diagnosis of traditional Chinese medicine (TCM).The change of the thickness and color of the tongue coating can reflect the pathological state for the patient. By observing the tongue coating, a Chinese doctor can determine the nature or severity of disease. Because some limitations existed in the tongue diagnosis method of TCM and the method based on the digital image processing, a novel tongue coating analyzer(TCA) based on the concave grating monochrometer and virtual instrument is developed in this paper. This analyzer consists of the light source system, check cavity, optical fiber probe, concave grating monochrometer, spectrum detector system based on CCD and data acquisition (DAQ) card, signal processing circuit system, computer and data analysis software based on LabVIEW, etc. Experimental results show that the novel TCA's spectral range can reach 300-1000 nm, its wavelength resolution can reach 1nm, and this TCA uses the back-split-light technology and multi-channel parallel analysis. Compared with the TCA based on the image processing technology, this TCA has many advantages, such as, compact volume, simpler algorithm, faster processing speed, higher accuracy, cheaper cost and real-time handle data and display the result, etc. Therefore, it has the greatly potential values in the fields of the tongue coating diagnosis for TCM.

Fault Diagnosis for Micro-Gas Turbine Engine Sensors via Wavelet Entropy

PubMed Central

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can’t be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient. PMID:22163734

Fault diagnosis for micro-gas turbine engine sensors via wavelet entropy.

PubMed

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can't be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient.

Field programmable gate array based fuzzy neural signal processing system for differential diagnosis of QRS complex tachycardia and tachyarrhythmia in noisy ECG signals.

PubMed

Chowdhury, Shubhajit Roy

2012-04-01

The paper reports of a Field Programmable Gate Array (FPGA) based embedded system for detection of QRS complex in a noisy electrocardiogram (ECG) signal and thereafter differential diagnosis of tachycardia and tachyarrhythmia. The QRS complex has been detected after application of entropy measure of fuzziness to build a detection function of ECG signal, which has been previously filtered to remove power line interference and base line wander. Using the detected QRS complexes, differential diagnosis of tachycardia and tachyarrhythmia has been performed. The entire algorithm has been realized in hardware on an FPGA. Using the standard CSE ECG database, the algorithm performed highly effectively. The performance of the algorithm in respect of QRS detection with sensitivity (Se) of 99.74% and accuracy of 99.5% is achieved when tested using single channel ECG with entropy criteria. The performance of the QRS detection system has been compared and found to be better than most of the QRS detection systems available in literature. Using the system, 200 patients have been diagnosed with an accuracy of 98.5%.

Realization of a CORDIC-Based Plug-In Accelerometer Module for PSG System in Head Position Monitoring for OSAS Patients

PubMed Central

Chou, Wen-Cheng; Shiao, Tsu-Hui; Shiao, Guang-Ming; Luo, Chin-Shan

2017-01-01

Overnight polysomnography (PSG) is currently the standard diagnostic procedure for obstructive sleep apnea (OSA). It has been known that monitoring of head position in sleep is crucial not only for the diagnosis (positional sleep apnea) but also for the management of OSA (positional therapy). However, there are no sensor systems available clinically to hook up with PSG for accurate head position monitoring. In this paper, an accelerometer-based sensing system for accurate head position monitoring is developed and realized. The core CORDIC- (COordinate Rotation DIgital Computer-) based tilting sensing algorithm is realized in the system to quickly and accurately convert accelerometer raw data into the desired head position tilting angles. The system can hook up with PSG devices for diagnosis to have head position information integrated with other PSG-monitored signals. It has been applied in an IRB test in Taipei Veterans General Hospital and has been proved that it can meet the medical needs of accurate head position monitoring for PSG diagnosis. PMID:29065608

Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis.

PubMed

Campbell, J Peter; Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir N; Reynolds, James D; Horowitz, Jason; Hutcheson, Kelly; Shapiro, Michael; Repka, Michael X; Ferrone, Phillip; Drenser, Kimberly; Martinez-Castellanos, Maria Ana; Ostmo, Susan; Jonas, Karyn; Chan, R V Paul; Chiang, Michael F

2016-06-01

Published definitions of plus disease in retinopathy of prematurity (ROP) reference arterial tortuosity and venous dilation within the posterior pole based on a standard published photograph. One possible explanation for limited interexpert reliability for a diagnosis of plus disease is that experts deviate from the published definitions. To identify vascular features used by experts for diagnosis of plus disease through quantitative image analysis. A computer-based image analysis system (Imaging and Informatics in ROP [i-ROP]) was developed using a set of 77 digital fundus images, and the system was designed to classify images compared with a reference standard diagnosis (RSD). System performance was analyzed as a function of the field of view (circular crops with a radius of 1-6 disc diameters) and vessel subtype (arteries only, veins only, or all vessels). Routine ROP screening was conducted from June 29, 2011, to October 14, 2014, in neonatal intensive care units at 8 academic institutions, with a subset of 73 images independently classified by 11 ROP experts for validation. The RSD was compared with the majority diagnosis of experts. The primary outcome measure was the percentage of accuracy of the i-ROP system classification of plus disease, with the RSD as a function of the field of view and vessel type. Secondary outcome measures included the accuracy of the 11 experts compared with the RSD. Accuracy of plus disease diagnosis by the i-ROP computer-based system was highest (95%; 95% CI, 94%-95%) when it incorporated vascular tortuosity from both arteries and veins and with the widest field of view (6-disc diameter radius). Accuracy was 90% or less when using only arterial tortuosity and 85% or less using a 2- to 3-disc diameter view similar to the standard published photograph. Diagnostic accuracy of the i-ROP system (95%) was comparable to that of 11 expert physicians (mean 87%, range 79%-99%). Experts in ROP appear to consider findings from beyond the posterior retina when diagnosing plus disease and consider tortuosity of both arteries and veins, in contrast with published definitions. It is feasible for a computer-based image analysis system to perform comparably with ROP experts, using manually segmented images.

The Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining

NASA Astrophysics Data System (ADS)

Sun, Baiqing; Li, Yange; Zhang, Lin

This thesis gives the general definition of the concepts of extension knowledge, extension data mining and extension data mining theorem in high dimension space, and also builds the IDSS integrated system by the rough set, expert system and neural network, develops the relevant computer software. From the diagnosis tests, according to the common diseases of myocardial infarctions, angina pectoris and hypertension, and made the test result with physicians, the results shows that the sensitivity, specific and accuracy diagnosis by the IDSS are all higher than the physicians. It can improve the rate of the accuracy diagnosis of physician with the auxiliary help of this system, which have the obvious meaning in low the mortality, disability rate and high the survival rate, and has strong practical values and further social benefits.

Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

PubMed

Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

2012-01-01

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

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.

Engine Data Interpretation System (EDIS)

NASA Technical Reports Server (NTRS)

Cost, Thomas L.; Hofmann, Martin O.

1990-01-01

A prototype of an expert system was developed which applies qualitative or model-based reasoning to the task of post-test analysis and diagnosis of data resulting from a rocket engine firing. A combined component-based and process theory approach is adopted as the basis for system modeling. Such an approach provides a framework for explaining both normal and deviant system behavior in terms of individual component functionality. The diagnosis function is applied to digitized sensor time-histories generated during engine firings. The generic system is applicable to any liquid rocket engine but was adapted specifically in this work to the Space Shuttle Main Engine (SSME). The system is applied to idealized data resulting from turbomachinery malfunction in the SSME.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

PubMed

Segovia, F; Górriz, J M; Ramírez, J; Phillips, C

2016-01-01

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

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.

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

NASA Astrophysics Data System (ADS)

Bos, Andre; Callies, Jorg; Lefebvre, Alain

1995-05-01

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.

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.

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Adaptive neural network/expert system that learns fault diagnosis for different structures

NASA Astrophysics Data System (ADS)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

A Model-Based Diagnosis Framework for Distributed Systems

DTIC Science & Technology

2002-05-04

of centralized compilation techniques as applied to [6] Marco Cadoli and Francesco M . Donini . A survey several areas, of which diagnosis is one. Our...for doing so than the family for that (1) Vi 1 ... m . Xi E 2V; (2) V ui(Xi[Xi E 1). tree-structured systems. For simplicity of notation, we will that (i...our diagnosis synthesis diagnoses using a likelihood weight ri assigned to each as- algorithm. sumable Ai, i = I, ... m . Using the likelihood algebra

Assessment of a Tele-education System to Enhance Retinopathy of Prematurity Training by International Ophthalmologists-in-Training in Mexico.

PubMed

Patel, Samir N; Martinez-Castellanos, Maria Ana; Berrones-Medina, David; Swan, Ryan; Ryan, Michael C; Jonas, Karyn E; Ostmo, Susan; Campbell, J Peter; Chiang, Michael F; Chan, R V Paul

2017-07-01

To evaluate a tele-education system developed to improve diagnostic competency in retinopathy of prematurity (ROP) by ophthalmologists-in-training in Mexico. Prospective, randomized cohort study. Fifty-eight ophthalmology residents and fellows from a training program in Mexico consented to participate. Twenty-nine of 58 trainees (50%) were randomized to the educational intervention (pretest, ROP tutorial, ROP educational chapters, and posttest), and 29 of 58 trainees (50%) were randomized to a control group (pretest and posttest only). A secure web-based educational system was created using clinical cases (20 pretest, 20 posttest, and 25 training chapter-based) developed from a repository of over 2500 unique image sets of ROP. For each image set used, a reference standard ROP diagnosis was established by combining the clinical diagnosis by indirect ophthalmoscope examination and image-based diagnosis by multiple experts. Trainees were presented with image-based clinical cases of ROP during a pretest, posttest, and training chapters. The accuracy of ROP diagnosis (e.g., plus disease, zone, stage, category) was determined using sensitivity and specificity calculations from the pretest and posttest results of the educational intervention group versus control group. The unweighted kappa statistic was used to analyze the intragrader agreement for ROP diagnosis by the ophthalmologists-in-training during the pretest and posttest for both groups. Trainees completing the tele-education system had statistically significant improvements (P < 0.01) in the accuracy of ROP diagnosis for plus disease, zone, stage, category, and aggressive posterior ROP (AP-ROP). Compared with the control group, trainees who completed the ROP tele-education system performed better on the posttest for accurately diagnosing plus disease (67% vs. 48%; P = 0.04) and the presence of ROP (96% vs. 91%; P < 0.01). The specificity for diagnosing AP-ROP (94% vs. 78%; P < 0.01), type 2 ROP or worse (92% vs. 84%; P = 0.04), and ROP requiring treatment (89% vs. 79%; P < 0.01) was better for the trainees completing the tele-education system compared with the control group. Intragrader agreement improved for identification of plus disease, zone, stage, and category of ROP after completion of the educational intervention. A tele-education system for ROP education was effective in improving the diagnostic accuracy of ROP by ophthalmologists-in-training in Mexico. This system has the potential to increase competency in ROP diagnosis and management for ophthalmologists-in-training from middle-income nations. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

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.

Development of Cad System for Diffuse Disease Based on Ultrasound Elasticity Images

NASA Astrophysics Data System (ADS)

Yamazaki, M.; Shiina, T.; Yamakawa, M.; Takizawa, H.; Tonomura, A.; Mitake, T.

It is well known that as hepatic cirrhosis progresses, hepatocyte fibrosis spreads and nodule increases. However, it is not easy to diagnosis its early stage by conventional B-mode image because we have to read subtle change of speckle pattern which is not sensitive to the stage of fibrosis. Ultrasonic tissue elasticity imaging can provide us novel diagnostic information based on tissue hardness. We recently developed commercial-based equipment for tissue elasticity imaging. In this work, we investigated to develop the CAD system based on elasticity image for diagnosing defused type diseases such as hepatic cirrhosis. The results of clinical data analysis indicate that the CAD system is promising as means for diagnosis of diffuse disease with simple criterion.

A web-based knowledge management system integrating Western and Traditional Chinese Medicine for relational medical diagnosis.

PubMed

Herrera-Hernandez, Maria C; Lai-Yuen, Susana K; Piegl, Les A; Zhang, Xiao

2016-10-26

This article presents the design of a web-based knowledge management system as a training and research tool for the exploration of key relationships between Western and Traditional Chinese Medicine, in order to facilitate relational medical diagnosis integrating these mainstream healing modalities. The main goal of this system is to facilitate decision-making processes, while developing skills and creating new medical knowledge. Traditional Chinese Medicine can be considered as an ancient relational knowledge-based approach, focusing on balancing interrelated human functions to reach a healthy state. Western Medicine focuses on specialties and body systems and has achieved advanced methods to evaluate the impact of a health disorder on the body functions. Identifying key relationships between Traditional Chinese and Western Medicine opens new approaches for health care practices and can increase the understanding of human medical conditions. Our knowledge management system was designed from initial datasets of symptoms, known diagnosis and treatments, collected from both medicines. The datasets were subjected to process-oriented analysis, hierarchical knowledge representation and relational database interconnection. Web technology was implemented to develop a user-friendly interface, for easy navigation, training and research. Our system was prototyped with a case study on chronic prostatitis. This trial presented the system's capability for users to learn the correlation approach, connecting knowledge in Western and Traditional Chinese Medicine by querying the database, mapping validated medical information, accessing complementary information from official sites, and creating new knowledge as part of the learning process. By addressing the challenging tasks of data acquisition and modeling, organization, storage and transfer, the proposed web-based knowledge management system is presented as a tool for users in medical training and research to explore, learn and update relational information for the practice of integrated medical diagnosis. This proposal in education has the potential to enable further creation of medical knowledge from both Traditional Chinese and Western Medicine for improved care providing. The presented system positively improves the information visualization, learning process and knowledge sharing, for training and development of new skills for diagnosis and treatment, and a better understanding of medical diseases. © IMechE 2016.

Remote diagnosis server

NASA Technical Reports Server (NTRS)

Deb, Somnath (Inventor); Ghoshal, Sudipto (Inventor); Malepati, Venkata N. (Inventor); Kleinman, David L. (Inventor); Cavanaugh, Kevin F. (Inventor)

2004-01-01

A network-based diagnosis server for monitoring and diagnosing a system, the server being remote from the system it is observing, comprises a sensor for generating signals indicative of a characteristic of a component of the system, a network-interfaced sensor agent coupled to the sensor for receiving signals therefrom, a broker module coupled to the network for sending signals to and receiving signals from the sensor agent, a handler application connected to the broker module for transmitting signals to and receiving signals therefrom, a reasoner application in communication with the handler application for processing, and responding to signals received from the handler application, wherein the sensor agent, broker module, handler application, and reasoner applications operate simultaneously relative to each other, such that the present invention diagnosis server performs continuous monitoring and diagnosing of said components of the system in real time. The diagnosis server is readily adaptable to various different systems.

Bayes' theorem application in the measure information diagnostic value assessment

NASA Astrophysics Data System (ADS)

Orzechowski, Piotr D.; Makal, Jaroslaw; Nazarkiewicz, Andrzej

2006-03-01

The paper presents Bayesian method application in the measure information diagnostic value assessment that is used in the computer-aided diagnosis system. The computer system described here has been created basing on the Bayesian Network and is used in Benign Prostatic Hyperplasia (BPH) diagnosis. The graphic diagnostic model enables to juxtapose experts' knowledge with data.

A Logical Account of Diagnosis with Multiple Theories

NASA Technical Reports Server (NTRS)

Pandurang, P.; Lum, Henry Jr. (Technical Monitor)

1994-01-01

Model-based diagnosis is a powerful, first-principles approach to diagnosis. The primary drawback with model-based diagnosis is that it is based on a system model, and this model might be inappropriate. The inappropriateness of models usually stems from the fundamental tradeoff between completeness and efficiency. Recently, Struss has developed an elegant proposal for diagnosis with multiple models. Struss characterizes models as relations and develops a precise notion of abstraction. He defines relations between models and analyzes the effect of a model switch on the space of possible diagnoses. In this paper we extend Struss's proposal in three ways. First, our account of diagnosis with multiple models is based on representing models as more expressive first-order theories, rather than as relations. A key technical contribution is the use of a general notion of abstraction based on interpretations between theories. Second, Struss conflates component modes with models, requiring him to define models relations such as choices which result in non-relational models. We avoid this problem by differentiating component modes from models. Third, we present a more general account of simplifications that correctly handles situations where the simplification contradicts the base theory.

Comparison of Fault Detection Algorithms for Real-time Diagnosis in Large-Scale System. Appendix E

NASA Technical Reports Server (NTRS)

Kirubarajan, Thiagalingam; Malepati, Venkat; Deb, Somnath; Ying, Jie

2001-01-01

In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of I's and O's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the faults. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time fault diagnosis using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time diagnosis tool), we test the performances of 1) TEAMSAT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) HidderMarkov Model based diagnosis.

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.

Automated beam monitoring and diagnosis for CO2 lasers

NASA Astrophysics Data System (ADS)

Mann, Stefan; Boeske, Lars; Kaierle, Stefan; Kreutz, Ernst-Wolfgang; Poprawe, Reinhart

2002-06-01

The usage of a quality management, in combination with a standard certification, is nearly inevitable for today's industrial manufacturing. In laser materials processing, a periodical beam diagnosis is to be executed as a quality-maintaining measure with any change of the workpiece geometry to guarantee an unambiguous allocation of the beam quality factors. Otherwise changes in the beam quality, caused by pollution, aging or defect of the optical components, remain unidentified for a long time, leading to impairments of the treatment quality or even costly down-times. As a solution a diagnosis system is integrated into a laser system. Data sources like measuring instruments, sensors and laser control transmit the diagnosis data to a diagnosis PC. A user-friendly software, based on Fuzzy algorithms, enables the operator to retrace changes in the beam quality to failures of the laser system. All diagnosis data are getting archived in a databank. The access to the archived data through the World Wide Web allows remote diagnoses. With the help of the beam diagnosis system failures can be discovered in advance, and losses of production can be avoided. The gained transparency of the beam characteristic values facilitates the integration of the laser system in the quality management. A prototype installation has been realized and latest results will be demonstrated.

Design of penicillin fermentation process simulation system

NASA Astrophysics Data System (ADS)

Qi, Xiaoyu; Yuan, Zhonghu; Qi, Xiaoxuan; Zhang, Wenqi

2011-10-01

Real-time monitoring for batch process attracts increasing attention. It can ensure safety and provide products with consistent quality. The design of simulation system of batch process fault diagnosis is of great significance. In this paper, penicillin fermentation, a typical non-linear, dynamic, multi-stage batch production process, is taken as the research object. A visual human-machine interactive simulation software system based on Windows operation system is developed. The simulation system can provide an effective platform for the research of batch process fault diagnosis.

Implementation and evaluation of a tele-education system for the diagnosis of ophthalmic disease by international trainees

PubMed Central

Campbell, J. Peter; Swan, Ryan; Jonas, Karyn; Ostmo, Susan; Ventura, Camila V.; Martinez-Castellanos, Maria A.; Anzures, Rachelle Go Ang Sam; Chiang, Michael F.; Chan, R.V. Paul

2015-01-01

Tele-education systems are increasingly being utilized in medical education worldwide. Due to limited human resources in healthcare in low and middle-income countries, developing online systems that are accessible to medical trainees in underserved areas potentially represents a highly efficient and effective method of improving the quantity and quality of the health care workforce. We developed, implemented, and evaluated an interactive web-based tele-education system (based on internationally accepted, image-based guidelines) for the diagnosis of retinopathy of prematurity among ophthalmologists-in-training in Brazil, Mexico, and the Philippines. We demonstrate that participation in this tele-education program improved diagnostic accuracy and reliability, and was preferred to standard pedagogical methods. This system may be employed not only in training, but also in international certification programs, and the process may be generalizable to other image-based specialties, such as dermatology and radiology. PMID:26958168

Implementation and evaluation of a tele-education system for the diagnosis of ophthalmic disease by international trainees.

PubMed

Campbell, J Peter; Swan, Ryan; Jonas, Karyn; Ostmo, Susan; Ventura, Camila V; Martinez-Castellanos, Maria A; Anzures, Rachelle Go Ang Sam; Chiang, Michael F; Chan, R V Paul

Tele-education systems are increasingly being utilized in medical education worldwide. Due to limited human resources in healthcare in low and middle-income countries, developing online systems that are accessible to medical trainees in underserved areas potentially represents a highly efficient and effective method of improving the quantity and quality of the health care workforce. We developed, implemented, and evaluated an interactive web-based tele-education system (based on internationally accepted, image-based guidelines) for the diagnosis of retinopathy of prematurity among ophthalmologists-in-training in Brazil, Mexico, and the Philippines. We demonstrate that participation in this tele-education program improved diagnostic accuracy and reliability, and was preferred to standard pedagogical methods. This system may be employed not only in training, but also in international certification programs, and the process may be generalizable to other image-based specialties, such as dermatology and radiology.

[The diagnostic methods applied in mycology].

PubMed

Kurnatowska, Alicja; Kurnatowski, Piotr

2008-01-01

The systemic fungal invasions are recognized with increasing frequency and constitute a primary cause of morbidity and mortality, especially in immunocompromised patients. Early diagnosis improves prognosis, but remains a problem because there is lack of sensitive tests to aid in the diagnosis of systemic mycoses on the one hand, and on the other the patients only present unspecific signs and symptoms, thus delaying early diagnosis. The diagnosis depends upon a combination of clinical observation and laboratory investigation. The successful laboratory diagnosis of fungal infection depends in major part on the collection of appropriate clinical specimens for investigations and on the selection of appropriate microbiological test procedures. So these problems (collection of specimens, direct techniques, staining methods, cultures on different media and non-culture-based methods) are presented in article.

[Chairman's introductory remarks].

PubMed

Shimo, Masamune

2013-01-01

Remote medicine, as well as the technological development of IT, has been realized in various fields of medical care. Reports have been published of the actual utilization of a system of remote diagnosis by infectious disease surveillance in each region of Nagano Prefecture, a telepathology system in Nagano, operation case of a radiological image remote diagnosis. Situation in the lack of pathologists and radiologists, that changes in the way of working of the physician, or medical information digitized convey how the doctor involved in the diagnosis, any diagnosis based on the information that was received doctor such as whether to perform, various issues have been raised. Soot digitization of information.

Development and realization of the open fault diagnosis system based on XPE

NASA Astrophysics Data System (ADS)

Deng, Hui; Wang, TaiYong; He, HuiLong; Xu, YongGang; Zeng, JuXiang

2005-12-01

To make the complex mechanical equipment work in good service, the technology for realizing an embedded open system is introduced systematically, including open hardware configuration, customized embedded operation system and open software structure. The ETX technology is adopted in this system, integrating the CPU main-board functions, and achieving the quick, real-time signal acquisition and intelligent data analysis with applying DSP and CPLD data acquisition card. Under the open configuration, the signal bus mode such as PCI, ISA and PC/104 can be selected and the styles of the signals can be chosen too. In addition, through customizing XPE system, adopting the EWF (Enhanced Write Filter), and realizing the open system authentically, the stability of the system is enhanced. Multi-thread and multi-task programming techniques are adopted in the software programming process. Interconnecting with the remote fault diagnosis center via the net interface, cooperative diagnosis is conducted and the intelligent degree of the fault diagnosis is improved.

On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis.

PubMed

Rodrigues, Jose F; Paulovich, Fernando V; de Oliveira, Maria Cf; de Oliveira, Osvaldo N

2016-04-01

An overview is provided of the challenges involved in building computer-aided diagnosis systems capable of precise medical diagnostics based on integration and interpretation of data from different sources and formats. The availability of massive amounts of data and computational methods associated with the Big Data paradigm has brought hope that such systems may soon be available in routine clinical practices, which is not the case today. We focus on visual and machine learning analysis of medical data acquired with varied nanotech-based techniques and on methods for Big Data infrastructure. Because diagnosis is essentially a classification task, we address the machine learning techniques with supervised and unsupervised classification, making a critical assessment of the progress already made in the medical field and the prospects for the near future. We also advocate that successful computer-aided diagnosis requires a merge of methods and concepts from nanotechnology and Big Data analysis.

An evaluation of a real-time fault diagnosis expert system for aircraft applications

NASA Technical Reports Server (NTRS)

Schutte, Paul C.; Abbott, Kathy H.; Palmer, Michael T.; Ricks, Wendell R.

1987-01-01

A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

Knowledge and intelligent computing system in medicine.

PubMed

Pandey, Babita; Mishra, R B

2009-03-01

Knowledge-based systems (KBS) and intelligent computing systems have been used in the medical planning, diagnosis and treatment. The KBS consists of rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) whereas intelligent computing method (ICM) encompasses genetic algorithm (GA), artificial neural network (ANN), fuzzy logic (FL) and others. The combination of methods in KBS such as CBR-RBR, CBR-MBR and RBR-CBR-MBR and the combination of methods in ICM is ANN-GA, fuzzy-ANN, fuzzy-GA and fuzzy-ANN-GA. The combination of methods from KBS to ICM is RBR-ANN, CBR-ANN, RBR-CBR-ANN, fuzzy-RBR, fuzzy-CBR and fuzzy-CBR-ANN. In this paper, we have made a study of different singular and combined methods (185 in number) applicable to medical domain from mid 1970s to 2008. The study is presented in tabular form, showing the methods and its salient features, processes and application areas in medical domain (diagnosis, treatment and planning). It is observed that most of the methods are used in medical diagnosis very few are used for planning and moderate number in treatment. The study and its presentation in this context would be helpful for novice researchers in the area of medical expert system.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

PubMed

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-06-01

This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

DNA methylation-based classification of central nervous system tumours.

PubMed

Capper, David; Jones, David T W; Sill, Martin; Hovestadt, Volker; Schrimpf, Daniel; Sturm, Dominik; Koelsche, Christian; Sahm, Felix; Chavez, Lukas; Reuss, David E; Kratz, Annekathrin; Wefers, Annika K; Huang, Kristin; Pajtler, Kristian W; Schweizer, Leonille; Stichel, Damian; Olar, Adriana; Engel, Nils W; Lindenberg, Kerstin; Harter, Patrick N; Braczynski, Anne K; Plate, Karl H; Dohmen, Hildegard; Garvalov, Boyan K; Coras, Roland; Hölsken, Annett; Hewer, Ekkehard; Bewerunge-Hudler, Melanie; Schick, Matthias; Fischer, Roger; Beschorner, Rudi; Schittenhelm, Jens; Staszewski, Ori; Wani, Khalida; Varlet, Pascale; Pages, Melanie; Temming, Petra; Lohmann, Dietmar; Selt, Florian; Witt, Hendrik; Milde, Till; Witt, Olaf; Aronica, Eleonora; Giangaspero, Felice; Rushing, Elisabeth; Scheurlen, Wolfram; Geisenberger, Christoph; Rodriguez, Fausto J; Becker, Albert; Preusser, Matthias; Haberler, Christine; Bjerkvig, Rolf; Cryan, Jane; Farrell, Michael; Deckert, Martina; Hench, Jürgen; Frank, Stephan; Serrano, Jonathan; Kannan, Kasthuri; Tsirigos, Aristotelis; Brück, Wolfgang; Hofer, Silvia; Brehmer, Stefanie; Seiz-Rosenhagen, Marcel; Hänggi, Daniel; Hans, Volkmar; Rozsnoki, Stephanie; Hansford, Jordan R; Kohlhof, Patricia; Kristensen, Bjarne W; Lechner, Matt; Lopes, Beatriz; Mawrin, Christian; Ketter, Ralf; Kulozik, Andreas; Khatib, Ziad; Heppner, Frank; Koch, Arend; Jouvet, Anne; Keohane, Catherine; Mühleisen, Helmut; Mueller, Wolf; Pohl, Ute; Prinz, Marco; Benner, Axel; Zapatka, Marc; Gottardo, Nicholas G; Driever, Pablo Hernáiz; Kramm, Christof M; Müller, Hermann L; Rutkowski, Stefan; von Hoff, Katja; Frühwald, Michael C; Gnekow, Astrid; Fleischhack, Gudrun; Tippelt, Stephan; Calaminus, Gabriele; Monoranu, Camelia-Maria; Perry, Arie; Jones, Chris; Jacques, Thomas S; Radlwimmer, Bernhard; Gessi, Marco; Pietsch, Torsten; Schramm, Johannes; Schackert, Gabriele; Westphal, Manfred; Reifenberger, Guido; Wesseling, Pieter; Weller, Michael; Collins, Vincent Peter; Blümcke, Ingmar; Bendszus, Martin; Debus, Jürgen; Huang, Annie; Jabado, Nada; Northcott, Paul A; Paulus, Werner; Gajjar, Amar; Robinson, Giles W; Taylor, Michael D; Jaunmuktane, Zane; Ryzhova, Marina; Platten, Michael; Unterberg, Andreas; Wick, Wolfgang; Karajannis, Matthias A; Mittelbronn, Michel; Acker, Till; Hartmann, Christian; Aldape, Kenneth; Schüller, Ulrich; Buslei, Rolf; Lichter, Peter; Kool, Marcel; Herold-Mende, Christel; Ellison, David W; Hasselblatt, Martin; Snuderl, Matija; Brandner, Sebastian; Korshunov, Andrey; von Deimling, Andreas; Pfister, Stefan M

2018-03-22

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

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.

An expert system design to diagnose cancer by using a new method reduced rule base.

PubMed

Başçiftçi, Fatih; Avuçlu, Emre

2018-04-01

A Medical Expert System (MES) was developed which uses Reduced Rule Base to diagnose cancer risk according to the symptoms in an individual. A total of 13 symptoms were used. With the new MES, the reduced rules are controlled instead of all possibilities (2 13 = 8192 different possibilities occur). By controlling reduced rules, results are found more quickly. The method of two-level simplification of Boolean functions was used to obtain Reduced Rule Base. Thanks to the developed application with the number of dynamic inputs and outputs on different platforms, anyone can easily test their own cancer easily. More accurate results were obtained considering all the possibilities related to cancer. Thirteen different risk factors were determined to determine the type of cancer. The truth table produced in our study has 13 inputs and 4 outputs. The Boolean Function Minimization method is used to obtain less situations by simplifying logical functions. Diagnosis of cancer quickly thanks to control of the simplified 4 output functions. Diagnosis made with the 4 output values obtained using Reduced Rule Base was found to be quicker than diagnosis made by screening all 2 13 = 8192 possibilities. With the improved MES, more probabilities were added to the process and more accurate diagnostic results were obtained. As a result of the simplification process in breast and renal cancer diagnosis 100% diagnosis speed gain, in cervical cancer and lung cancer diagnosis rate gain of 99% was obtained. With Boolean function minimization, less number of rules is evaluated instead of evaluating a large number of rules. Reducing the number of rules allows the designed system to work more efficiently and to save time, and facilitates to transfer the rules to the designed Expert systems. Interfaces were developed in different software platforms to enable users to test the accuracy of the application. Any one is able to diagnose the cancer itself using determinative risk factors. Thereby likely to beat the cancer with early diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Dawn of the digital diagnosis assisting system, can it open a new age for pathology?

NASA Astrophysics Data System (ADS)

Saito, Akira; Cosatto, Eric; Kiyuna, Tomoharu; Sakamoto, Michiie

2013-03-01

Digital pathology is developing based on the improvement and popularization of WSI (whole slide imaging) scanners. WSI scanners are widely expected to be used as the next generation microscope for diagnosis; however, their usage is currently mostly limited to education and archiving. Indeed, there are still many hindrances in using WSI scanners for diagnosis (not research purpose), two of the main reasons being the perceived high cost and small gain in productivity obtained by switching from the microscope to a WSI system and the lack of WSI standardization. We believe that a key factor for advancing digital pathology is the creation of computer assisted diagnosis systems (CAD). Such systems require high-resolution digitization of slides and provide a clear added value to the often costly conversion to WSI. We (NEC Corporation) are creating a CAD system, named e-Pathologist ®. This system is currently used at independent pathology labs for quality control (QC/QA), double-checking pathologists diagnosis and preventing missed cancers. At the end of 2012, about 80,000 slides, 200,000 tissues of gastric and colorectal samples will have been analyzed by e-Pathologist ®. Through the development of e-Pathologist ®, it has become clear that a computer program should be inspired by the pathologist diagnosis process, yet it should not be a mere copy or simulation of it. Indeed pathologists often approach the diagnosis of slides in a "holistic" manner, examining them at various magnifications, panning and zooming in a seemingly haphazard way that they often have a hard time to precisely describe. Hence there has been no clear recipe emerging from numerous interviews with pathologists on how to exactly computer code a diagnosis expert system. Instead, we focused on extracting a small set of histopathological features that were consistently indicated as important by the pathologists and then let the computer figure out how to interpret in a quantitative way the presence or absence of these features over the entire slide. Using the overall pathologists diagnosis (into a class of disease), we train the computer system using advanced machine learning techniques to predict the disease based on the extracted features. By considering the diagnosis of several expert pathologists during the training phase, we insure that the machine is learning a "gold standard" that will be applied consistently and objectively for all subsequent diagnosis, making them more predictable and reliable. Considering the future of digital pathology, it is essential for a CAD system to produce effective and accurate clinical data. To this effect, there remain many hurdles, including standardization as well as more research into seeking clinical evidences from "computer-friendly" objective measurements of histological images. Currently the most commonly used staining method is H&E (Hematoxylin and Eosin), but it is extremely difficult to standardize the H&E staining process. Current pathology criteria, category, definitions, and thresholds are all on based pathologists subjective observations. Digital pathology is an emerging field and researchers should bear responsibility not only for developing new algorithms, but also for understanding the meaning of measured quantitative data.

Near-infrared imaging for management of chronic maxillary sinusitis

NASA Astrophysics Data System (ADS)

You, Joon S.; Cerussi, Albert E.; Kim, James; Ison, Sean; Wong, Brian; Cui, Haotian; Bhandarkar, Naveen

2015-03-01

Efficient management of chronic sinusitis remains a great challenge for primary care physicians. Unlike ENT specialists using Computed Tomography scans, they lack an affordable and safe method to accurately screen and monitor sinus diseases in primary care settings. Lack of evidence-based sinusitis management leads to frequent under-treatments and unnecessary over-treatments (i.e. antibiotics). Previously, we reported low-cost optical imaging designs for oral illumination and facial optical imaging setup. It exploits the sensitivity of NIR transmission intensity and their unique patterns to the sinus structures and presence of fluid/mucous-buildup within the sinus cavities. Using the improved NIR system, we have obtained NIR sinus images of 45 subjects with varying degrees of sinusitis symptoms. We made diagnoses of these patients based on two types of evidence: symptoms alone or NIR images along. These diagnostic results were then compared to the gold standard diagnosis using computed tomography through sensitivity and specificity analysis. Our results indicate that diagnosis of mere presence of sinusitis that is, distinguishing between healthy individuals vs. diseased individuals did not improve much when using NIR imaging compared to the diagnosis based on symptoms alone (69% in sensitivity, 75% specificity). However, use of NIR imaging improved the differential diagnosis between mild and severe diseases significantly as the sensitivity improved from 75% for using diagnosis based on symptoms alone up to 95% for using diagnosis based on NIR images. Reported results demonstrate great promise for using NIR imaging system for management of chronic sinusitis patients in primary care settings without resorting to CT.

Hybrid Automated Diagnosis of Discrete/Continuous Systems

NASA Technical Reports Server (NTRS)

Park, Han; James, Mark; MacKey, Ryan; Cannon, Howard; Bajwa, Anapa; Maul, William

2007-01-01

A recently conceived method of automated diagnosis of a complex electromechanical system affords a complete set of capabilities for hybrid diagnosis in the case in which the state of the electromechanical system is characterized by both continuous and discrete values (as represented by analog and digital signals, respectively). The method is an integration of two complementary diagnostic systems: (1) beacon-based exception analysis for multi-missions (BEAM), which is primarily useful in the continuous domain and easily performs diagnoses in the presence of transients; and (2) Livingstone, which is primarily useful in the discrete domain and is typically restricted to quasi-steady conditions. BEAM has been described in several prior NASA Tech Briefs articles: "Software for Autonomous Diagnosis of Complex Systems" (NPO-20803), Vol. 26, No. 3 (March 2002), page 33; "Beacon-Based Exception Analysis for Multimissions" (NPO-20827), Vol. 26, No. 9 (September 2002), page 32; "Wavelet-Based Real-Time Diagnosis of Complex Systems" (NPO-20830), Vol. 27, No. 1 (January 2003), page 67; and "Integrated Formulation of Beacon-Based Exception Analysis for Multimissions" (NPO-21126), Vol. 27, No. 3 (March 2003), page 74. Briefly, BEAM is a complete data-analysis method, implemented in software, for real-time or off-line detection and characterization of faults. The basic premise of BEAM is to characterize a system from all available observations and train the characterization with respect to normal phases of operation. The observations are primarily continuous in nature. BEAM isolates anomalies by analyzing the deviations from nominal for each phase of operation. Livingstone is a model-based reasoner that uses a model of a system, controller commands, and sensor observations to track the system s state, and detect and diagnose faults. Livingstone models a system within the discrete domain. Therefore, continuous sensor readings, as well as time, must be discretized. To reason about continuous systems, Livingstone uses monitors that discretize the sensor readings using trending and thresholding techniques. In development of the a hybrid method, BEAM results were sent to Livingstone to serve as an independent source of evidence that is in addition to the evidence gathered by Livingstone standard monitors. The figure depicts the flow of data in an early version of a hybrid system dedicated to diagnosing a simulated electromechanical system. In effect, BEAM served as a "smart" monitor for Livingstone. BEAM read the simulation data, processed the data to form observations, and stored the observations in a file. A monitor stub synchronized the events recorded by BEAM with the output of the Livingstone standard monitors according to time tags. This information was fed to a real-time interface, which buffered and fed the information to Livingstone, and requested diagnoses at the appropriate times. In a test, the hybrid system was found to correctly identify a failed component in an electromechanical system for which neither BEAM nor Livingstone alone yielded the correct diagnosis.

Signal Analysis of Automotive Engine Spark Ignition System using Case-Based Reasoning (CBR) and Case-based Maintenance (CBM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, H.; Vong, C. M.; Wong, P. K.

2010-05-21

With the development of modern technology, modern vehicles adopt electronic control system for injection and ignition. In traditional way, whenever there is any malfunctioning in an automotive engine, an automotive mechanic usually performs a diagnosis in the ignition system of the engine to check any exceptional symptoms. In this paper, we present a case-based reasoning (CBR) approach to help solve human diagnosis problem. Nevertheless, one drawback of CBR system is that the case library will be expanded gradually after repeatedly running the system, which may cause inaccuracy and longer time for the CBR retrieval. To tackle this problem, case-based maintenancemore » (CBM) framework is employed so that the case library of the CBR system will be compressed by clustering to produce a set of representative cases. As a result, the performance (in retrieval accuracy and time) of the whole CBR system can be improved.« less

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.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

An online outlier identification and removal scheme for improving fault detection performance.

PubMed

Ferdowsi, Hasan; Jagannathan, Sarangapani; Zawodniok, Maciej

2014-05-01

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.

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.

Recent advances in biosensor development for the detection of cancer biomarkers.

PubMed

Jayanthi, V S P K Sankara Aditya; Das, Asim Bikas; Saxena, Urmila

2017-05-15

Cancer is the second largest disease throughout the world with an increasing mortality rate over the past few years. The patient's survival rate is uncertain due to the limitations of cancer diagnosis and therapy. Early diagnosis of cancer is decisive for its successful treatment. A biomarker-based cancer diagnosis may significantly improve the early diagnosis and subsequent treatment. Biosensors play a crucial role in the detection of biomarkers as they are easy to use, portable, and can do analysis in real time. This review describes various biosensors designed for detecting nucleic acid and protein-based cancer biomarkers for cancer diagnosis. It mainly lays emphasis on different approaches to use electrochemical, optical, and mass-based transduction systems in cancer biomarker detection. It also highlights the analytical performances of various biosensor designs concerning cancer biomarkers in detail. Copyright © 2016 Elsevier B.V. All rights reserved.

Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

PubMed

Li, Zhiming; Yu, Lan; Wang, Xin; Yu, Haiyang; Gao, Yuanxiang; Ren, Yande; Wang, Gang; Zhou, Xiaoming

2017-11-09

The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Review of Diagnosis-Related Group-Based Financing of Hospital Care

PubMed Central

Kocic, Sanja; Jakovljevic, Mihajlo

2016-01-01

Since the 1990s, diagnosis-related group (DRG)-based payment systems were gradually introduced in many countries. The main design characteristics of a DRG-based payment system are an exhaustive patient case classification system (ie, the system of diagnosis-related groupings) and the payment formula, which is based on the base rate multiplied by a relative cost weight specific for each DRG. Cases within the same DRG code group are expected to undergo similar clinical evolution. Consecutively, they should incur the costs of diagnostics and treatment within a predefined scale. Such predictability was proven in a number of cost-of-illness studies conducted on major prosperity diseases alongside clinical trials on efficiency. This was the case with risky pregnancies, chronic obstructive pulmonary disease, diabetes, depression, alcohol addiction, hepatitis, and cancer. This article presents experience of introduced DRG-based payments in countries of western and eastern Europe, Scandinavia, United States, Canada, and Australia. This article presents the results of few selected reviews and systematic reviews of the following evidence: published reports on health system reforms by World Health Organization, World Bank, Organization for Economic Co-operation and Development, Canadian Institute for Health Information, Canadian Health Services Research Foundation, and Centre for Health Economics University of York. Diverse payment systems have different strengths and weaknesses in relation to the various objectives. The advantages of the DRG payment system are reflected in the increased efficiency and transparency and reduced average length of stay. The disadvantage of DRG is creating financial incentives toward earlier hospital discharges. Occasionally, such polices are not in full accordance with the clinical benefit priorities. PMID:28462278

An expert fitness diagnosis system based on elastic cloud computing.

PubMed

Tseng, Kevin C; Wu, Chia-Chuan

2014-01-01

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

Computer-assisted education and interdisciplinary breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Whatmough, Pamela; Gale, Alastair G.; Wilson, A. R. M.

1996-04-01

The diagnosis of breast disease for screening or symptomatic women is largely arrived at by a multi-disciplinary team. We report work on the development and assessment of an inter- disciplinary computer based learning system to support the diagnosis of this disease. The diagnostic process is first modelled from different viewpoints and then appropriate knowledge structures pertinent to the domains of radiologist, pathologist and surgeon are depicted. Initially the underlying inter-relationships of the mammographic diagnostic approach were detailed which is largely considered here. Ultimately a system is envisaged which will link these specialties and act as a diagnostic aid as well as a multi-media educational system.

Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

NASA Astrophysics Data System (ADS)

Qin, Liguo; He, Xiao; Zhou, D. H.

2017-10-01

This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.

Two Methods for Efficient Solution of the Hitting-Set Problem

NASA Technical Reports Server (NTRS)

Vatan, Farrokh; Fijany, Amir

2005-01-01

A paper addresses much of the same subject matter as that of Fast Algorithms for Model-Based Diagnosis (NPO-30582), which appears elsewhere in this issue of NASA Tech Briefs. However, in the paper, the emphasis is more on the hitting-set problem (also known as the transversal problem), which is well known among experts in combinatorics. The authors primary interest in the hitting-set problem lies in its connection to the diagnosis problem: it is a theorem of model-based diagnosis that in the set-theory representation of the components of a system, the minimal diagnoses of a system are the minimal hitting sets of the system. In the paper, the hitting-set problem (and, hence, the diagnosis problem) is translated from a combinatorial to a computational problem by mapping it onto the Boolean satisfiability and integer- programming problems. The paper goes on to describe developments nearly identical to those summarized in the cited companion NASA Tech Briefs article, including the utilization of Boolean-satisfiability and integer- programming techniques to reduce the computation time and/or memory needed to solve the hitting-set problem.

A case-based reasoning tool for breast cancer knowledge management with data mining concepts and techniques

NASA Astrophysics Data System (ADS)

Demigha, Souâd.

2016-03-01

The paper presents a Case-Based Reasoning Tool for Breast Cancer Knowledge Management to improve breast cancer screening. To develop this tool, we combine both concepts and techniques of Case-Based Reasoning (CBR) and Data Mining (DM). Physicians and radiologists ground their diagnosis on their expertise (past experience) based on clinical cases. Case-Based Reasoning is the process of solving new problems based on the solutions of similar past problems and structured as cases. CBR is suitable for medical use. On the other hand, existing traditional hospital information systems (HIS), Radiological Information Systems (RIS) and Picture Archiving Information Systems (PACS) don't allow managing efficiently medical information because of its complexity and heterogeneity. Data Mining is the process of mining information from a data set and transform it into an understandable structure for further use. Combining CBR to Data Mining techniques will facilitate diagnosis and decision-making of medical experts.

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 component model is expressed as operating modes of the component and conditions for transitions between these various modes. Faults are modeled as transitions whose conditions for transitions are unknown (and have to be inferred through the reasoning process). Finally, the behavior of the components is expressed as a set of variables/ parameters and relations governing the interaction between the variables. The hybrid nature of the systems being modeled is captured by a combination of the above transitional model and behavioral model. Stochasticity is captured as probabilities associated with transitions (indicating the likelihood of that transition being taken), as well as noise on the sensed variables.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Case-based tutoring from a medical knowledge base.

PubMed

Chin, H L; Cooper, G F

1989-01-01

The past decade has seen the emergence of programs that make use of large knowledge bases to assist physicians in diagnosis within the general field of internal medicine. One such program, Internist-I, contains knowledge about over 600 diseases, covering a significant proportion of internal medicine. This paper describes the process of converting a subset of this knowledge base--in the area of cardiovascular diseases--into a probabilistic format, and the use of this resulting knowledge base to teach medical diagnostic knowledge. The system (called KBSimulator--for Knowledge-Based patient Simulator) generates simulated patient cases and uses these cases as a focal point from which to teach medical knowledge. This project demonstrates the feasibility of building an intelligent, flexible instructional system that uses a knowledge base constructed primarily for medical diagnosis.

A Mobile Multi-Agent Information System for Ubiquitous Fetal Monitoring

PubMed Central

Su, Chuan-Jun; Chu, Ta-Wei

2014-01-01

Electronic fetal monitoring (EFM) systems integrate many previously separate clinical activities related to fetal monitoring. Promoting the use of ubiquitous fetal monitoring services with real time status assessments requires a robust information platform equipped with an automatic diagnosis engine. This paper presents the design and development of a mobile multi-agent platform-based open information systems (IMAIS) with an automated diagnosis engine to support intensive and distributed ubiquitous fetal monitoring. The automatic diagnosis engine that we developed is capable of analyzing data in both traditional paper-based and digital formats. Issues related to interoperability, scalability, and openness in heterogeneous e-health environments are addressed through the adoption of a FIPA2000 standard compliant agent development platform—the Java Agent Development Environment (JADE). Integrating the IMAIS with light-weight, portable fetal monitor devices allows for continuous long-term monitoring without interfering with a patient’s everyday activities and without restricting her mobility. The system architecture can be also applied to vast monitoring scenarios such as elder care and vital sign monitoring. PMID:24452256

Overview of MDX-A System for Medical Diagnosis

PubMed Central

Mittal, S.; Chandrasekaran, B.; Smith, J.

1979-01-01

We describe the design and performance of MDX, an experimental medical diagnosis system, which currently diagnoses in the syndrome called Cholestasis. The needed medical knowledge is represented in a scheme called conceptual structures, which can be viewed as a collection of conceptual experts interacting according to certain well-defined principles. MDX has three components: the diagnostic system, a patient data base and a radiology consultant. We describe these components, the inter-expert communication system and the query language used by these components. The system is illustrated by means of its performance on a real case.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation

PubMed Central

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-01-01

Introduction: This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. Material and methods: The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. Results: The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. Conclusion: The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care. PMID:28883678

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.

AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

PubMed

Kayser, Klaus; Görtler, Jürgen; Bogovac, Milica; Bogovac, Aleksandar; Goldmann, Torsten; Vollmer, Ekkehard; Kayser, Gian

2009-01-01

The technological progress in digitalization of complete histological glass slides has opened a new door in tissue--based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all) fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue--based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue--based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures) and pixel based (texture) measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and continuous education in anatomy and pathology. First attempts to introduce them into routine work have been reported. Application of AI has been established by automated immunohistochemical measurement systems (EAMUS, www.diagnomX.eu). The performance of automated diagnosis has been reported for a broad variety of organs at sensitivity and specificity levels >85%). The implementation of a complete connected AI supported system is in its childhood. Application of AI in digital tissue--based diagnosis will allow the pathologists to work as supervisors and no longer as primary "water carriers". Its accurate use will give them the time needed to concentrating on difficult cases for the benefit of their patients.

Cloud-based image sharing network for collaborative imaging diagnosis and consultation

NASA Astrophysics Data System (ADS)

Yang, Yuanyuan; Gu, Yiping; Wang, Mingqing; Sun, Jianyong; Li, Ming; Zhang, Weiqiang; Zhang, Jianguo

2018-03-01

In this presentation, we presented a new approach to design cloud-based image sharing network for collaborative imaging diagnosis and consultation through Internet, which can enable radiologists, specialists and physicians locating in different sites collaboratively and interactively to do imaging diagnosis or consultation for difficult or emergency cases. The designed network combined a regional RIS, grid-based image distribution management, an integrated video conferencing system and multi-platform interactive image display devices together with secured messaging and data communication. There are three kinds of components in the network: edge server, grid-based imaging documents registry and repository, and multi-platform display devices. This network has been deployed in a public cloud platform of Alibaba through Internet since March 2017 and used for small lung nodule or early staging lung cancer diagnosis services between Radiology departments of Huadong hospital in Shanghai and the First Hospital of Jiaxing in Zhejiang Province.

A deep-learning based automatic pulmonary nodule detection system

NASA Astrophysics Data System (ADS)

Zhao, Yiyuan; Zhao, Liang; Yan, Zhennan; Wolf, Matthias; Zhan, Yiqiang

2018-02-01

Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.

Study on fault diagnosis and load feedback control system of combine harvester

NASA Astrophysics Data System (ADS)

Li, Ying; Wang, Kun

2017-01-01

In order to timely gain working status parameters of operating parts in combine harvester and improve its operating efficiency, fault diagnosis and load feedback control system is designed. In the system, rotation speed sensors were used to gather these signals of forward speed and rotation speeds of intermediate shaft, conveying trough, tangential and longitudinal flow threshing rotors, grain conveying auger. Using C8051 single chip microcomputer (SCM) as processor for main control unit, faults diagnosis and forward speed control were carried through by rotation speed ratio analysis of each channel rotation speed and intermediate shaft rotation speed by use of multi-sensor fused fuzzy control algorithm, and these processing results would be sent to touch screen and display work status of combine harvester. Field trials manifest that fault monitoring and load feedback control system has good man-machine interaction and the fault diagnosis method based on rotation speed ratios has low false alarm rate, and the system can realize automation control of forward speed for combine harvester.

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.

PubMed

Liu, Xiao; Wang, Xiaoli; Su, Qiang; Zhang, Mo; Zhu, Yanhong; Wang, Qiugen; Wang, Qian

2017-01-01

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.

PubMed

Welter, Petra; Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno, Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process.

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis

PubMed Central

Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno (né Lehmann), Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process. PMID:21672913

Computer Based Expert Systems.

ERIC Educational Resources Information Center

Parry, James D.; Ferrara, Joseph M.

1985-01-01

Claims knowledge-based expert computer systems can meet needs of rural schools for affordable expert advice and support and will play an important role in the future of rural education. Describes potential applications in prediction, interpretation, diagnosis, remediation, planning, monitoring, and instruction. (NEC)

An SSME High Pressure Oxidizer Turbopump diagnostic system using G2 real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2 real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for the SSME. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach has been adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

An SSME high pressure oxidizer turbopump diagnostic system using G2(TM) real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2(TM) real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for Space Shuttle Main Engine. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach was adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

a Study on Satellite Diagnostic Expert Systems Using Case-Based Approach

NASA Astrophysics Data System (ADS)

Park, Young-Tack; Kim, Jae-Hoon; Park, Hyun-Soo

1997-06-01

Many research works are on going to monitor and diagnose diverse malfunctions of satellite systems as the complexity and number of satellites increase. Currently, many works on monitoring and diagnosis are carried out by human experts but there are needs to automate much of the routine works of them. Hence, it is necessary to study on using expert systems which can assist human experts routine work by doing automatically, thereby allow human experts devote their expertise more critical and important areas of monitoring and diagnosis. In this paper, we are employing artificial intelligence techniques to model human experts' knowledge and inference the constructed knowledge. Especially, case-based approaches are used to construct a knowledge base to model human expert capabilities which use previous typical exemplars. We have designed and implemented a prototype case-based system for diagnosing satellite malfunctions using cases. Our system remembers typical failure cases and diagnoses a current malfunction by indexing the case base. Diverse methods are used to build a more user friendly interface which allows human experts can build a knowledge base in as easy way.

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

NASA Astrophysics Data System (ADS)

Traverso, A.; Lopez Torres, E.; Fantacci, M. E.; Cerello, P.

2017-05-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.

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.

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 security improvement of medical information.

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts.

PubMed

Misawa, Masashi; Kudo, Shin-Ei; Mori, Yuichi; Takeda, Kenichi; Maeda, Yasuharu; Kataoka, Shinichi; Nakamura, Hiroki; Kudo, Toyoki; Wakamura, Kunihiko; Hayashi, Takemasa; Katagiri, Atsushi; Baba, Toshiyuki; Ishida, Fumio; Inoue, Haruhiro; Nimura, Yukitaka; Oda, Msahiro; Mori, Kensaku

2017-05-01

Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists. We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated. ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; [Formula: see text]), but similar to experts (87.8 vs 84.2%; [Formula: see text]). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; [Formula: see text]) and comparable to experts (93.5 vs 90.8%; [Formula: see text]). ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

[Research and development of medical case database: a novel medical case information system integrating with biospecimen management].

PubMed

Pan, Shiyang; Mu, Yuan; Wang, Hong; Wang, Tong; Huang, Peijun; Ma, Jianfeng; Jiang, Li; Zhang, Jie; Gu, Bing; Yi, Lujiang

2010-04-01

To meet the needs of management of medical case information and biospecimen simultaneously, we developed a novel medical case information system integrating with biospecimen management. The database established by MS SQL Server 2000 covered, basic information, clinical diagnosis, imaging diagnosis, pathological diagnosis and clinical treatment of patient; physicochemical property, inventory management and laboratory analysis of biospecimen; users log and data maintenance. The client application developed by Visual C++ 6.0 was used to implement medical case and biospecimen management, which was based on Client/Server model. This system can perform input, browse, inquest, summary of case and related biospecimen information, and can automatically synthesize case-records based on the database. Management of not only a long-term follow-up on individual, but also of grouped cases organized according to the aim of research can be achieved by the system. This system can improve the efficiency and quality of clinical researches while biospecimens are used coordinately. It realizes synthesized and dynamic management of medical case and biospecimen, which may be considered as a new management platform.

Robotic telepathology for intraoperative remote diagnosis using a still-imaging-based system.

PubMed

Demichelis, F; Barbareschi, M; Boi, S; Clemente, C; Dalla Palma, P; Eccher, C; Forti, S

2001-11-01

The aim of the present study was to assess whether a telemicroscopy system based on static imaging could provide a remote intraoperative frozen section service. Three pathologists evaluated 70 consecutive frozen section cases (for a total of 210 diagnoses) using a static telemicroscopy system (STeMiSy) and light microscopy (LM). STeMiSy uses a robotic microscope, enabling full remote control by consultant pathologists in a near real-time manner. Clinically important concordance between STeMiSy and LM was 98.6% (95.2% overall concordance), indicating very good agreement. The rates of deferred diagnoses given by STeMiSy and LM were comparable (11.0% and 9.5%, respectively). Compared with the consensus diagnosis, the diagnostic accuracy of STeMiSy and LM was 95.2% and 96.2%. The mean viewing time per slide was 3.6 minutes, and the overall time to make a diagnosis by STeMiSy was 6.2 minutes, conforming to intraoperative practice requirements. Our study demonstrates that a static imaging active telepathology system is comparable to dynamic telepathology systems and can provide a routine frozen section service.

76 FR 32815 - Medicaid Program; Payment Adjustment for Provider-Preventable Conditions Including Health Care...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-06

... Hospital IPPS Inpatient prospective payment system MS-DRG Diagnosis-related group NCA National coverage... based on the ``inpatient prospective payment system'' (IPPS) described in section 1886(d) of the Act... and procedures, and payment systems. We reviewed various articles, reports, summaries, and data bases...

Development of an Intelligent Instruction System for Mathematical Computation

ERIC Educational Resources Information Center

Kim, Du Gyu; Lee, Jaemu

2013-01-01

In this paper, we propose the development of a web-based, intelligent instruction system to help elementary school students for mathematical computation. We concentrate on the intelligence facilities which support diagnosis and advice. The existing web-based instruction systems merely give information on whether the learners' replies are…

Prediction of Disease Case Severity Level To Determine INA CBGs Rate

NASA Astrophysics Data System (ADS)

Puspitorini, Sukma; Kusumadewi, Sri; Rosita, Linda

2017-03-01

Indonesian Case-Based Groups (INA CBGs) is case-mix payment system using software grouper application. INA CBGs consisting of four digits code where the last digits indicating the severity level of disease cases. Severity level influence by secondary diagnosis (complications and co-morbidity) related to resource intensity level. It is medical resources used to treat a hospitalized patient. Objectives of this research is developing decision support system to predict severity level of disease cases and illustrate INA CBGs rate by using data mining decision tree classification model. Primary diagnosis (DU), first secondary diagnosis (DS 1), and second secondary diagnosis (DS 2) are attributes that used as input of severity level. The training process using C4.5 algorithm and the rules will represent in the IF-THEN form. Credibility of the system analyzed through testing process and confusion matrix present the results. Outcome of this research shows that first secondary diagnosis influence significant to form severity level predicting rules from new disease cases and INA CBGs rate illustration.

Polymerase chain reaction-based detection of Pneumocystis jirovecii in bronchoalveolar lavage fluid for the diagnosis of pneumocystis pneumonia.

PubMed

Oren, Ilana; Hardak, Emilia; Finkelstein, Renato; Yigla, Mordechai; Sprecher, Hannah

2011-09-01

The diagnosis of pneumocystis pneumonia (PCP) in non-human immunodeficiency virus (HIV)-infected immunocompromised patients is notoriously difficult. The recent advent of polymerase chain reaction (PCR)-based detection systems, based on the identification of single fungal genes, has markedly improved diagnostic accuracy in this ominous disease. In an attempt to further improve diagnostic yield, the authors used a PCR-based detection system for Pneumocystis jirovecii, based on targeting 3 distinct genes. During the 4-year period (January 2005 to January 2009), all consecutive immunocompromised patients suspected of having PCP in the differential diagnosis underwent bronchoscopy with bronchoalveolar lavage sampling for the evaluation of the etiology of pulmonary infiltrates. Bronchoalveolar fluid was tested for the presence of a wide variety of possible etiological microorganisms. In a cohort of 214 immunocompromised patients (of which 198 were non-HIV immunocompromised patients) who underwent bronchoscopy with bronchoalveolar lavage for evaluation of pulmonary infiltrates, PCR correctly diagnosed PCP in 75% (42/56) compared with 14% (8/56) diagnosed by traditional stains, and increased diagnostic yield 5.4-fold. Given the absence of a sensitive gold standard, this study demonstrates the usefulness of a multigene PCR-based detection of Pneumocystis jirovecii DNA for supporting the clinical diagnosis of PCP, with high sensitivity and negative predictive value rates compared with direct stains, especially in non-HIV immunocompromised patients.

[A new information technology for system diagnosis of functional activity of human organs].

PubMed

Avshalumov, A Sh; Sudakov, K V; Filaretov, G F

2006-01-01

The goal of this work was to consider a new diagnostic technology based on analysis of objective information parameters of functional activity and interaction of normal and pathologically changed human organs. The technology is based on the use of very low power millimeter (EHF) radiation emitted by human body and other biological objects in the process of vital activity. The importance of consideration of the information aspect of vital activity from the standpoint of the theory of functional systems suggested by P. K. Anokhin is emphasized. The suggested information technology is theoretically substantiated. The capabilities of the suggested technology for diagnosis, as well as the difficulties of its practical implementation caused by very low power of electromagnetic fields generated by human body, are discussed. It is noted that only use of modern radiophysical equipment together with new software based on specially developed algorithms made it possible to construct a medical EHF diagnostic system for effective implementation of the suggested technology. The system structure, functions of its components, the examination procedure, and the form of representation of diagnostic information are described together with the specific features of applied software based on the principle of maximal objectivity of analysis and interpretation of the results of diagnosis on the basis of artificial intelligence algorithms. The diagnostic capabilities of the system are illustrated by several examples.

Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Analysis of Renal Cell Carcinoma as a First Step for Developing Mass Spectrometry-Based Diagnostics

NASA Astrophysics Data System (ADS)

Yoshimura, Kentaro; Chen, Lee Chuin; Mandal, Mridul Kanti; Nakazawa, Tadao; Yu, Zhan; Uchiyama, Takahito; Hori, Hirokazu; Tanabe, Kunio; Kubota, Takeo; Fujii, Hideki; Katoh, Ryohei; Hiraoka, Kenzo; Takeda, Sen

2012-10-01

Immediate diagnosis of human specimen is an essential prerequisites in medical routines. This study aimed to establish a novel cancer diagnostics system based on probe electrospray ionization-mass spectrometry (PESI-MS) combined with statistical data processing. PESI-MS uses a very fine acupuncture needle as a probe for sampling as well as for ionization. To demonstrate the applicability of PESI-MS for cancer diagnosis, we analyzed nine cases of clear cell renal cell carcinoma (ccRCC) by PESI-MS and processed the data by principal components analysis (PCA). Our system successfully delineated the differences in lipid composition between non-cancerous and cancerous regions. In this case, triacylglycerol (TAG) was reproducibly detected in the cancerous tissue of nine different individuals, the result being consistent with well-known profiles of ccRCC. Moreover, this system enabled us to detect the boundaries of cancerous regions based on the expression of TAG. These results strongly suggest that PESI-MS will be applicable to cancer diagnosis, especially when the number of data is augmented.

A Computer-Based Nursing Diagnosis Consultant

PubMed Central

Evans, Steven

1984-01-01

This consultant permits a nurse to enter patient signs and symptoms which are then interpreted by the system in order to relate them to well-established nursing-related dysfunctional patterns. The system attempts to confirm the pattern by soliciting additional patient information from the nurse. This process provides an educational prompt to the nurse, and the suggestions of the system also provide a clinical support tool that can be of practical value. As our testing hones the system and subtlety is added to the weighing of the evidence the nurse provides, it is expected that this tool will be a useful adjunct to computer-based nursing services in support of health care. This Nursing Diagnosis Consultant is yet another element in the COMMES family of consultants for health professionals.

Contrast-enhanced photoacoustic tomography of human joints

NASA Astrophysics Data System (ADS)

Tian, Chao; Keswani, Rahul K.; Gandikota, Girish; Rosania, Gus R.; Wang, Xueding

2016-03-01

Photoacoustic tomography (PAT) provides a unique tool to diagnose inflammatory arthritis. However, the specificity and sensitivity of PAT based on endogenous contrasts is limited. The development of contrast enhanced PAT imaging modalities in combination with small molecule contrast agents could lead to improvements in diagnosis and treatment of joint disease. Accordingly, we adapted and tested a PAT clinical imaging system for imaging the human joints, in combination with a novel PAT contrast agent derived from an FDA-approved small molecule drug. Imaging results based on a photoacoustic and ultrasound (PA/US) dual-modality system revealed that this contrast-enhanced PAT imaging system may offer additional information beyond single-modality PA or US imaging system, for the imaging, diagnosis and assessment of inflammatory arthritis.

The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

NASA Technical Reports Server (NTRS)

Lee, S. C.

1989-01-01

The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

A handheld computer-aided diagnosis system and simulated analysis

NASA Astrophysics Data System (ADS)

Su, Mingjian; Zhang, Xuejun; Liu, Brent; Su, Kening; Louie, Ryan

2016-03-01

This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.

Proposal of diagnostic process model for computer based diagnosis.

PubMed

Matsumura, Yasushi; Takeda, Toshihiro; Manabe, Shiro; Saito, Hirokazu; Teramoto, Kei; Kuwata, Shigeki; Mihara, Naoki

2012-01-01

We aim at making a diagnosis support system that can be put to practical use. We proposed a diagnostic process model based on simple knowledge which can be gleaned from textbooks. We defined clinical finding (CF) as a general concept for patient's symptom or findings etc., whose value is expressed by Boolean. We call the combination of several CFs a "CF pattern", and a set of CF patterns with concomitant diseases "case base". We consider diagnosis as a process of searching an instance from the case base whose CF pattern is concomitant with that of a patient. The diseases which have the same CF pattern are candidates for diagnosis. Then we select a CF which is present in part of the candidates and check whether it is present or absent in the patient in order to narrow down the candidates. Because the case base does not exist in reality, the probability of CF pattern is calculated by the product of CF occurrence rate assuming that occurrence of CF is independent. Therefore the knowledge required for diagnosis is frequency of disease under sex and age group and CF-disease relation (CF and its occurrence rate in the disease). By processing these two types of knowledge, diagnosis can be made.

Bayesian networks and statistical analysis application to analyze the diagnostic test accuracy

NASA Astrophysics Data System (ADS)

Orzechowski, P.; Makal, Jaroslaw; Onisko, A.

2005-02-01

The computer aided BPH diagnosis system based on Bayesian network is described in the paper. First result are compared to a given statistical method. Different statistical methods are used successfully in medicine for years. However, the undoubted advantages of probabilistic methods make them useful in application in newly created systems which are frequent in medicine, but do not have full and competent knowledge. The article presents advantages of the computer aided BPH diagnosis system in clinical practice for urologists.

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.

Digital diagnosis of medical images

NASA Astrophysics Data System (ADS)

Heinonen, Tomi; Kuismin, Raimo; Jormalainen, Raimo; Dastidar, Prasun; Frey, Harry; Eskola, Hannu

2001-08-01

The popularity of digital imaging devices and PACS installations has increased during the last years. Still, images are analyzed and diagnosed using conventional techniques. Our research group begun to study the requirements for digital image diagnostic methods to be applied together with PACS systems. The research was focused on various image analysis procedures (e.g., segmentation, volumetry, 3D visualization, image fusion, anatomic atlas, etc.) that could be useful in medical diagnosis. We have developed Image Analysis software (www.medimag.net) to enable several image-processing applications in medical diagnosis, such as volumetry, multimodal visualization, and 3D visualizations. We have also developed a commercial scalable image archive system (ActaServer, supports DICOM) based on component technology (www.acta.fi), and several telemedicine applications. All the software and systems operate in NT environment and are in clinical use in several hospitals. The analysis software have been applied in clinical work and utilized in numerous patient cases (500 patients). This method has been used in the diagnosis, therapy and follow-up in various diseases of the central nervous system (CNS), respiratory system (RS) and human reproductive system (HRS). In many of these diseases e.g. Systemic Lupus Erythematosus (CNS), nasal airways diseases (RS) and ovarian tumors (HRS), these methods have been used for the first time in clinical work. According to our results, digital diagnosis improves diagnostic capabilities, and together with PACS installations it will become standard tool during the next decade by enabling more accurate diagnosis and patient follow-up.

Approach to Health Supporting System Using Traditional Chinese Medicine

NASA Astrophysics Data System (ADS)

Watsuji, Tadashi; Shinohara, Shoji; Arita, Seizaburo

The primary prevention of disease related to the lifestyle is an essential theme in medical research. Preventing before it arises is the important concept in traditional Chinese medicine (TCM). Since TCM, which emphasizes individual physical condition in medical treatment, has recently attracted considerable attention globally, objective diagnostic methods in TCM have been investigated in this work. Firstly, the fuzzy theory was applied to develop a tongue diagnosis supporting system based on the tongue diagnosis in TCM. Secondly, the usefulness of TCM health questionnaire was examined to identify individual physical condition. Our results suggest that the TCM health questionnaire is useful in the construction of a health supporting system based on TCM.

Comparative research of redundant strap down inertial navigation system based on different configuration schemes

NASA Astrophysics Data System (ADS)

Yu, Yuting; Cheng, Ming

2018-05-01

Aiming at various configuration scheme and inertial measurement units of Strapdown Inertial Navigation System, selected tetrahedron skew configuration and coaxial orthogonal configuration by nine low cost IMU to build system. Calculation and simulation the performance index, reliability and fault diagnosis ability of the navigation system. Analysis shows that the reliability and reconfiguration scheme of skew configuration is superior to the orthogonal configuration scheme, while the performance index and fault diagnosis ability of the system are similar. The work in this paper provides a strong reference for the selection of engineering applications.

RadPath: A Web-based System for Integrating and Correlating Radiology and Pathology Findings During Cancer Diagnosis.

PubMed

Arnold, Corey W; Wallace, W Dean; Chen, Shawn; Oh, Andrea; Abtin, Fereidoun; Genshaft, Scott; Binder, Scott; Aberle, Denise; Enzmann, Dieter

2016-01-01

The current paradigm of cancer diagnosis involves uncoordinated communication of findings from radiology and pathology to downstream physicians. Discordance between these findings can require additional time from downstream users to resolve, or given incorrect resolution, may adversely impact treatment decisions. To mitigate this problem, we developed a web-based system, called RadPath, for correlating and integrating radiology and pathology reporting. RadPath includes interfaces to our institution's clinical information systems, which are used to retrieve reports, images, and test results that are structured into an interactive compendium for a diagnostic patient case. The system includes an editing interface for physicians, allowing for the inclusion of additional clinical data, as well as the ability to retrospectively correlate and contextualize imaging findings following pathology diagnosis. During pilot deployment and testing over the course of 1 year, physicians at our institution have completed 60 RadPath cases, requiring an average of 128 seconds from a radiologist and an average of 93 seconds from a pathologist per case. Several technical and workflow challenges were encountered during development, including interfacing with diverse clinical information systems, automatically structuring report contents, and determining the appropriate physicians to create RadPath summaries. Reaction to RadPath has been positive, with users valuing the system's ability to consolidate diagnostic information. With the increasing complexity of medicine and the movement toward team-based disease management, there is a need for improved clinical communication and information exchange. RadPath provides a platform for generating coherent and correlated diagnostic summaries in cancer diagnosis with minimal additional effort from physicians. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Truck circuits diagnosis for railway lines equipped with an automatic block signalling system

NASA Astrophysics Data System (ADS)

Spunei, E.; Piroi, I.; Muscai, C.; Răduca, E.; Piroi, F.

2018-01-01

This work presents a diagnosis method for detecting track circuits failures on a railway traffic line equipped with an Automatic Block Signalling installation. The diagnosis method uses the installation’s electrical schemas, based on which a series of diagnosis charts have been created. Further, the diagnosis charts were used to develop a software package, CDCBla, which substantially contributes to reducing the diagnosis time and human error during failure remedies. The proposed method can also be used as a training package for the maintenance staff. Since the diagnosis method here does not need signal or measurement inputs, using it does not necessitate additional IT knowledge and can be deployed on a mobile computing device (tablet, smart phone).

Knowledge-based fault diagnosis system for refuse collection vehicle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, CheeFai; Juffrizal, K.; Khalil, S. N.

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 knowledgemore » 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.« less

Pricing Medicare's diagnosis-related groups: Charges versus estimated costs

PubMed Central

Price, Kurt F.

1989-01-01

Hospital payments under Medicare's prospective payment system (PPS) are based on prices established for 474 diagnosis-related groups (DRG's). Previous analyses using 1981 data demonstrated that DRG prices based on charges alone were not that different from prices calculated from estimated costs. Data for 1986 were used in this study to show that the differences between the two sets of DRG prices are much larger than previously reported. If DRG prices were once again based on estimated costs instead of the current charge-based prices, payments would be significantly redistributed. PMID:10313356

Mayo Clinic/Renal Pathology Society Consensus Report on Pathologic Classification, Diagnosis, and Reporting of GN.

PubMed

Sethi, Sanjeev; Haas, Mark; Markowitz, Glen S; D'Agati, Vivette D; Rennke, Helmut G; Jennette, J Charles; Bajema, Ingeborg M; Alpers, Charles E; Chang, Anthony; Cornell, Lynn D; Cosio, Fernando G; Fogo, Agnes B; Glassock, Richard J; Hariharan, Sundaram; Kambham, Neeraja; Lager, Donna J; Leung, Nelson; Mengel, Michael; Nath, Karl A; Roberts, Ian S; Rovin, Brad H; Seshan, Surya V; Smith, Richard J H; Walker, Patrick D; Winearls, Christopher G; Appel, Gerald B; Alexander, Mariam P; Cattran, Daniel C; Casado, Carmen Avila; Cook, H Terence; De Vriese, An S; Radhakrishnan, Jai; Racusen, Lorraine C; Ronco, Pierre; Fervenza, Fernando C

2016-05-01

Renal pathologists and nephrologists met on February 20, 2015 to establish an etiology/pathogenesis-based system for classification and diagnosis of GN, with a major aim of standardizing the kidney biopsy report of GN. On the basis of etiology/pathogenesis, GN is classified into the following five pathogenic types, each with specific disease entities: immune-complex GN, pauci-immune GN, antiglomerular basement membrane GN, monoclonal Ig GN, and C3 glomerulopathy. The pathogenesis-based classification forms the basis of the kidney biopsy report. To standardize the report, the diagnosis consists of a primary diagnosis and a secondary diagnosis. The primary diagnosis should include the disease entity/pathogenic type (if disease entity is not known) followed in order by pattern of injury (mixed patterns may be present); score/grade/class for disease entities, such as IgA nephropathy, lupus nephritis, and ANCA GN; and additional features as detailed herein. A pattern diagnosis as the sole primary diagnosis is not recommended. Secondary diagnoses should be reported separately and include coexisting lesions that do not form the primary diagnosis. Guidelines for the report format, light microscopy, immunofluorescence microscopy, electron microscopy, and ancillary studies are also provided. In summary, this consensus report emphasizes a pathogenesis-based classification of GN and provides guidelines for the standardized reporting of GN. Copyright © 2016 by the American Society of Nephrology.

Advances in Psychiatric Diagnosis: Past, Present, and Future.

PubMed

North, Carol S; Surís, Alina M

2017-04-26

This editorial examines controversies identified by the articles in this special issue, which explore psychopathology in the broad history of the classification of selected psychiatric disorders and syndromes over time through current American criteria. Psychiatric diagnosis has a long history of scientific investigation and application, with periods of rapid change, instability, and heated controversy associated with it. The articles in this issue examine the history of psychiatric nomenclature and explore current and future directions in psychiatric diagnosis through the various versions of accepted diagnostic criteria and accompanying research literature addressing the criteria. The articles seek to guide readers in appreciating the complexities of psychiatric diagnosis as the field of psychiatry pushes forward toward future advancements in diagnosis. Despite efforts of many scientists to advance a diagnostic classification system that incorporates neuroscience and genetics, it has been argued that it may be premature to attempt to move to a biologically-based classification system, because psychiatric disorders cannot yet be fully distinguished by any specific biological markers. For now, the symptom-based criteria that the field has been using continue to serve many essential purposes, including selection of the most effective treatment, communication about disease with colleagues, education about psychiatric illness, and support for ongoing research.

An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification.

PubMed

Marin, D; Gegundez-Arias, M E; Ponte, B; Alvarez, F; Garrido, J; Ortega, C; Vasallo, M J; Bravo, J M

2018-01-10

The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme. Graphical Abstract Diagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images.

Iris recognition and what is next? Iris diagnosis: a new challenging topic for machine vision from image acquisition to image interpretation

NASA Astrophysics Data System (ADS)

Perner, Petra

2017-03-01

Molecular image-based techniques are widely used in medicine to detect specific diseases. Look diagnosis is an important issue but also the analysis of the eye plays an important role in order to detect specific diseases. These topics are important topics in medicine and the standardization of these topics by an automatic system can be a new challenging field for machine vision. Compared to iris recognition has the iris diagnosis much more higher demands for the image acquisition and interpretation of the iris. One understands by iris diagnosis (Iridology) the investigation and analysis of the colored part of the eye, the iris, to discover factors, which play an important role for the prevention and treatment of illnesses, but also for the preservation of an optimum health. An automatic system would pave the way for a much wider use of the iris diagnosis for the diagnosis of illnesses and for the purpose of individual health protection. With this paper, we describe our work towards an automatic iris diagnosis system. We describe the image acquisition and the problems with it. Different ways are explained for image acquisition and image preprocessing. We describe the image analysis method for the detection of the iris. The meta-model for image interpretation is given. Based on this model we show the many tasks for image analysis that range from different image-object feature analysis, spatial image analysis to color image analysis. Our first results for the recognition of the iris are given. We describe how detecting the pupil and not wanted lamp spots. We explain how to recognize orange blue spots in the iris and match them against the topological map of the iris. Finally, we give an outlook for further work.

Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification.

PubMed

AbuHassan, Kamal J; Bakhori, Noremylia M; Kusnin, Norzila; Azmi, Umi Z M; Tania, Marzia H; Evans, Benjamin A; Yusof, Nor A; Hossain, M A

2017-07-01

Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.

Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

PubMed

Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

2017-05-01

Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

Diesel Technology: Engines. [Teacher and Student Editions.

ERIC Educational Resources Information Center

Barbieri, Dave; Miller, Roger; Kellum, Mary

Competency-based teacher and student materials on diesel engines are provided for a diesel technology curriculum. Seventeen units of instruction cover the following topics: introduction to engine principles and procedures; engine systems and components; fuel systems; engine diagnosis and maintenance. The materials are based on the…

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows.

PubMed

Depeursinge, Adrien; Vargas, Alejandro; Gaillard, Frédéric; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning

2012-01-01

Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.

Web-based triage in a college health setting.

PubMed

Sole, Mary Lou; Stuart, Patricia L; Deichen, Michael

2006-01-01

The authors describe the initiation and use of a Web-based triage system in a college health setting. During the first 4 months of implementation, the system recorded 1,290 encounters. More women accessed the system (70%); the average age was 21.8 years. The Web-based triage system advised the majority of students to seek care within 24 hours; however, it recommended self-care management in 22.7% of encounters. Sore throat was the most frequent chief complaint (14.2%). A subset of 59 students received treatment at student health services after requesting an appointment via e-mail. The authors used kappa statistics to compare congruence between chief complaint and 24/7 WebMed classification (kappa = .94), between chief complaint and student health center diagnosis (kappa = .91), and between 24/7 WebMed classification and student health center diagnosis (kappa = .89). Initial evaluation showed high use and good accuracy of Web-based triage. This service provides education and advice to students about their health care concerns.

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.

A Point-of-Care Raman Spectroscopy-Based Device for the Diagnosis of Gout and Pseudogout: Comparison With the Clinical Standard Microscopy.

PubMed

Li, Bolan; Singer, Nora G; Yeni, Yener N; Haggins, Donard G; Barnboym, Emma; Oravec, Daniel; Lewis, Steven; Akkus, Ozan

2016-07-01

To demonstrate the usefulness of a novel medical device based on Raman spectroscopy for the rapid point-of-care diagnosis of gout and pseudogout. A shoebox-sized point-of-care Raman spectroscopy (POCRS) device was developed for use in the diagnosis of gout and pseudogout. The device included a disposable syringe microfiltration kit to collect arthropathic crystals from synovial fluid and a customized automated Raman spectroscopy system to chemically identify crystal species. Diagnosis according to the findings of POCRS was compared with the clinical standard diagnosis based on compensated polarized light microscopy (CPLM) of synovial fluid aspirates collected from symptomatic patients (n = 174). Kappa coefficients were used to measure the agreement between POCRS and CPLM findings. Overall, POCRS and CPLM results were consistent in 89.7% of samples (156 of 174). For the diagnosis of gout, the kappa coefficient for POCRS and CPLM was 0.84 (95% confidence interval [95% CI] 0.75-0.94). For the diagnosis of pseudogout, the kappa coefficient for POCRS and CPLM was 0.61 (95% CI 0.42-0.81). Kappa coefficients indicated that there was excellent agreement between POCRS and CPLM for the diagnosis of gout, with good agreement for the diagnosis of pseudogout. The POCRS device holds the potential to standardize and expedite the time to clinical diagnosis of gout and pseudogout, especially in settings where certified operators trained for CPLM analysis are not available. © 2016, American College of Rheumatology.

First International Diagnosis Competition - DXC'09

NASA Technical Reports Server (NTRS)

Kurtoglu, tolga; Narasimhan, Sriram; Poll, Scott; Garcia, David; Kuhn, Lukas; deKleer, Johan; vanGemund, Arjan; Feldman, Alexander

2009-01-01

A framework to compare and evaluate diagnosis algorithms (DAs) has been created jointly by NASA Ames Research Center and PARC. In this paper, we present the first concrete implementation of this framework as a competition called DXC 09. The goal of this competition was to evaluate and compare DAs in a common platform and to determine a winner based on diagnosis results. 12 DAs (model-based and otherwise) competed in this first year of the competition in 3 tracks that included industrial and synthetic systems. Specifically, the participants provided algorithms that communicated with the run-time architecture to receive scenario data and return diagnostic results. These algorithms were run on extended scenario data sets (different from sample set) to compute a set of pre-defined metrics. A ranking scheme based on weighted metrics was used to declare winners. This paper presents the systems used in DXC 09, description of faults and data sets, a listing of participating DAs, the metrics and results computed from running the DAs, and a superficial analysis of the results.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.

PubMed

Jian, Wushuai; Sun, Xueyan; Luo, Shuqian

2012-12-19

Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

PubMed Central

2012-01-01

Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

Computer-assisted initial diagnosis of rare diseases

PubMed Central

Piñol, Marc; Vilaplana, Jordi; Teixidó, Ivan; Cruz, Joaquim; Comas, Jorge; Vilaprinyo, Ester; Sorribas, Albert

2016-01-01

Introduction. Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype. Methods. Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis. Results. We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms. Discussion. The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed at http://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded from https://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers. PMID:27547534

A Review of Diagnostic Techniques for ISHM Applications

NASA Technical Reports Server (NTRS)

Patterson-Hine, Ann; Biswas, Gautam; Aaseng, Gordon; Narasimhan, Sriam; Pattipati, Krishna

2005-01-01

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.

[The role of fetal echocardiography in the prenatal diagnosis of aneuploidy based upon prenatally diagnosed patau syndrome fetuses (case analysis)].

PubMed

Janiak, Katarzyna; Kaczmarek, Piotr; Krasoń, Aneta; Nowicki, Grzegorz; Piotrowicz, Małgorzata; Respondek-Liberska, Maria

2002-07-01

Assessment of usefulness of the fetal echocardiography and genetic sonography in prenatal diagnosis trisomy 13 (retrospective analysis). Between 1994-1999 at the Department for Diagnosis of Congenital Malformation at the Institute of PPMH in 11 fetuses with Patau Syndrome ultrasound and echocardiography examination were performed. In our study the most of cases come from low risk of pregnant women. Fetal heart defect was the most common anomaly diagnosed prenatally in fetuses with Patau Syndrome (7/11), the second one were central nervous system anomalies (6/11) and genitourinary system anomalies (6/11).

Diagnosis-related group (DRG)-based case-mix funding system, a promising alternative for fee for service payment in China.

PubMed

Zhao, Cuirong; Wang, Chao; Shen, Chengwu; Wang, Qian

2018-05-13

Fee for services (FFS) is the prevailing method of payment in most Chinese public hospitals. Under this retrospective payment system, medical care providers are paid based on medical services and tend to over-treat to maximize their income, thereby contributing to rising medical costs and uncontrollable health expenditures to a large extent. Payment reform needs to be promptly implemented to move to a prospective payment plan. The diagnosis-related group (DRG)-based case-mix payment system, with its superior efficiency and containment of costs, has garnered increased attention and it represents a promising alternative. This article briefly describes the DRG-based case-mix payment system, it comparatively analyzes differences between FFS and case-mix funding systems, and it describes the implementation of DRGs in China. China's social and economic conditions differ across regions, so establishment of a national payment standard will take time and involve difficulties. No single method of provider payment is perfect. Measures to monitor and minimize the negative ethical implications and unintended effects of a DRG-based case-mix payment system are essential to ensuring the lasting social benefits of payment reform in Chinese public hospitals.

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.

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

Using Supervised Learning Techniques for Diagnosis of Dynamic Systems

DTIC Science & Technology

2002-05-04

M. Gasca 2 , Juan A. Ortega2 Abstract. This paper describes an approach based on supervised diagnose systems faults are needed to maintain the systems...labelled, data will be used for this purpose [5] [6]. treated to add additional information about the running of system. In [7] the fundaments of the based ...8] proposes classification tool to the set of labelled and treated data. This a consistency- based approach with qualitative models. way, any

Robust fault diagnosis of physical systems in operation. Ph.D. Thesis - Rutgers - The State Univ.

NASA Technical Reports Server (NTRS)

Abbott, Kathy Hamilton

1991-01-01

Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of diagnosis. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based diagnosis systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative diagnosis. The suitability of this constructive approach is shown for diagnosing fault propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.

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.

An Ontology for Identifying Cyber Intrusion Induced Faults in Process Control Systems

NASA Astrophysics Data System (ADS)

Hieb, Jeffrey; Graham, James; Guan, Jian

This paper presents an ontological framework that permits formal representations of process control systems, including elements of the process being controlled and the control system itself. A fault diagnosis algorithm based on the ontological model is also presented. The algorithm can identify traditional process elements as well as control system elements (e.g., IP network and SCADA protocol) as fault sources. When these elements are identified as a likely fault source, the possibility exists that the process fault is induced by a cyber intrusion. A laboratory-scale distillation column is used to illustrate the model and the algorithm. Coupled with a well-defined statistical process model, this fault diagnosis approach provides cyber security enhanced fault diagnosis information to plant operators and can help identify that a cyber attack is underway before a major process failure is experienced.

A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

PubMed Central

Zhao, Weixiang; Davis, Cristina E.

2011-01-01

Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network. Conclusion In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems. PMID:21515033

Automated knowledge generation

NASA Technical Reports Server (NTRS)

Myler, Harley R.; Gonzalez, Avelino J.

1988-01-01

The general objectives of the NASA/UCF Automated Knowledge Generation Project were the development of an intelligent software system that could access CAD design data bases, interpret them, and generate a diagnostic knowledge base in the form of a system model. The initial area of concentration is in the diagnosis of the process control system using the Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. A secondary objective was the study of general problems of automated knowledge generation. A prototype was developed, based on object-oriented language (Flavors).

Nonparametric method for failures diagnosis in the actuating subsystem of aircraft control system

NASA Astrophysics Data System (ADS)

Terentev, M. N.; Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.

2018-02-01

In this paper we design a nonparametric method for failures diagnosis in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on analytical nonparametric one-step-ahead state prediction approach. This makes it possible to predict the behavior of unidentified and failure dynamic systems, to weaken the requirements to control signals, and to reduce the diagnostic time and problem complexity.

Parasite-based malaria diagnosis: Are Health Systems in Uganda equipped enough to implement the policy?

PubMed Central

2012-01-01

Background Malaria case management is a key strategy for malaria control. Effective coverage of parasite-based malaria diagnosis (PMD) remains limited in malaria endemic countries. This study assessed the health system's capacity to absorb PMD at primary health care facilities in Uganda. Methods In a cross sectional survey, using multi-stage cluster sampling, lower level health facilities (LLHF) in 11 districts in Uganda were assessed for 1) tools, 2) skills, 3) staff and infrastructure, and 4) structures, systems and roles necessary for the implementing of PMD. Results Tools for PMD (microscopy and/or RDTs) were available at 30 (24%) of the 125 LLHF. All LLHF had patient registers and 15% had functional in-patient facilities. Three months’ long stock-out periods were reported for oral and parenteral quinine at 39% and 47% of LLHF respectively. Out of 131 health workers interviewed, 86 (66%) were nursing assistants; 56 (43%) had received on-job training on malaria case management and 47 (36%) had adequate knowledge in malaria case management. Overall, only 18% (131/730) Ministry of Health approved staff positions were filled by qualified personnel and 12% were recruited or transferred within six months preceding the survey. Of 186 patients that received referrals from LLHF, 130(70%) had received pre-referral anti-malarial drugs, none received pre-referral rectal artesunate and 35% had been referred due to poor response to antimalarial drugs. Conclusion Primary health care facilities had inadequate human and infrastructural capacity to effectively implement universal parasite-based malaria diagnosis. The priority capacity building needs identified were: 1) recruitment and retention of qualified staff, 2) comprehensive training of health workers in fever management, 3) malaria diagnosis quality control systems and 4) strengthening of supply chain, stock management and referral systems. PMID:22920954

Connection method of separated luminal regions of intestine from CT volumes

NASA Astrophysics Data System (ADS)

Oda, Masahiro; Kitasaka, Takayuki; Furukawa, Kazuhiro; Watanabe, Osamu; Ando, Takafumi; Hirooka, Yoshiki; Goto, Hidemi; Mori, Kensaku

2015-03-01

This paper proposes a connection method of separated luminal regions of the intestine for Crohn's disease diagnosis. Crohn's disease is an inflammatory disease of the digestive tract. Capsule or conventional endoscopic diagnosis is performed for Crohn's disease diagnosis. However, parts of the intestines may not be observed in the endoscopic diagnosis if intestinal stenosis occurs. Endoscopes cannot pass through the stenosed parts. CT image-based diagnosis is developed as an alternative choice of the Crohn's disease. CT image-based diagnosis enables physicians to observe the entire intestines even if stenosed parts exist. CAD systems for Crohn's disease using CT volumes are recently developed. Such CAD systems need to reconstruct separated luminal regions of the intestines to analyze intestines. We propose a connection method of separated luminal regions of the intestines segmented from CT volumes. The luminal regions of the intestines are segmented from a CT volume. The centerlines of the luminal regions are calculated by using a thinning process. We enumerate all the possible sequences of the centerline segments. In this work, we newly introduce a condition using distance between connected ends points of the centerline segments. This condition eliminates unnatural connections of the centerline segments. Also, this condition reduces processing time. After generating a sequence list of the centerline segments, the correct sequence is obtained by using an evaluation function. We connect the luminal regions based on the correct sequence. Our experiments using four CT volumes showed that our method connected 6.5 out of 8.0 centerline segments per case. Processing times of the proposed method were reduced from the previous method.

Explanation-aware computing of the prognosis for breast cancer supported by IK-DCBRC: Technical innovation.

PubMed

Khelassi, Abdeldjalil

2014-01-01

Active research is being conducted to determine the prognosis for breast cancer. However, the uncertainty is a major obstacle in this domain of medical research. In that context, explanation-aware computing has the potential for providing meaningful interactions between complex medical applications and users, which would ensure a significant reduction of uncertainty and risks. This paper presents an explanation-aware agent, supported by Intensive Knowledge-Distributed Case-Based Reasoning Classifier (IK-DCBRC), to reduce the uncertainty and risks associated with the diagnosis of breast cancer. A meaningful explanation is generated by inferring from a rule-based system according to the level of abstraction and the reasoning traces. The computer-aided detection is conducted by IK-DCBRC, which is a multi-agent system that applies the case-based reasoning paradigm and a fuzzy similarity function for the automatic prognosis by the class of breast tumors, i.e. malignant or benign, from a pattern of cytological images. A meaningful interaction between the physician and the computer-aided diagnosis system, IK-DCBRC, is achieved via an intelligent agent. The physician can observe the trace of reasoning, terms, justifications, and the strategy to be used to decrease the risks and doubts associated with the automatic diagnosis. The capability of the system we have developed was proven by an example in which conflicts were clarified and transparency was ensured. The explanation agent ensures the transparency of the automatic diagnosis of breast cancer supported by IK-DCBRC, which decreases uncertainty and risks and detects some conflicts.

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.

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.

Methods of formation of the knowledge base in the diagnosis of melanoma

NASA Astrophysics Data System (ADS)

Selchuk, V. Y.; Rodionova, O. V.; Sukhova, O. G.; Polyakov, E. V.; Grebennikova, O. P.; Burov, D. A.; Emelianova, G. S.

2017-01-01

The method of building of information systems for the diagnosis of skin melanoma is described in the presented work. Malignant tumors at the level of macro - and microimages in combination with clinical data are investigated. The development is made with the use of MySQL. An information system is a result of joint activities of the National research nuclear University “MEPhI” (Moscow Engineering Physics Institute) with N. N. Blokhin Russian Cancer Scientific Center.

Research of test fault diagnosis method for micro-satellite PSS

NASA Astrophysics Data System (ADS)

Wu, Haichao; Wang, Jinqi; Yang, Zhi; Yan, Meizhi

2017-11-01

Along with the increase in the number of micro-satellite and the shortening of the product's lifecycle, negative effects of satellite ground test failure become more and more serious. Real-time and efficient fault diagnosis becomes more and more necessary. PSS plays an important role in the satellite ground test's safety and reliability as one of the most important subsystems that guarantees the safety of micro-satellite energy. Take test fault diagnosis method of micro-satellite PSS as research object. On the basis of system features of PSS and classic fault diagnosis methods, propose a kind of fault diagnosis method based on the layered and loose coupling way. This article can provide certain reference for fault diagnosis methods research of other subsystems of micro-satellite.

Expert systems for fault diagnosis in nuclear reactor control

NASA Astrophysics Data System (ADS)

Jalel, N. A.; Nicholson, H.

1990-11-01

An expert system for accident analysis and fault diagnosis for the Loss Of Fluid Test (LOFT) reactor, a small scale pressurized water reactor, was developed for a personal computer. The knowledge of the system is presented using a production rule approach with a backward chaining inference engine. The data base of the system includes simulated dependent state variables of the LOFT reactor model. Another system is designed to assist the operator in choosing the appropriate cooling mode and to diagnose the fault in the selected cooling system. The response tree, which is used to provide the link between a list of very specific accident sequences and a set of generic emergency procedures which help the operator in monitoring system status, and to differentiate between different accident sequences and select the correct procedures, is used to build the system knowledge base. Both systems are written in TURBO PROLOG language and can be run on an IBM PC compatible with 640k RAM, 40 Mbyte hard disk and color graphics.

Fetal alcohol spectrum disorder: Canadian guidelines for diagnosis

PubMed Central

Chudley, Albert E.; Conry, Julianne; Cook, Jocelynn L.; Loock, Christine; Rosales, Ted; LeBlanc, Nicole

2005-01-01

THE DIAGNOSIS OF FETAL ALCOHOL SPECTRUM DISORDER (FASD) is complex and guidelines are warranted. A subcommittee of the Public Health Agency of Canada's National Advisory Committee on Fetal Alcohol Spectrum Disorder reviewed, analysed and integrated current approaches to diagnosis to reach agreement on a standard in Canada. The purpose of this paper is to review and clarify the use of current diagnostic systems and make recommendations on their application for diagnosis of FASD-related disabilities in people of all ages. The guidelines are based on widespread consultation of expert practitioners and partners in the field. The guidelines have been organized into 7 categories: screening and referral; the physical examination and differential diagnosis; the neurobehavioural assessment; and treatment and follow-up; maternal alcohol history in pregnancy; diagnostic criteria for fetal alcohol syndrome (FAS), partial FAS and alcohol-related neurodevelopmental disorder; and harmonization of Institute of Medicine and 4-Digit Diagnostic Code approaches. The diagnosis requires a comprehensive history and physical and neurobehavioural assessments; a multidisciplinary approach is necessary. These are the first Canadian guidelines for the diagnosis of FAS and its related disabilities, developed by broad-based consultation among experts in diagnosis. PMID:15738468

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil

2016-01-01

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

PubMed Central

Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

2018-01-01

The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. PMID:29401730

Collaboration Between NASA Centers of Excellence on Autonomous System Software Development

NASA Technical Reports Server (NTRS)

Goodrich, Charles H.; Larson, William E.; Delgado, H. (Technical Monitor)

2001-01-01

Software for space systems flight operations has its roots in the early days of the space program when computer systems were incapable of supporting highly complex and flexible control logic. Control systems relied on fast data acquisition and supervisory control from a roomful of systems engineers on the ground. Even though computer hardware and software has become many orders of magnitude more capable, space systems have largely adhered to this original paradigm In an effort to break this mold, Kennedy Space Center (KSC) has invested in the development of model-based diagnosis and control applications for ten years having broad experience in both ground and spacecraft systems and software. KSC has now partnered with Ames Research Center (ARC), NASA's Center of Excellence in Information Technology, to create a new paradigm for the control of dynamic space systems. ARC has developed model-based diagnosis and intelligent planning software that enables spacecraft to handle most routine problems automatically and allocate resources in a flexible way to realize mission objectives. ARC demonstrated the utility of onboard diagnosis and planning with an experiment aboard Deep Space I in 1999. This paper highlights the software control system collaboration between KSC and ARC. KSC has developed a Mars In-situ Resource Utilization testbed based on the Reverse Water Gas Shift (RWGS) reaction. This plant, built in KSC's Applied Chemistry Laboratory, is capable of producing the large amount of Oxygen that would be needed to support a Human Mars Mission. KSC and ARC are cooperating to develop an autonomous, fault-tolerant control system for RWGS to meet the need for autonomy on deep space missions. The paper will also describe how the new system software paradigm will be applied to Vehicle Health Monitoring, tested on the new X vehicles and integrated into future launch processing systems.

A distributed fault-detection and diagnosis system using on-line parameter estimation

NASA Technical Reports Server (NTRS)

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

1991-01-01

The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

Medical diagnosis using adaptive perceptive particle swarm optimization and its hardware realization using field programmable gate array.

PubMed

Chowdhury, Shubhajit Roy; Chakrabarti, Dipankar; Hiranmay, Saha

2009-12-01

The paper proposes to develop a field programmable gate array (FPGA) based low cost, low power and high speed novel diagnostic system that can detect in absence of the physician the approaching critical condition of a patient at an early stage and is thus suitable for diagnosis of patients in the rural areas of developing countries where availability of physicians and availability of power is really scarce. The diagnostic system could be installed in health care centres of rural areas where patients can register themselves for periodic diagnoses and thereby detect potential health hazards at an early stage. Multiple pathophysiological parameters with different weights are involved in diagnosing a particular disease. A novel variation of particle swarm optimization called as adaptive perceptive particle swarm optimization has been proposed to determine the optimal weights of these pathophysiological parameters for a more accurate diagnosis. The FPGA based smart system has been applied for early detection of renal criticality of patients. For renal diagnosis, body mass index, glucose, urea, creatinine, systolic and diastolic blood pressures have been considered as pathophysiological parameters. The detection of approaching critical condition of a patient by the instrument has also been validated with the standard Cockford Gault Equation to verify whether the patient is really approaching a critical condition or not. Using Bayesian analysis on the population of 80 patients under study an accuracy of up to 97.5% in renal diagnosis has been obtained.

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

Acute rheumatic fever and rheumatic heart disease--priorities in prevention, diagnosis and management. A report of the CSANZ Indigenous Cardiovascular Health Conference, Alice Springs 2011.

PubMed

Rémond, M G W; Wheaton, G R; Walsh, W F; Prior, D L; Maguire, G P

2012-10-01

Three priority areas in the prevention, diagnosis and management of acute rheumatic fever (ARF) and rheumatic heart disease (RHD) were identified and discussed in detail: 1. Echocardiography and screening/diagnosis of RHD – Given the existing uncertainty it remains premature to advocate for or to incorporate echocardiographic screening for RHD into Australian clinical practice. Further research is currently being undertaken to evaluate the potential for echocardiography screening. 2. Secondary prophylaxis – Secondary prophylaxis (long acting benzathine penicillin injections) must be seen as a priority. Systems-based approaches are necessary with a focus on the development and evaluation of primary health care-based or led strategies incorporating effective health information management systems. Better/novel systems of delivery of prophylactic medications should be investigated. 3. Management of advanced RHD – National centres of excellence for the diagnosis, assessment and surgical management of RHD are required. Early referral for surgical input is necessary with multidisciplinary care and team-based decision making that includes patient, family, and local health providers. There is a need for a national RHD surgical register and research strategy for the assessment, intervention and long-term outcome of surgery and other interventions for RHD. Copyright © 2012 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

A Clinical Approach to the Diagnosis of Acid-Base Disorders

PubMed Central

Bear, Robert A.

1986-01-01

The ability to diagnose and manage acid-base disorders rapidly and effectively is essential to the care of critically ill patients. This article presents an approach to the diagnosis of pure and mixed acid-base disorders, metabolic or respiratory. The approach taken is based on using the law of mass-action equation as it applies to the bicarbonate buffer system (Henderson equation), using sub-classifications for diagnostic purposes of causes of metabolic acidosis and metabolic alkalosis, and using a knowledge of the well-defined and predictable compensatory responses that attempt to limit the change in pH in each of the primary acid-base disorders. PMID:21267134

Development and clinical study of mobile 12-lead electrocardiography based on cloud computing for cardiac emergency.

PubMed

Fujita, Hideo; Uchimura, Yuji; Waki, Kayo; Omae, Koji; Takeuchi, Ichiro; Ohe, Kazuhiko

2013-01-01

To improve emergency services for accurate diagnosis of cardiac emergency, we developed a low-cost new mobile electrocardiography system "Cloud Cardiology®" based upon cloud computing for prehospital diagnosis. This comprises a compact 12-lead ECG unit equipped with Bluetooth and Android Smartphone with an application for transmission. Cloud server enables us to share ECG simultaneously inside and outside the hospital. We evaluated the clinical effectiveness by conducting a clinical trial with historical comparison to evaluate this system in a rapid response car in the real emergency service settings. We found that this system has an ability to shorten the onset to balloon time of patients with acute myocardial infarction, resulting in better clinical outcome. Here we propose that cloud-computing based simultaneous data sharing could be powerful solution for emergency service for cardiology, along with its significant clinical outcome.

[Application of Mass Spectrometry to the Diagnosis of Cancer--Chairman's Introductory Remarks].

PubMed

Yatomi, Yutaka

2015-09-01

In this symposium, the latest application of mass spectrometry to laboratory medicine, i.e., to the early diagnosis of cancer, was introduced. Dr. Masaru YOSHIDA, who has been using metabolome analysis to discover biomarker candidates for gastroenterological diseases, presented an automated early diagnosis system for early stages of colon cancer based on metabolome analysis and using a minute amount of blood. On the other hand, Dr. Sen TAKEDA, who has developed a new approach by employing both mass spectrometry and machine-learning for cancer diagnosis, presented a device for the clinical diagnosis of cancer using probe electrospray ionization (PESI) and machine-learning called the dual penalized logistic regression machine (dPLRM).

Proceedings of the 1984 IEEE international conference on systems, man and cybernetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1984-01-01

This conference contains papers on artificial intelligence, pattern recognition, and man-machine systems. Topics considered include concurrent minimization, a robot programming system, system modeling and simulation, camera calibration, thermal power plants, image processing, fault diagnosis, knowledge-based systems, power systems, hydroelectric power plants, expert systems, and electrical transients.

Intelligent, Self-Diagnostic Thermal Protection System for Future Spacecraft

NASA Technical Reports Server (NTRS)

Hyers, Robert W.; SanSoucie, Michael P.; Pepyne, David; Hanlon, Alaina B.; Deshmukh, Abhijit

2005-01-01

The goal of this project is to provide self-diagnostic capabilities to the thermal protection systems (TPS) of future spacecraft. Self-diagnosis is especially important in thermal protection systems (TPS), where large numbers of parts must survive extreme conditions after weeks or years in space. In-service inspections of these systems are difficult or impossible, yet their reliability must be ensured before atmospheric entry. In fact, TPS represents the greatest risk factor after propulsion for any transatmospheric mission. The concepts and much of the technology would be applicable not only to the Crew Exploration Vehicle (CEV), but also to ablative thermal protection for aerocapture and planetary exploration. Monitoring a thermal protection system on a Shuttle-sized vehicle is a daunting task: there are more than 26,000 components whose integrity must be verified with very low rates of both missed faults and false positives. The large number of monitored components precludes conventional approaches based on centralized data collection over separate wires; a distributed approach is necessary to limit the power, mass, and volume of the health monitoring system. Distributed intelligence with self-diagnosis further improves capability, scalability, robustness, and reliability of the monitoring subsystem. A distributed system of intelligent sensors can provide an assurance of the integrity of the system, diagnosis of faults, and condition-based maintenance, all with provable bounds on errors.

From Fault-Diagnosis and Performance Recovery of a Controlled System to Chaotic Secure Communication

NASA Astrophysics Data System (ADS)

Hsu, Wen-Teng; Tsai, Jason Sheng-Hong; Guo, Fang-Cheng; Guo, Shu-Mei; Shieh, Leang-San

Chaotic systems are often applied to encryption on secure communication, but they may not provide high-degree security. In order to improve the security of communication, chaotic systems may need to add other secure signals, but this may cause the system to diverge. In this paper, we redesign a communication scheme that could create secure communication with additional secure signals, and the proposed scheme could keep system convergence. First, we introduce the universal state-space adaptive observer-based fault diagnosis/estimator and the high-performance tracker for the sampled-data linear time-varying system with unanticipated decay factors in actuators/system states. Besides, robustness, convergence in the mean, and tracking ability are given in this paper. A residual generation scheme and a mechanism for auto-tuning switched gain is also presented, so that the introduced methodology is applicable for the fault detection and diagnosis (FDD) for actuator and state faults to yield a high tracking performance recovery. The evolutionary programming-based adaptive observer is then applied to the problem of secure communication. Whenever the tracker induces a large control input which might not conform to the input constraint of some physical systems, the proposed modified linear quadratic optimal tracker (LQT) can effectively restrict the control input within the specified constraint interval, under the acceptable tracking performance. The effectiveness of the proposed design methodology is illustrated through tracking control simulation examples.

Development of smart piezoelectric transducer self-sensing, self-diagnosis and tuning schemes for structural health monitoring applications

NASA Astrophysics Data System (ADS)

Lee, Sang Jun

Autonomous structural health monitoring (SHM) systems using active sensing devices have been studied extensively to diagnose the current state of aerospace, civil infrastructure and mechanical systems in near real-time and aims to eventually reduce life-cycle costs by replacing current schedule-based maintenance with condition-based maintenance. This research develops four schemes for SHM applications: (1) a simple and reliable PZT transducer self-sensing scheme; (2) a smart PZT self-diagnosis scheme; (3) an instantaneous reciprocity-based PZT diagnosis scheme; and (4) an effective PZT transducer tuning scheme. First, this research develops a PZT transducer self-sensing scheme, which is a necessary condition to accomplish a PZT transducer self-diagnosis. Main advantages of the proposed self-sensing approach are its simplicity and adaptability. The necessary hardware is only an additional self-sensing circuit which includes a minimum of electric components. With this circuit, the self-sensing parameters can be calibrated instantaneously in the presence of changing operational and environmental conditions of the system. In particular, this self-sensing scheme focuses on estimating the mechanical response in the time domain for the subsequent applications of the PZT transducer self-diagnosis and tuning with guided wave propagation. The most significant challenge of this self-sensing comes from the fact that the magnitude of the mechanical response is generally several orders of magnitude smaller than that of the input signal. The proposed self-sensing scheme fully takes advantage of the fact that any user-defined input signals can be applied to a host structure and the input waveform is known. The performance of the proposed self-sensing scheme is demonstrated by theoretical analysis, numerical simulations and various experiments. Second, this research proposes a smart PZT transducer self-diagnosis scheme based on the developed self-sensing scheme. Conventionally, the capacitance change of the PZT wafer is monitored to identify the abnormal PZT condition because the capacitance of the PZT wafer is linearly proportional to its size and also related to the bonding condition. However, temperature variation is another primary factor that affects the PZT capacitance. To ensure the reliable transducer self-diagnosis, two different self-diagnosis features are proposed to differentiate two main PZT wafer defects, i.e., PZT debonding and PZT cracking, from temperature variations and structural damages. The PZT debonding is identified using two indices based on time reversal process (TRP) without any baseline data. Also, the PZT cracking is identified by monitoring the change of the generated Lamb wave power ratio index with respect to the driving frequency. The uniqueness of this self-diagnosis scheme is that the self-diagnosis features can differentiate the PZT defects from environmental variations and structural damages. Therefore, it is expected to minimize false-alarms which are induced by operational or environmental variations as well as structural damages. The applicability of the proposed self-diagnosis scheme is verified by theoretical analysis, numerical simulations, and experimental tests. Third, a new methodology of guided wave-based PZT transducer diagnosis is developed to identify PZT transducer defects without using prior baseline data. This methodology can be applied when a number of same-size PZT transducers are attached to a target structure to form a sensor network. The advantage of the proposed technique is that abnormal PZT transducers among intact PZT transducers can be detected even when the system being monitored is subjected to varying operational and environmental conditions or changing structural conditions. To achieve this goal, the proposed diagnosis technique utilizes the linear reciprocity of guided wave propagation between a pair of surface-bonded PZT transducers. Finally, a PZT transducer tuning scheme is being developed for selective Lamb wave excitation and sensing. This is useful for structural damage detection based on Lamb wave propagation because the proper transducer size and the corresponding input frequency can be is crucial for selective Lamb wave excitation and sensing. The circular PZT response model is derived, and the energy balance is included for a better prediction of the PZT responses because the existing PZT response models do not consider any energy balance between Lamb wave modes. In addition, two calibration methods are also suggested in order to model the PZT responses more accurately by considering a bonding layer effect. (Abstract shortened by UMI.)

Knowledge assisted diagnosis of mood disorders using DSM-3

NASA Technical Reports Server (NTRS)

Fritz, Robert H.

1990-01-01

As part of an Expert Systems class at the University of Houston Clear Lake, a system has been developed using CLIPS to allow a clinical psychologist or psychiatrist to diagnose mood disturbances by providing answers to questions corresponding to branches of a DSM-III criteria tree. Experienced clinicians may assert indications of the client's behavior in order to circumvent multiple levels of the tree, thus speeding diagnosis. An explanation facility was developed for validation of the diagnosis . It also allows for 'what if' scenarios by allowing the clinician to move backwards from the diagnosis to any decision branch and alter the answer previously provided. The system was implemented with a limited vocabulary of symptoms associated primarily with depressive disorders. However, the design supports the addition of vocabulary modules and knowledge bases for other types of disorders. The system currently has applicability in an instructional setting. With the addition of a more complete vocabulary, it could have applicability in a clinical setting. The overall design will support any application where determinations are made via a decision tree.

The systemic inflammatory response syndrome (SIRS) to identify infected patients in the emergency room.

PubMed

Jaimes, Fabián; Garcés, Jenny; Cuervo, Jorge; Ramírez, Federico; Ramírez, Jorge; Vargas, Andrea; Quintero, Claudia; Ochoa, Jorge; Tandioy, Fabio; Zapata, Láder; Estrada, Juan; Yepes, Maria; Leal, Hiulber

2003-08-01

Evaluation of the usefulness of criteria for systemic inflammatory response syndrome (SIRS) compared with the final diagnosis of infection in patients admitted to the emergency room of two university-based hospitals. Longitudinal cohort study. Hospital Universitario San Vicente de Paul and Hospital General de Medellín, Medellín, Colombia. PATIENTS. Seven hundred thirty-four patients with suspected infection as main diagnosis for admittance into the emergency room. Sensitivity, specificity, predictive values and likelihood ratios (LR) of SIRS criteria at admission were determined using, as gold standards, the diagnosis at the time of discharge based on clinical history and evolution, and microbiological confirmation of infection. SIRS criteria were met by 503 patients (68.5%); the discharge diagnosis of infection was found in 657 (89.4%) and 276 (37%) had microbiological confirmation. SIRS criteria exhibited a sensitivity of 69%, specificity of 35%, positive predictive value (PPV) of 90%, negative predictive value (NPV) of 12% and positive LR of 1.06. There were no differences between the two gold standards. The finding of two or more SIRS criteria was of little usefulness for diagnosis of infection. It is necessary to work with new criteria and probably with biological markers, in order to obtain a simple, precise and operative definition of the sepsis phenomenon.

Enhancements in medicine by integrating content based image retrieval in computer-aided diagnosis

NASA Astrophysics Data System (ADS)

Aggarwal, Preeti; Sardana, H. K.

2010-02-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. With cad, radiologists use the computer output as a "second opinion" and make the final decisions. Retrieving images is a useful tool to help radiologist to check medical image and diagnosis. The impact of contentbased access to medical images is frequently reported but existing systems are designed for only a particular context of diagnosis. The challenge in medical informatics is to develop tools for analyzing the content of medical images and to represent them in a way that can be efficiently searched and compared by the physicians. CAD is a concept established by taking into account equally the roles of physicians and computers. To build a successful computer aided diagnostic system, all the relevant technologies, especially retrieval need to be integrated in such a manner that should provide effective and efficient pre-diagnosed cases with proven pathology for the current case at the right time. In this paper, it is suggested that integration of content-based image retrieval (CBIR) in cad can bring enormous results in medicine especially in diagnosis. This approach is also compared with other approaches by highlighting its advantages over those approaches.

Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel

2004-01-01

Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.

Fault Detection and Diagnosis System for the Air-conditioning

NASA Astrophysics Data System (ADS)

Nakahara, Nobuo

The fault detection and diagnosis system, the FDD system, for the HVAC was initiated around the middle of 1970s in Japan but it still remains at the elementary stage. The HVAC is really one of the most complicated and large scaled system for the FDD system. Besides, the maintenance engineering was never focussed as the target of the academic study since after the war, but the FDD system for some kinds of the components and subsystems has been developed for the sake of the practical industrial needs. Recently, international cooperative study in the IEA Annex 25 on the energy conservation for the building and community targetted on the BOFD, the building optimization, fault detection and diagnosis. Not a few academic peaple from various engineering field got interested and, moreover, some national projects seem to start in the European countries. The author has reviewed the state of the art of the FDD and BO as well based on the references and the experience at the IEA study.

Lyme disease: review.

PubMed

Biesiada, Grażyna; Czepiel, Jacek; Leśniak, Maciej R; Garlicki, Aleksander; Mach, Tomasz

2012-12-20

Lyme disease is a multi-organ animal-borne disease, caused by spirochetes of Borrelia burgdorferi (Bb), which typically affect the skin, nervous system, musculoskeletal system and heart. A history of confirmed exposure to tick bites, typical signs and symptoms of Lyme borreliosis and positive tests for anti-Bb antibodies, are the basis of a diagnosis. A two-step diagnosis is necessary: the first step is based on a high sensitivity ELISA test with positive results confirmed by a more specific Western blot assay. Antibiotic therapy is curative in most cases, but some patients develop chronic symptoms, which do not respond to antibiotics. The aim of this review is to summarize our current knowledge of the symptoms, clinical diagnosis and treatment of Lyme borreliosis.

Taking cognizance of mental illness in schizophrenics and its association with crime and substance-related diagnoses.

PubMed

Munkner, R; Haastrup, S; Jørgensen, T; Andreasen, A H; Kramp, P

2003-02-01

To analyse how committed crimes and substance-related diagnoses are associated with the age on the first contact with the psychiatric hospital system and the age at diagnosing of schizophrenia among schizophrenics. In a register-based study including all Danes diagnosed with schizophrenia born after November 1, 1963, data on criminality, substance-related diagnoses and contacts with the psychiatric hospital system were analysed. Compared with the non-convicted schizophrenics the convicted were older on first contact with the psychiatric hospital system and older when the diagnosis of schizophrenia was first given. In contrast, having a substance-related diagnosis was associated with a younger age on first contact but did not influence the age at which the diagnosis of schizophrenia was given. It is important that both psychiatrists and the judicial system are aware of possible psychotic symptoms in criminal and abusing individuals to enable earlier detection and treatment.

Minimal ensemble based on subset selection using ECG to diagnose categories of CAN.

PubMed

Abawajy, Jemal; Kelarev, Andrei; Yi, Xun; Jelinek, Herbert F

2018-07-01

Early diagnosis of cardiac autonomic neuropathy (CAN) is critical for reversing or decreasing its progression and prevent complications. Diagnostic accuracy or precision is one of the core requirements of CAN detection. As the standard Ewing battery tests suffer from a number of shortcomings, research in automating and improving the early detection of CAN has recently received serious attention in identifying additional clinical variables and designing advanced ensembles of classifiers to improve the accuracy or precision of CAN diagnostics. Although large ensembles are commonly proposed for the automated diagnosis of CAN, large ensembles are characterized by slow processing speed and computational complexity. This paper applies ECG features and proposes a new ensemble-based approach for diagnosis of CAN progression. We introduce a Minimal Ensemble Based On Subset Selection (MEBOSS) for the diagnosis of all categories of CAN including early, definite and atypical CAN. MEBOSS is based on a novel multi-tier architecture applying classifier subset selection as well as the training subset selection during several steps of its operation. Our experiments determined the diagnostic accuracy or precision obtained in 5 × 2 cross-validation for various options employed in MEBOSS and other classification systems. The experiments demonstrate the operation of the MEBOSS procedure invoking the most effective classifiers available in the open source software environment SageMath. The results of our experiments show that for the large DiabHealth database of CAN related parameters MEBOSS outperformed other classification systems available in SageMath and achieved 94% to 97% precision in 5 × 2 cross-validation correctly distinguishing any two CAN categories to a maximum of five categorizations including control, early, definite, severe and atypical CAN. These results show that MEBOSS architecture is effective and can be recommended for practical implementations in systems for the diagnosis of CAN progression. Copyright © 2018 Elsevier B.V. All rights reserved.

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

The groningen laryngomalacia classification system--based on systematic review and dynamic airway changes.

PubMed

van der Heijden, Martijn; Dikkers, Frederik G; Halmos, Gyorgy B

2015-12-01

Laryngomalacia is the most common cause of dyspnea and stridor in newborn infants. Laryngomalacia is a dynamic change of the upper airway based on abnormally pliable supraglottic structures, which causes upper airway obstruction. In the past, different classification systems have been introduced. Until now no classification system is widely accepted and applied. Our goal is to provide a simple and complete classification system based on systematic literature search and our experiences. Retrospective cohort study with literature review. All patients with laryngomalacia under the age of 5 at time of diagnosis were included. Photo and video documentation was used to confirm diagnosis and characteristics of dynamic airway change. Outcome was compared with available classification systems in literature. Eighty-five patients were included. In contrast to other classification systems, only three typical different dynamic changes have been identified in our series. Two existing classification systems covered 100% of our findings, but there was an unnecessary overlap between different types in most of the systems. Based on our finding, we propose a new a classification system for laryngomalacia, which is purely based on dynamic airway changes. The groningen laryngomalacia classification is a new, simplified classification system with three types, based on purely dynamic laryngeal changes, tested in a tertiary referral center: Type 1: inward collapse of arytenoids cartilages, Type 2: medial displacement of aryepiglottic folds, and Type 3: posterocaudal displacement of epiglottis against the posterior pharyngeal wall. © 2015 Wiley Periodicals, Inc.

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 whose consistency can be tracked across multiple time steps.

"DSM IV," "DSM-5," and the Five-Factor Model: the Diagnosis of Personality Disorder with Intellectual and Developmental Disabilities

ERIC Educational Resources Information Center

Lindsay, William R.; Steptoe, Lesley; McVicker, Ronnie; Haut, Fabian; Robertson, Colette

2018-01-01

In "DSM-5" there has been a move to dimensional personality disorder (PD) diagnosis, incorporating personality theory in the form of the five-factor model (FFM). It proposes an alternative assessment system based on diagnostic indicators and the FFM, while retaining "DSM-IV" categorical criteria. Four individuals with…

Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aykac, Deniz; Chaum, Edward; Fox, Karen

A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection,more » and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.« less

Systemic mastocytosis: A rare cause of non-cirrhotic portal hypertension.

PubMed

Martins, Cláudio; Teixeira, Cristina; Ribeiro, Suzane; Trabulo, Daniel; Cardoso, Cláudia; Mangualde, João; Freire, Ricardo; Gamito, Élia; Alves, Ana Luísa; Cremers, Isabelle; Alves, Cecília; Neves, Anabela; Oliveira, Ana Paula

2016-07-28

Mastocytosis is a clonal neoplastic disorder of the mast cells (MC) that can be limited to the skin (cutaneous mastocytosis) or involve one or more extracutaneous organs (systemic mastocytosis). The clinical manifestations of mastocytosis are heterogeneous ranging from indolent disease with a long-term survival to a highly aggressive neoplasm with survival of about 6 mo. Although liver involvement in aggressive systemic mastocytosis (ASM) is relatively common, the development of portal hypertension with or without cirrhosis is rare. We report a case of ASM without skin involvement in a 72-year-old caucasian male who presented with non-cirrhotic portal hypertension based on clinical, analytical, imagiological and endoscopic findings. Given the hematological picture, the correct diagnosis was established based on ancillary tests for MC using bone marrow aspirates and biopsy. Extensive involvement of the liver and gastrointestinal tract was histologically documented. The disease progressed rapidly and severe pancytopenia and recurrent upper gastrointestinal bleeding became the dominant problem. This case illustrates the challenge in establishing a diagnosis of ASM especially when the clinical picture is atypical and without skin involvement. Gastroenterologists should consider infiltrative disease, particularly systemic mastocytosis, as a differential diagnosis in a clinical case of portal hypertension of unknown etiology.

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

NASA Technical Reports Server (NTRS)

Schumann, Johann; Rozier, Kristin Y.; Reinbacher, Thomas; Mengshoel, Ole J.; Mbaya, Timmy; Ippolito, Corey

2013-01-01

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.

Reasoning methods in medical consultation systems: artificial intelligence approaches.

PubMed

Shortliffe, E H

1984-01-01

It has been argued that the problem of medical diagnosis is fundamentally ill-structured, particularly during the early stages when the number of possible explanations for presenting complaints can be immense. This paper discusses the process of clinical hypothesis evocation, contrasts it with the structured decision making approaches used in traditional computer-based diagnostic systems, and briefly surveys the more open-ended reasoning methods that have been used in medical artificial intelligence (AI) programs. The additional complexity introduced when an advice system is designed to suggest management instead of (or in addition to) diagnosis is also emphasized. Example systems are discussed to illustrate the key concepts.

Population-based analysis of hospitalizations in a West-European region revealed major changes in hospital utilization for patients with systemic lupus erythematosus over the period 2001-2012.

PubMed

Piga, M; Casula, L; Perra, D; Sanna, S; Floris, A; Antonelli, A; Cauli, A; Mathieu, A

2016-01-01

The objective of this paper is to evaluate hospital admissions in systemic lupus erythematosus (SLE) patients through a retrospective population-based study analyzing hospitalization data during 2001-2012 in Sardinia, an Italian region with universal health system coverage. Data on the hospital discharge records with the ICD-9-CM code for SLE (710.0) were obtained from the Department of Health and Hygiene and analyzed, mostly focusing on primary and non-primary diagnosis and Diagnosis-Related Group (DRG) code. In order to establish the significance of the annual trend for number and type of primary and non-primary discharge diagnosis, the two-tailed Cochran-Armitage test for trend was applied. In order to estimate SLE prevalence, data from administrative database and medical records were assembled. This study included 6222 hospitalizations in 1675 patients (87% women). Hospitalizations with SLE as primary diagnosis were 3782 (58.0%) and significantly decreased during the study period. The annual number of renal, hematologic and neuropsychiatric disorders as non-primary diagnosis associated with SLE remained constant; however, their percentage increased (p < 0.0001) because of a declining number of admissions for SLE without associated diagnosis and without complications. Hospitalizations with SLE as non-primary diagnosis showed a significant upward trend in number and percentage of cerebrovascular accident (p = 0.0004), acute coronary syndrome (p = 0.0004) and chronic renal failure (p = 0.0003) as underlying primary diagnosis, while complications of pregnancy, labor and childbirth (p = 0.3375), malignancies (p = 0.6608) and adverse drug reactions (p = 0.2456) did not show statistically significant changes. Infections showed an increasing trend between 2001 and 2012 but did not reach statistical significance (p = 0.0304). After correction for hospitalization (93.8%) and survival (91.1%) rates calculated over the study period, the 2012 SLE prevalence in Sardinia was estimated to be 99.3 per 100,000 inhabitants. While overall hospitalizations for SLE patients declined, those for cerebrovascular accident, acute coronary syndrome and chronic renal failure as underlying primary diagnosis increased during the study period. © The Author(s) 2015.

A CMOS wireless biomolecular sensing system-on-chip based on polysilicon nanowire technology.

PubMed

Huang, C-W; Huang, Y-J; Yen, P-W; Tsai, H-H; Liao, H-H; Juang, Y-Z; Lu, S-S; Lin, C-T

2013-11-21

As developments of modern societies, an on-field and personalized diagnosis has become important for disease prevention and proper treatment. To address this need, in this work, a polysilicon nanowire (poly-Si NW) based biosensor system-on-chip (bio-SSoC) is designed and fabricated by a 0.35 μm 2-Poly-4-Metal (2P4M) complementary metal-oxide-semiconductor (CMOS) process provided by a commercialized semiconductor foundry. Because of the advantages of CMOS system-on-chip (SoC) technologies, the poly-Si NW biosensor is integrated with a chopper differential-difference amplifier (DDA) based analog-front-end (AFE), a successive approximation analog-to-digital converter (SAR ADC), and a microcontroller to have better sensing capabilities than a traditional Si NW discrete measuring system. In addition, an on-off key (OOK) wireless transceiver is also integrated to form a wireless bio-SSoC technology. This is pioneering work to harness the momentum of CMOS integrated technology into emerging bio-diagnosis technologies. This integrated technology is experimentally examined to have a label-free and low-concentration biomolecular detection for both Hepatitis B Virus DNA (10 fM) and cardiac troponin I protein (3.2 pM). Based on this work, the implemented wireless bio-SSoC has demonstrated a good biomolecular sensing characteristic and a potential for low-cost and mobile applications. As a consequence, this developed technology can be a promising candidate for on-field and personalized applications in biomedical diagnosis.

Performance improvement of the one-dot lateral flow immunoassay for aflatoxin B1 by using a smartphone-based reading system.

PubMed

Lee, Sangdae; Kim, Giyoung; Moon, Jihea

2013-04-18

This study was conducted to develop a simple, rapid, and accurate lateral flow immunoassay (LFIA) detection method for point-of-care diagnosis. The one-dot LFIA for aflatoxin B1 (AFB1) was based on the modified competitive binding format using competition between AFB1 and colloidal gold-AFB1-BSA conjugate for antibody binding sites in the test zone. A Smartphone-based reading system consisting of a Samsung Galaxy S2 Smartphone, a LFIA reader, and a Smartphone application for the image acquisition and data analysis. The detection limit of one-dot LFIA for AFB1 is 5 μg/kg. This method provided semi-quantitative analysis of AFB1 samples in the range of 5 to 1,000 μg/kg. Using combination of the one-dot LFIA and the Smartphone-based reading system, it is possible to conduct a more fast and accurate point-of-care diagnosis.

Performance Improvement of the One-Dot Lateral Flow Immunoassay for Aflatoxin B1 by Using a Smartphone-Based Reading System

PubMed Central

Lee, Sangdae; Kim, Giyoung; Moon, Jihea

2013-01-01

This study was conducted to develop a simple, rapid, and accurate lateral flow immunoassay (LFIA) detection method for point-of-care diagnosis. The one-dot LFIA for aflatoxin B1 (AFB1) was based on the modified competitive binding format using competition between AFB1 and colloidal gold-AFB1-BSA conjugate for antibody binding sites in the test zone. A Smartphone-based reading system consisting of a Samsung Galaxy S2 Smartphone, a LFIA reader, and a Smartphone application for the image acquisition and data analysis. The detection limit of one-dot LFIA for AFB1 is 5 μg/kg. This method provided semi-quantitative analysis of AFB1 samples in the range of 5 to 1,000 μg/kg. Using combination of the one-dot LFIA and the Smartphone-based reading system, it is possible to conduct a more fast and accurate point-of-care diagnosis. PMID:23598499

Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

PubMed Central

Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

2013-01-01

Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study. PMID:24351636

Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

PubMed

Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

2013-12-13

Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

Evolving rule-based systems in two medical domains using genetic programming.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

2004-11-01

To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

PubMed

Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

2007-12-15

Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

A Dynamic Security Framework for Ambient Intelligent Systems: A Smart-Home Based eHealth Application

NASA Astrophysics Data System (ADS)

Compagna, Luca; El Khoury, Paul; Massacci, Fabio; Saidane, Ayda

Providing context-dependent security services is an important challenge for ambient intelligent systems. The complexity and the unbounded nature of such systems make it difficult even for the most experienced and knowledgeable security engineers, to foresee all possible situations and interactions when developing the system. In order to solve this problem context based self- diagnosis and reconfiguration at runtime should be provided.

Multilevel semantic analysis and problem-solving in the flight domain

NASA Technical Reports Server (NTRS)

Chien, R. T.; Chen, D. C.; Ho, W. P. C.; Pan, Y. C.

1982-01-01

A computer based cockpit system which is capable of assisting the pilot in such important tasks as monitoring, diagnosis, and trend analysis was developed. The system is properly organized and is endowed with a knowledge base so that it enhances the pilot's control over the aircraft while simultaneously reducing his workload.

Individualizing Educational Strategies: An Apple Computer Managed System for the Diagnosis and Evaluation of Reading, Math and Behavior.

ERIC Educational Resources Information Center

Kern, Richard

1985-01-01

A computer-based interactive system for diagnosing academic and school behavior problems is described. Elements include criterion-referenced testing, an instructional management system, and a behavior evaluation tool developed by the author. (JW)

Space shuttle main engine definition (phase B). Volume 2: Avionics. [for space shuttle

NASA Technical Reports Server (NTRS)

1971-01-01

The advent of the space shuttle engine with its requirements for high specific impulse, long life, and low cost have dictated a combustion cycle and a closed loop control system to allow the engine components to run close to operating limits. These performance requirements, combined with the necessity for low operational costs, have placed new demands on rocket engine control, system checkout, and diagnosis technology. Based on considerations of precision environment, and compatibility with vehicle interface commands, an electronic control, makes available many functions that logically provide the information required for engine system checkout and diagnosis.

Development of coaxial ultrasonic probe for fatty liver diagnostic system using ultrasonic velocity change

NASA Astrophysics Data System (ADS)

Hori, Makoto; Yokota, Daiki; Aotani, Yuhei; Kumagai, Yuta; Wada, Kenji; Matsunaka, Toshiyuki; Morikawa, Hiroyasu; Horinaka, Hiromichi

2017-07-01

A diagnostic system for fatty liver at an early stage is needed because fatty liver is linked to metabolic syndrome. We have already proposed a fatty liver diagnosis method based on the temperature coefficient of ultrasonic velocity. In this study, we fabricated a coaxial ultrasonic probe by integrating two kinds of transducers for warming and signal detection. The diagnosis system equipped with the coaxial probe was applied to tissue-mimicking phantoms including the fat area. The fat content rates corresponding to the set rates of the phantoms were estimated by the ultrasonic velocity-change method.

Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

NASA Astrophysics Data System (ADS)

Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

2017-01-01

In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

Efficiency of optical-electronic systems: methods application for the analysis of structural changes in the process of eye grounds diagnosis

NASA Astrophysics Data System (ADS)

Saldan, Yosyp R.; Pavlov, Sergii V.; Vovkotrub, Dina V.; Saldan, Yulia Y.; Vassilenko, Valentina B.; Mazur, Nadia I.; Nikolaichuk, Daria V.; Wójcik, Waldemar; Romaniuk, Ryszard; Suleimenov, Batyrbek; Bainazarov, Ulan

2017-08-01

Process of eye tomogram obtaining by means of optical coherent tomography is studied. Stages of idiopathic macula holes formation in the process of eye grounds diagnostics are considered. Main stages of retina pathology progression are determined: Fuzzy logic units for obtaining reliable conclusions regarding the result of diagnosis are developed. By the results of theoretical and practical research system and technique of retinal macular region of the eye state analysis is developed ; application of the system, based on fuzzy logic device, improves the efficiency of eye retina complex.

Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

PubMed

Xiao, Zhitao; Zhang, Xinpeng; Geng, Lei; Zhang, Fang; Wu, Jun; Tong, Jun; Ogunbona, Philip O; Shan, Chunyan

2017-10-26

Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

PubMed Central

2010-01-01

Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available. PMID:20459613

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks.

PubMed

Andrade, Bruno B; Reis-Filho, Antonio; Barros, Austeclino M; Souza-Neto, Sebastião M; Nogueira, Lucas L; Fukutani, Kiyoshi F; Camargo, Erney P; Camargo, Luís M A; Barral, Aldina; Duarte, Angelo; Barral-Netto, Manoel

2010-05-06

Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

PubMed

Tzallas, A T; Karvelis, P S; Katsis, C D; Fotiadis, D I; Giannopoulos, S; Konitsiotis, S

2006-01-01

The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

SU-E-I-30: Image Analysis in Ultrasonography for Diagnosis of Sjoegren's Syndrome Using Dual-Tree Complex Wavelet Transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matsui, T; Ohki, M; Nakamura, T

Purpose: Sjoegren's syndrome (SS) is an autoimmune disease invading mainly salivary and lacrimal glands. Ultrasonography is used for an initial and non-invasive examination of this disease. However, the ultrasonography diagnosis tends to lack in objectivity and depends on the operator's skills. The purpose of this study is to propose a computer-aided diagnosis (CAD) system for SS based on a dual-tree complex wavelet transform (DT-CWT) and machine learning. Methods: The subjects of this study were 174 patients suspected of having SS at Nagasaki University Hospital and examined with ultrasonography of the parotid glands. Out of these patients, 77 patients were diagnosedmore » with SS by sialography. A region of interest (ROI) of 128 × 128 pixels was set within the parotid gland that was indicated by a dental radiologist. The DT-CWT was applied to the images in the ROI and every image was decomposed into 72 sub-images of the real and imaginary components in six different resolution levels and six orientations. The statistical features of the sub-image were calculated and used as data input for the support vector machine (SVM) classifier for the detection of SS. A ten-fold cross-validation was employed to verify the Resultof SVM. The accuracy of diagnosis was compared by a CAD system with a human observer performance. Results: The sensitivity, specificity, and accuracy in the detection of SS were 95%, 86%, and 91% through our CAD system respectively, while those by a human observer were 84%, 81%, and 83% respectively. Conclusion: The proposed computer-aided diagnosis system for Sjoegren's syndrome in ultrasonography based on dual-tree complex wavelet transform had a better performance than a human observer.« less

[A symptom oriented diagnostic approach to the workup of secondary forms of uveitis -- an internet based program for better and easier orientation in differential diagnosis].

PubMed

Wiehler, U; Schmidt, R; Skonetzki, S; Becker, M

2006-05-01

The long list of ophthalmologic findings and symptoms at a baseline examination of a patient with uveitis can have an impact on further laboratory workup and differential diagnosis. Based on publications and previous work, a decision tree was transformed into the expert system shell D3. A web-based Java Servlet was then programmed and published on our website (http://www.uveitiscenter.de at DiagnoseFinder). Ophthalmologic findings and symptoms of the patient can be checked with this online questionnaire, and recommendations for further laboratory tests and further care of the patient received. Retrospective analysis of 62 patients with characteristic features of secondary uveitis showed a good correlation between the actual diagnosis of the patient and the diagnosis found by the program. In 74% of cases, the correct diagnosis was found among other options. In 37%, only the correct diagnosis was found, while in 37% the correct diagnosis was found among other probable diagnoses. Not all forms of secondary uveitis were found easily by the software. This program is meant to be an easily accessible and simply employable help for the care of patients with a secondary form of uveitis. It is intended to be a support not only for residents in particular, but also for ophthalmologists in private practice who rarely treat patients with uveitis.

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.

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.

Network Approach to Disease Diagnosis

NASA Astrophysics Data System (ADS)

Sharma, Amitabh; Bashan, Amir; Barabasi, Alber-Laszlo

2014-03-01

Human diseases could be viewed as perturbations of the underlying biological system. A thorough understanding of the topological and dynamical properties of the biological system is crucial to explain the mechanisms of many complex diseases. Recently network-based approaches have provided a framework for integrating multi-dimensional biological data that results in a better understanding of the pathophysiological state of complex diseases. Here we provide a network-based framework to improve the diagnosis of complex diseases. This framework is based on the integration of transcriptomics and the interactome. We analyze the overlap between the differentially expressed (DE) genes and disease genes (DGs) based on their locations in the molecular interaction network (''interactome''). Disease genes and their protein products tend to be much more highly connected than random, hence defining a disease sub-graph (called disease module) in the interactome. DE genes, even though different from the known set of DGs, may be significantly associated with the disease when considering their closeness to the disease module in the interactome. This new network approach holds the promise to improve the diagnosis of patients who cannot be diagnosed using conventional tools. Support was provided by HL066289 and HL105339 grants from the U.S. National Institutes of Health.

Unusual presentation of a skull base mass lesion in sarcoidosis mimicking malignant neoplasm: a case report.

PubMed

Shijo, Katsunori; Moro, Nobuhiro; Sasano, Mari; Watanabe, Mitsuru; Yagasaki, Hiroshi; Takahashi, Shori; Homma, Taku; Yoshino, Atsuo

2018-05-29

Sarcoidosis is a multi-organ disease of unknown etiology characterised by the presence of epithelioid granulomas, without caseous necrosis. Systemic sarcoidosis is rare among children, while neurosarcoidosis in children is even rarer whether it is systemic or not. We described the case of a 12-year-old boy who presented with monocular vision loss accompanied by unusual MRI features of an extensive meningeal infiltrating mass lesion. The patient underwent surgical resection (biopsy) via a frontotemporal craniotomy to establish a definitive diagnosis based on the histopathology, since neurosarcoidosis remains a very difficult diagnosis to establish from neuroradiogenic imagings. Based on the histopathology of the resected mass lesion, neurosarcoidosis was diagnosed. On follow-up after 3 months of steroid therapy, the patient displayed a good response on the imaging studies. MRI revealed that the preexisting mass lesion had regressed extremely. We also conducted a small literature review on imaging studies, manifestations, appropriate treatments, etc., in particular neurosarcoidosis including children. Although extremely rare, neurosarcoidosis, even in children, should be considered in the differential diagnosis of skull base mass lesions to avoid unnecessary aggressive surgery and delay in treatment, since surgery may have little role in the treatment of sarcoidosis.

Clinical diagnosis and typing of systemic amyloidosis in subcutaneous fat aspirates by mass spectrometry-based proteomics

PubMed Central

Vrana, Julie A.; Theis, Jason D.; Dasari, Surendra; Mereuta, Oana M.; Dispenzieri, Angela; Zeldenrust, Steven R.; Gertz, Morie A.; Kurtin, Paul J.; Grogg, Karen L.; Dogan, Ahmet

2014-01-01

Examination of abdominal subcutaneous fat aspirates is a practical, sensitive and specific method for the diagnosis of systemic amyloidosis. Here we describe the development and implementation of a clinical assay using mass spectrometry-based proteomics to type amyloidosis in subcutaneous fat aspirates. First, we validated the assay comparing amyloid-positive (n=43) and -negative (n=26) subcutaneous fat aspirates. The assay classified amyloidosis with 88% sensitivity and 96% specificity. We then implemented the assay as a clinical test, and analyzed 366 amyloid-positive subcutaneous fat aspirates in a 4-year period as part of routine clinical care. The assay had a sensitivity of 90%, and diverse amyloid types, including immunoglobulin light chain (74%), transthyretin (13%), serum amyloid A (%1), gelsolin (1%), and lysozyme (1%), were identified. Using bioinformatics, we identified a universal amyloid proteome signature, which has high sensitivity and specificity for amyloidosis similar to that of Congo red staining. We curated proteome databases which included variant proteins associated with systemic amyloidosis, and identified clonotypic immunoglobulin variable gene usage in immunoglobulin light chain amyloidosis, and the variant peptides in hereditary transthyretin amyloidosis. In conclusion, mass spectrometry-based proteomic analysis of subcutaneous fat aspirates offers a powerful tool for the diagnosis and typing of systemic amyloidosis. The assay reveals the underlying pathogenesis by identifying variable gene usage in immunoglobulin light chains and the variant peptides in hereditary amyloidosis. PMID:24747948

A practical approach to the diagnosis of systemic amyloidoses.

PubMed

Fernández de Larrea, Carlos; Verga, Laura; Morbini, Patrizia; Klersy, Catherine; Lavatelli, Francesca; Foli, Andrea; Obici, Laura; Milani, Paolo; Capello, Gian Luca; Paulli, Marco; Palladini, Giovanni; Merlini, Giampaolo

2015-04-02

Accurate diagnosis of systemic amyloidosis is necessary both for assessing the prognosis and for delineating the appropriate treatment. It is based on histologic evidence of amyloid deposits and characterization of the amyloidogenic protein. We prospectively evaluated the diagnostic performance of immunoelectron microscopy (IEM) of abdominal fat aspirates from 745 consecutive patients with suspected systemic amyloidoses. All cases were extensively investigated with clinical and laboratory data, with a follow-up of at least 18 months. The 423 (56.8%) cases with confirmed systemic forms were used to estimate the diagnostic performance of IEM. Compared with Congo-red-based light microscopy, IEM was equally sensitive (75% to 80%) but significantly more specific (100% vs 80%; P < .001). In amyloid light-chain (AL) amyloidosis, κ cases were more difficult to diagnose (sensitivity 71%), whereas the analysis of abdominal aspirate was informative in only 40% of patients with transthyretin amyloidosis. We found a high prevalence (20%) of a monoclonal component in patients with non-AL amyloidosis, highlighting the risk of misdiagnosis and the need for unequivocal amyloid typing. Notably, IEM identified correctly the specific form of amyloidosis in >99% of the cases. IEM of abdominal fat aspirates is an effective tool in the routine diagnosis of systemic amyloidoses. © 2015 by The American Society of Hematology.

The case for DNA methylation based molecular profiling to improve diagnostic accuracy for central nervous system embryonal tumors (not otherwise specified) in adults.

PubMed

Halliday, Gail C; Junckerstorff, Reimar C; Bentel, Jacqueline M; Miles, Andrew; Jones, David T W; Hovestadt, Volker; Capper, David; Endersby, Raelene; Cole, Catherine H; van Hagen, Tom; Gottardo, Nicholas G

2018-01-01

Central nervous system primitive neuro-ectodermal tumors (CNS-PNETs), have recently been re-classified in the most recent 2016 WHO Classification into a standby catch all category, "CNS Embryonal Tumor, not otherwise specified" (CNS embryonal tumor, NOS) based on epigenetic, biologic and histopathologic criteria. CNS embryonal tumors (NOS) are a rare, histologically and molecularly heterogeneous group of tumors that predominantly affect children, and occasionally adults. Diagnosis of this entity continues to be challenging and the ramifications of misdiagnosis of this aggressive class of brain tumors are significant. We report the case of a 45-year-old woman who was diagnosed with a central nervous system embryonal tumor (NOS) based on immunohistochemical analysis of the patient's tumor at diagnosis. However, later genome-wide methylation profiling of the diagnostic tumor undertaken to guide treatment, revealed characteristics most consistent with IDH-mutant astrocytoma. DNA sequencing and immunohistochemistry confirmed the presence of IDH1 and ATRX mutations resulting in a revised diagnosis of high-grade small cell astrocytoma, and the implementation of a less aggressive treatment regime tailored more appropriately to the patient's tumor type. This case highlights the inadequacy of histology alone for the diagnosis of brain tumours and the utility of methylation profiling and integrated genomic analysis for the diagnostic verification of adults with suspected CNS embryonal tumor (NOS), and is consistent with the increasing realization in the field that a combined diagnostic approach based on clinical, histopathological and molecular data is required to more accurately distinguish brain tumor subtypes and inform more effective therapy. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

PubMed Central

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

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

Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

PubMed

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

ESR concept paper on value-based radiology.

PubMed

2017-10-01

The European Society of Radiology (ESR) established a Working Group on Value-Based Imaging (VBI WG) in August 2016 in response to developments in European healthcare systems in general, and the trend within radiology to move from volume- to value-based practice in particular. The value-based healthcare (VBH) concept defines "value" as health outcomes achieved for patients relative to the costs of achieving them. Within this framework, value measurements start at the beginning of therapy; the whole diagnostic process is disregarded, and is considered only if it is the cause of errors or complications. Making the case for a new, multidisciplinary organisation of healthcare delivery centred on the patient, this paper establishes the diagnosis of disease as a first outcome in the interrelated activities of the healthcare chain. Metrics are proposed for measuring the quality of radiologists' diagnoses and the various ways in which radiologists provide value to patients, other medical specialists and healthcare systems at large. The ESR strongly believes value-based radiology (VBR) is a necessary complement to existing VBH concepts. The Society is determined to establish a holistic VBR programme to help European radiologists deal with changes in the evolution from volume- to value-based evaluation of radiological activities. Main Messages • Value-based healthcare defines value as patient's outcome over costs. • The VBH framework disregards the diagnosis as an outcome. • VBH considers diagnosis only if wrong or a cause of complications. • A correct diagnosis is the first outcome that matters to patients. • Metrics to measure radiologists' impacts on patient outcomes are key. • The value provided by radiology is multifaceted, going beyond exam volumes.

Machine intelligence and autonomy for aerospace systems

NASA Technical Reports Server (NTRS)

Heer, Ewald (Editor); Lum, Henry (Editor)

1988-01-01

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

A naive Bayes algorithm for tissue origin diagnosis (TOD-Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system.

PubMed

Jiang, Weiqin; Shen, Yifei; Ding, Yongfeng; Ye, Chuyu; Zheng, Yi; Zhao, Peng; Liu, Lulu; Tong, Zhou; Zhou, Linfu; Sun, Shuo; Zhang, Xingchen; Teng, Lisong; Timko, Michael P; Fan, Longjiang; Fang, Weijia

2018-01-15

Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive Bayes algorithm (TOD-Bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and ∼1,000 feature genes were used to train and validate the TOD-Bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-Bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-Bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment. © 2017 UICC.

Dynamic MRI-based computer aided diagnostic systems for early detection of kidney transplant rejection: A survey

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Khalifa, Fahmi; Alansary, Amir; Soliman, Ahmed; Gimel'farb, Georgy; El-Baz, Ayman

2013-10-01

Early detection of renal transplant rejection is important to implement appropriate medical and immune therapy in patients with transplanted kidneys. In literature, a large number of computer-aided diagnostic (CAD) systems using different image modalities, such as ultrasound (US), magnetic resonance imaging (MRI), computed tomography (CT), and radionuclide imaging, have been proposed for early detection of kidney diseases. A typical CAD system for kidney diagnosis consists of a set of processing steps including: motion correction, segmentation of the kidney and/or its internal structures (e.g., cortex, medulla), construction of agent kinetic curves, functional parameter estimation, diagnosis, and assessment of the kidney status. In this paper, we survey the current state-of-the-art CAD systems that have been developed for kidney disease diagnosis using dynamic MRI. In addition, the paper addresses several challenges that researchers face in developing efficient, fast and reliable CAD systems for the early detection of kidney diseases.

Testing a diagnosis-related group index for skilled nursing facilities

PubMed Central

Cotterill, Philip G.

1986-01-01

Interest in case-mix measures for use in nursing home payment systems has been stimulated by the Medicare prospective payment system (PPS) for short-term acute-care hospitals. Appropriately matching payment with care needs is important to equitably compensate providers and to encourage them to admit patients who are most in need of nursing home care. The skilled nursing facility (SNF) Medicare benefit covers skilled convalescent or rehabilitative care following a hospital stay. Therefore, it might appear that diagnosis-related groups (DRG's), the basis for patient classification in PPS, could also be used for the Medicare SNF program. In this study, a DRG-based case-mix index (CMI) was developed and tested to determine how well it explains cost differences among SNF's. The results suggest that a DRG-based SNF payment system would be highly problematic. Incentives of this system would appear to discourage placement of patients who require relatively expensive care. PMID:10311674

Testing a diagnosis-related group index for skilled nursing facilities.

PubMed

Cotterill, P G

1986-01-01

Interest in case-mix measures for use in nursing home payment systems has been stimulated by the Medicare prospective payment system (PPS) for short-term acute-care hospitals. Appropriately matching payment with care needs is important to equitably compensate providers and to encourage them to admit patients who are most in need of nursing home care. The skilled nursing facility (SNF) Medicare benefit covers skilled convalescent or rehabilitative care following a hospital stay. Therefore, it might appear that diagnosis-related groups (DRG's), the basis for patient classification in PPS, could also be used for the Medicare SNF program. In this study, a DRG-based case-mix index (CMI) was developed and tested to determine how well it explains cost differences among SNF's. The results suggest that a DRG-based SNF payment system would be highly problematic. Incentives of this system would appear to discourage placement of patients who require relatively expensive care.

Health care information systems and formula-based reimbursement: an empirical study.

PubMed

Palley, M A; Conger, S

1995-01-01

Current initiatives in health care administration use formula-based approaches to reimbursement. Examples of such approaches include capitation and diagnosis related groups (DRGs). These approaches seek to contain medical costs and to facilitate managerial control over scarce health care resources. This article considers various characteristics of formula-based reimbursement, their operationalization on hospital information systems, and how these relate to hospital compliance costs.

Aircraft applications of fault detection and isolation techniques

NASA Astrophysics Data System (ADS)

Marcos Esteban, Andres

In this thesis the problems of fault detection & isolation and fault tolerant systems are studied from the perspective of LTI frequency-domain, model-based techniques. Emphasis is placed on the applicability of these LTI techniques to nonlinear models, especially to aerospace systems. Two applications of Hinfinity LTI fault diagnosis are given using an open-loop (no controller) design approach: one for the longitudinal motion of a Boeing 747-100/200 aircraft, the other for a turbofan jet engine. An algorithm formalizing a robust identification approach based on model validation ideas is also given and applied to the previous jet engine. A general linear fractional transformation formulation is given in terms of the Youla and Dual Youla parameterizations for the integrated (control and diagnosis filter) approach. This formulation provides better insight into the trade-off between the control and the diagnosis objectives. It also provides the basic groundwork towards the development of nested schemes for the integrated approach. These nested structures allow iterative improvements on the control/filter Youla parameters based on successive identification of the system uncertainty (as given by the Dual Youla parameter). The thesis concludes with an application of Hinfinity LTI techniques to the integrated design for the longitudinal motion of the previous Boeing 747-100/200 model.

Patient classification tool in home health care.

PubMed

Pavasaris, B

1989-01-01

Medicare's system of diagnosis related groups for health care cost reimbursements is inadequate for the special requirements of home health care. A visiting nurses association's patient classification tool correlates a meticulous record of professional time spent per patient with patient diagnosis and level of care, aimed at helping policymakers develop a more equitable DRG-based prospective payment formula for home care costs.

Impact of the Japanese Diagnosis Procedure Combination-based Payment System on cardiovascular medicine-related costs.

PubMed

Yasunaga, Hideo; Ide, Hiroo; Imamura, Tomoaki; Ohe, Kazuhiko

2005-09-01

In 2003, a lump-sum payment system based on Diagnosis Procedure Combinations (DPC) was introduced to 82 specific function hospitals in Japan. While the US DRG/PPS system is a "per case payment" system, the DPC based payment system adopts a "per day payment." It is generally believed that the Japanese system provides as much of an incentive as the DRG/PPS system to shorten the average length of stay (LOS). We performed an empirical analysis of the effect of LOS shortening on hospital revenue and expenditure under the DPC-based payment system, particularly in cardiovascular diseases. We also point out fundamentally controversial aspects of the current system. A total 109 cases were selected from patients hospitalized at the University of Tokyo Hospital from May to July, 2003 and classified into one of three categories: (1) cardiac catheter interventions, (2) cardiac catheter examinations, and (3) other conservative treatments. We analyzed the changes in profit per day in cases of a reduction in average LOS and an increase in the number of cases. In category (1) profit increased significantly in conjunction with reduced LOS. In category (2) profit increased only minimally. In category (3), profit increased rarely and sometimes decreased. In cases of conservative treatment, profits sometimes decreased because an increase in material costs exceeded the increase in revenue. It therefore became clear that the DPC-based payment system does not decisively provide an economic incentive to reduce LOS in cardiovascular medicine.

Lyme disease: review

PubMed Central

Czepiel, Jacek; Leśniak, Maciej R.; Garlicki, Aleksander; Mach, Tomasz

2012-01-01

Lyme disease is a multi-organ animal-borne disease, caused by spirochetes of Borrelia burgdorferi (Bb), which typically affect the skin, nervous system, musculoskeletal system and heart. A history of confirmed exposure to tick bites, typical signs and symptoms of Lyme borreliosis and positive tests for anti-Bb antibodies, are the basis of a diagnosis. A two-step diagnosis is necessary: the first step is based on a high sensitivity ELISA test with positive results confirmed by a more specific Western blot assay. Antibiotic therapy is curative in most cases, but some patients develop chronic symptoms, which do not respond to antibiotics. The aim of this review is to summarize our current knowledge of the symptoms, clinical diagnosis and treatment of Lyme borreliosis. PMID:23319969

Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis

PubMed Central

Shen, Feichen; Liu, Sijia; Wang, Yanshan; Wang, Liwei; Afzal, Naveed; Liu, Hongfang

2017-01-01

In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We hypothesize that patients’ phenotypic information available in electronic medical records (EMR) can be leveraged to accelerate disease diagnosis based on the intuition that providers need to document associated phenotypic information to support the diagnosis decision, especially for rare diseases. In this study, we proposed a collaborative filtering system enriched with natural language processing and semantic techniques to assist rare disease diagnosis based on phenotypic characterization. Specifically, we leveraged four similarity measurements with two neighborhood algorithms on 2010-2015 Mayo Clinic unstructured large patient cohort and evaluated different approaches. Preliminary results demonstrated that the use of collaborative filtering with phenotypic information is able to stratify patients with relatively similar rare diseases. PMID:29854225

Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.

PubMed

Shen, Feichen; Liu, Sijia; Wang, Yanshan; Wang, Liwei; Afzal, Naveed; Liu, Hongfang

2017-01-01

In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We hypothesize that patients' phenotypic information available in electronic medical records (EMR) can be leveraged to accelerate disease diagnosis based on the intuition that providers need to document associated phenotypic information to support the diagnosis decision, especially for rare diseases. In this study, we proposed a collaborative filtering system enriched with natural language processing and semantic techniques to assist rare disease diagnosis based on phenotypic characterization. Specifically, we leveraged four similarity measurements with two neighborhood algorithms on 2010-2015 Mayo Clinic unstructured large patient cohort and evaluated different approaches. Preliminary results demonstrated that the use of collaborative filtering with phenotypic information is able to stratify patients with relatively similar rare diseases.

Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

2018-05-01

This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

Design for testability and diagnosis at the system-level

NASA Technical Reports Server (NTRS)

Simpson, William R.; Sheppard, John W.

1993-01-01

The growing complexity of full-scale systems has surpassed the capabilities of most simulation software to provide detailed models or gate-level failure analyses. The process of system-level diagnosis approaches the fault-isolation problem in a manner that differs significantly from the traditional and exhaustive failure mode search. System-level diagnosis is based on a functional representation of the system. For example, one can exercise one portion of a radar algorithm (the Fast Fourier Transform (FFT) function) by injecting several standard input patterns and comparing the results to standardized output results. An anomalous output would point to one of several items (including the FFT circuit) without specifying the gate or failure mode. For system-level repair, identifying an anomalous chip is sufficient. We describe here an information theoretic and dependency modeling approach that discards much of the detailed physical knowledge about the system and analyzes its information flow and functional interrelationships. The approach relies on group and flow associations and, as such, is hierarchical. Its hierarchical nature allows the approach to be applicable to any level of complexity and to any repair level. This approach has been incorporated in a product called STAMP (System Testability and Maintenance Program) which was developed and refined through more than 10 years of field-level applications to complex system diagnosis. The results have been outstanding, even spectacular in some cases. In this paper we describe system-level testability, system-level diagnoses, and the STAMP analysis approach, as well as a few STAMP applications.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

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.

Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis

PubMed Central

Castro, Alfonso; Sedano, Andrés A.; García, Fco. Javier; Villoslada, Eduardo

2017-01-01

Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica’s global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam. PMID:29283398

Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis.

PubMed

Castro, Alfonso; Sedano, Andrés A; García, Fco Javier; Villoslada, Eduardo; Villagrá, Víctor A

2017-12-28

Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica's global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam.

Differences in the diagnosis and management of systemic lupus erythematosus by primary care and specialist providers in the American Indian/Alaska Native population.

PubMed

McDougall, J A; Helmick, C G; Lim, S S; Johnston, J M; Gaddy, J R; Gordon, C; Ferucci, E D

2018-06-01

Objectives The objective of this study is to investigate differences in the diagnosis and management of systemic lupus erythematosus (SLE) by primary care and specialist physicians in a population-based registry. Methods This study includes individuals from the 2009 Indian Health Service lupus registry population with a diagnosis of SLE documented by either a primary care provider or specialist. SLE classification criteria, laboratory testing, and medication use at any time during the course of disease were determined by medical record abstraction. Results Of the 320 individuals with a diagnosis of SLE, 249 had the diagnosis documented by a specialist, with 71 documented by primary care. Individuals with a specialist diagnosis of SLE were more likely to have medical record documentation of meeting criteria for SLE by all criteria sets (American College of Rheumatology, 79% vs 22%; Boston Weighted, 82% vs 32%; and Systemic Lupus International Collaborating Clinics, 83% vs 35%; p < 0.001 for all comparisons). In addition, specialist diagnosis was associated with documentation of ever having been tested for anti-double-stranded DNA antibody and complement 3 and complement 4 ( p < 0.001). Documentation of ever receiving hydroxychloroquine was also more common with specialist diagnosis (86% vs 64%, p < 0.001). Conclusions Within the population studied, specialist diagnosis of SLE was associated with a higher likelihood of having SLE classification criteria documented, being tested for biomarkers of disease, and ever receiving treatment with hydroxychloroquine. These data support efforts both to increase specialist access for patients with suspected SLE and to provide lupus education to primary care providers.

[Definition of the Diagnosis Osteomyelitis-Osteomyelitis Diagnosis Score (ODS)].

PubMed

Schmidt, H G K; Tiemann, A H; Braunschweig, R; Diefenbeck, M; Bühler, M; Abitzsch, D; Haustedt, N; Walter, G; Schoop, R; Heppert, V; Hofmann, G O; Glombitza, M; Grimme, C; Gerlach, U-J; Flesch, I

2011-08-01

The disease "osteomyelitis" is characterised by different symptoms and parameters. Decisive roles in the development of the disease are played by the causative bacteria, the route of infection and the individual defense mechanisms of the host. The diagnosis is based on different symptoms and findings from the clinical history, clinical symptoms, laboratory results, diagnostic imaging, microbiological and histopathological analyses. While different osteomyelitis classifications have been published, there is to the best of our knowledge no score that gives information how sure the diagnosis "osteomyelitis" is in general. For any scientific study of a disease a valid definition is essential. We have developed a special osteomyelitis diagnosis score for the reliable classification of clinical, laboratory and technical findings. The score is based on five diagnostic procedures: 1) clinical history and risk factors, 2) clinical examination and laboratory results, 3) diagnostic imaging (ultrasound, radiology, CT, MRI, nuclear medicine and hybrid methods), 4) microbiology, and 5) histopathology. Each diagnostic procedure is related to many individual findings, which are weighted by a score system, in order to achieve a relevant value for each assessment. If the sum of the five diagnostic criteria is 18 or more points, the diagnosis of osteomyelitis can be viewed as "safe" (diagnosis class A). Between 8-17 points the diagnosis is "probable" (diagnosis class B). Less than 8 points means that the diagnosis is "possible, but unlikely" (class C diagnosis). Since each parameter can score six points at a maximum, a reliable diagnosis can only be achieved if at least 3 parameters are scored with 6 points. The osteomyelitis diagnosis score should help to avoid the false description of a clinical presentation as "osteomyelitis". A safe diagnosis is essential for the aetiology, treatment and outcome studies of osteomyelitis. © Georg Thieme Verlag KG Stuttgart · New York.

Toward a Computational Model of Tutoring.

ERIC Educational Resources Information Center

Woolf, Beverly Park

1992-01-01

Discusses the integration of instructional science and computer science. Topics addressed include motivation for building knowledge-based systems; instructional design issues, including cognitive models, representing student intentions, and student models and error diagnosis; representing tutoring knowledge; building a tutoring system, including…

THE CAUSAL ANALYSIS / DIAGNOSIS DECISION INFORMATION SYSTEM (CADDIS) - 2007 UPDATE

EPA Science Inventory

CADDIS is an on-line decision support system that helps investigators in the regions, states and tribes find, access, organize, use and share information to produce causal evaluations in aquatic systems. It is based ...

A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis.

PubMed

Gegundez-Arias, Manuel E; Marin, Diego; Ponte, Beatriz; Alvarez, Fatima; Garrido, Javier; Ortega, Carlos; Vasallo, Manuel J; Bravo, Jose M

2017-09-01

This paper presents a methodology and first results of an automatic detection system of first signs of Diabetic Retinopathy (DR) in fundus images, developed for the Health Ministry of the Andalusian Regional Government (Spain). The system detects the presence of microaneurysms and haemorrhages in retinography by means of techniques of digital image processing and supervised classification. Evaluation was conducted on 1058 images of 529 diabetic patients at risk of presenting evidence of DR (an image of each eye is provided). To this end, a ground-truth diagnosis was created based on gradations performed by 3 independent ophthalmology specialists. The comparison between the diagnosis provided by the system and the reference clinical diagnosis shows that the system can work at a level of sensitivity that is similar to that achieved by experts (0.9380 sensitivity per patient against 0.9416 sensitivity of several specialists). False negatives have proven to be mild cases. Moreover, while the specificity of the system is significantly lower than that of human graders (0.5098), it is high enough to screen more than half of the patients unaffected by the disease. Results are promising in integrating this system in DR screening programmes. At an early stage, the system could act as a pre-screening system, by screening healthy patients (with no obvious signs of DR) and identifying only those presenting signs of the disease. Copyright © 2017 Elsevier Ltd. All rights reserved.

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 and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

PubMed Central

Masood, Ammara; Al-Jumaily, Adel Ali

2013-01-01

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. PMID:24575126

Breath analysis based on micropreconcentrator for early cancer diagnosis

NASA Astrophysics Data System (ADS)

Lee, Sang-Seok

2018-02-01

We are developing micropreconcentrators based on micro/nanotechnology to detect trace levels of volatile organic compound (VOC) gases contained in human and canine exhaled breath. The possibility of using exhaled VOC gases as biomarkers for various cancer diagnoses has been previously discussed. For early cancer diagnosis, detection of trace levels of VOC gas is indispensable. Using micropreconcentrators based on MEMS technology or nanotechnology is very promising for detection of VOC gas. A micropreconcentrator based breath analysis technique also has advantages from the viewpoints of cost performance and availability for various cancers diagnosis. In this paper, we introduce design, fabrication and evaluation results of our MEMS and nanotechnology based micropreconcentrators. In the MEMS based device, we propose a flower leaf type Si microstructure, and its shape and configuration are optimized quantitatively by finite element method simulation. The nanotechnology based micropreconcentrator consists of carbon nanotube (CNT) structures. As a result, we achieve ppb level VOC gas detection with our micropreconcentrators and usual gas chromatography system that can detect on the order of ppm VOC in gas samples. In performance evaluation, we also confirm that the CNT based micropreconcentrator shows 115 times better concentration ratio than that of the Si based micropreconcentrator. Moreover, we discuss a commercialization idea for new cancer diagnosis using breath analysis. Future work and preliminary clinical testing in dogs is also discussed.

Expert system for surveillance and diagnosis of breach fuel elements

DOEpatents

Gross, K.C.

1988-01-21

An apparatus and method are disclosed for surveillance and diagnosis of breached fuel elements in a nuclear reactor. A delayed neutron monitoring system provides output signals indicating the delayed neutron activity and age and the equivalent recoil area of a breached fuel element. Sensors are used to provide outputs indicating the status of each component of the delayed neutron monitoring system. Detectors also generate output signals indicating the reactor power level and the primary coolant flow rate of the reactor. The outputs from the detectors and sensors are interfaced with an artificial intelligence-based knowledge system which implements predetermined logic and generates output signals indicating the operability of the reactor. 2 figs.

Expert system for surveillance and diagnosis of breach fuel elements

DOEpatents

Gross, Kenny C.

1989-01-01

An apparatus and method are disclosed for surveillance and diagnosis of breached fuel elements in a nuclear reactor. A delayed neutron monitoring system provides output signals indicating the delayed neutron activity and age and the equivalent recoil areas of a breached fuel element. Sensors are used to provide outputs indicating the status of each component of the delayed neutron monitoring system. Detectors also generate output signals indicating the reactor power level and the primary coolant flow rate of the reactor. The outputs from the detectors and sensors are interfaced with an artificial intelligence-based knowledge system which implements predetermined logic and generates output signals indicating the operability of the reactor.

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

NASA Astrophysics Data System (ADS)

Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

2010-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

Rule-based mechanisms of learning for intelligent adaptive flight control

NASA Technical Reports Server (NTRS)

Handelman, David A.; Stengel, Robert F.

1990-01-01

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

Representing System Behaviors and Expert Behaviors for Intelligent Tutoring. Technical Report No. 108.

ERIC Educational Resources Information Center

Towne, Douglas M.; And Others

Simulation-based software tools that can infer system behaviors from a deep model of the system have the potential for automatically building the semantic representations required to support intelligent tutoring in fault diagnosis. The Intelligent Maintenance Training System (IMTS) is such a resource, designed for use in training troubleshooting…

Integrated Formulation of Beacon-Based Exception Analysis for Multimissions

NASA Technical Reports Server (NTRS)

Mackey, Ryan; James, Mark; Park, Han; Zak, Mickail

2003-01-01

Further work on beacon-based exception analysis for multimissions (BEAM), a method of real-time, automated diagnosis of a complex electromechanical systems, has greatly expanded its capability and suitability of application. This expanded formulation, which fully integrates physical models and symbolic analysis, is described. The new formulation of BEAM expands upon previous advanced techniques for analysis of signal data, utilizing mathematical modeling of the system physics, and expert-system reasoning,

Cost-Effectiveness of Automated Digital Microscopy for Diagnosis of Active Tuberculosis.

PubMed

Jha, Swati; Ismail, Nazir; Clark, David; Lewis, James J; Omar, Shaheed; Dreyer, Andries; Chihota, Violet; Churchyard, Gavin; Dowdy, David W

2016-01-01

Automated digital microscopy has the potential to improve the diagnosis of tuberculosis (TB), particularly in settings where molecular testing is too expensive to perform routinely. The cost-effectiveness of TB diagnostic algorithms using automated digital microscopy remains uncertain. Using data from a demonstration study of an automated digital microscopy system (TBDx, Applied Visual Systems, Inc.), we performed an economic evaluation of TB diagnosis in South Africa from the health system perspective. The primary outcome was the incremental cost per new TB diagnosis made. We considered costs and effectiveness of different algorithms for automated digital microscopy, including as a stand-alone test and with confirmation of positive results with Xpert MTB/RIF ('Xpert', Cepheid, Inc.). Results were compared against both manual microscopy and universal Xpert testing. In settings willing to pay $2000 per incremental TB diagnosis, universal Xpert was the preferred strategy. However, where resources were not sufficient to support universal Xpert, and a testing volume of at least 30 specimens per day could be ensured, automated digital microscopy with Xpert confirmation of low-positive results could facilitate the diagnosis of 79-84% of all Xpert-positive TB cases, at 50-60% of the total cost. The cost-effectiveness of this strategy was $1280 per incremental TB diagnosis (95% uncertainty range, UR: $340-$3440) in the base case, but improved under conditions likely reflective of many settings in sub-Saharan Africa: $677 per diagnosis (95% UR: $450-$935) when sensitivity of manual smear microscopy was lowered to 0.5, and $956 per diagnosis (95% UR: $40-$2910) when the prevalence of multidrug-resistant TB was lowered to 1%. Although universal Xpert testing is the preferred algorithm for TB diagnosis when resources are sufficient, automated digital microscopy can identify the majority of cases and halve the cost of diagnosis and treatment when resources are more scarce and multidrug-resistant TB is not common.

Study on a Real-Time BEAM System for Diagnosis Assistance Based on a System on Chips Design

PubMed Central

Sung, Wen-Tsai; Chen, Jui-Ho; Chang, Kung-Wei

2013-01-01

As an innovative as well as an interdisciplinary research project, this study performed an analysis of brain signals so as to establish BrainIC as an auxiliary tool for physician diagnosis. Cognition behavior sciences, embedded technology, system on chips (SOC) design and physiological signal processing are integrated in this work. Moreover, a chip is built for real-time electroencephalography (EEG) processing purposes and a Brain Electrical Activity Mapping (BEAM) system, and a knowledge database is constructed to diagnose psychosis and body challenges in learning various behaviors and signals antithesis by a fuzzy inference engine. This work is completed with a medical support system developed for the mentally disabled or the elderly abled. PMID:23681095

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

PubMed Central

Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Kanda, Paulo A. M.; Anghinah, Renato

2014-01-01

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment. PMID:24723886

Users' evaluation of the Navy Computer-Assisted Medical Diagnosis System.

PubMed

Merrill, L L; Pearsall, D M; Gauker, E D

1996-01-01

U.S. Navy Independent Duty Corpsmen (IDCs) aboard small ships and submarines are responsible for all clinical and related health care duties while at sea. During deployment, life-threatening illnesses sometimes require evacuation to a shore-based treatment facility. At-sea evacuations are dangerous, expensive, and may compromise the mission of the vessel. Therefore, Group Medical Officers and IDCs were trained to use the Navy Computer-Assisted Medical Diagnosis (NCAMD) system during deployment. They were then surveyed to evaluate the NCAMD system. Their responses show that NCAMD is a cost-efficient, user-friendly package. It is easy to learn, and is especially valuable for training in the diagnosis of chest and abdominal complaints. However, the delivery of patient care at sea would significantly improve if computer hardware were upgraded to current industry standards. Also, adding various computer peripheral devices, structured forms, and reference materials to the at-sea clinician's resources could enhance shipboard patient care.

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

PubMed

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Data acquisition for a real time fault monitoring and diagnosis knowledge-based system for space power system

NASA Technical Reports Server (NTRS)

Wilhite, Larry D.; Lee, S. C.; Lollar, Louis F.

1989-01-01

The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.

Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

PubMed

Yi, Qu; Zhan-ming, Li; Er-chao, Li

2012-11-01

A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

A low-cost digital filing system for echocardiography data with MPEG4 compression and its application to remote diagnosis.

PubMed

Umeda, Akira; Iwata, Yasushi; Okada, Yasumasa; Shimada, Megumi; Baba, Akiyasu; Minatogawa, Yasuyuki; Yamada, Takayasu; Chino, Masao; Watanabe, Takafumi; Akaishi, Makoto

2004-12-01

The high cost of digital echocardiographs and the large size of data files hinder the adoption of remote diagnosis of digitized echocardiography data. We have developed a low-cost digital filing system for echocardiography data. In this system, data from a conventional analog echocardiograph are captured using a personal computer (PC) equipped with an analog-to-digital converter board. Motion picture data are promptly compressed using a moving pictures expert group (MPEG) 4 codec. The digitized data with preliminary reports obtained in a rural hospital are then sent to cardiologists at distant urban general hospitals via the internet. The cardiologists can evaluate the data using widely available movie-viewing software (Windows Media Player). The diagnostic accuracy of this double-check system was confirmed by comparison with ordinary super-VHS videotapes. We have demonstrated that digitization of echocardiography data from a conventional analog echocardiograph and MPEG 4 compression can be performed using an ordinary PC-based system, and that this system enables highly efficient digital storage and remote diagnosis at low cost.

DMS augmented monitoring and diganosis application (DMS AMDA) prototype

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Boyd, Mark A.; Iverson, David L.; Donnell, Brian; Lauritsen, Janet; Doubek, Sharon; Gibson, Jim; Monahan, Christine; Rosenthal, Donald A.

1993-01-01

The Data Management System Augmented Monitoring and Diagnosis Application (DMS AMDA) is currently under development at NASA Ames Research Center (ARC). It will provide automated monitoring and diagnosis capabilities for the Space Station Freedom (SSF) Data Management System (DMS) in the Control Center Complex (CCC) at NASA Johnson Space Center. Several advanced automation applications are under development for use in the CCC for other SSF subsystems. The DMS AMDA, however, is the first application to utilize digraph failure analysis techniques and the Extended Realtime FEAT (ERF) application as the core of its diagnostic system design, since the other projects were begun before the digraph tools were available. Model-based diagnosis and expert systems techniques will provide additional capabilities and augment ERF where appropriate. Utilization of system knowledge captured in the design phase of a system in digraphs should result in both a cost savings and a technical advantage during implementation of the diagnostic software. This paper addresses both the programmatic and technical considerations of this approach, and describes the software design and initial prototyping effort.

New scoring system for intra-abdominal injury diagnosis after blunt trauma.

PubMed

Shojaee, Majid; Faridaalaee, Gholamreza; Yousefifard, Mahmoud; Yaseri, Mehdi; Arhami Dolatabadi, Ali; Sabzghabaei, Anita; Malekirastekenari, Ali

2014-01-01

An accurate scoring system for intra-abdominal injury (IAI) based on clinical manifestation and examination may decrease unnecessary CT scans, save time, and reduce healthcare cost. This study is designed to provide a new scoring system for a better diagnosis of IAI after blunt trauma. This prospective observational study was performed from April 2011 to October 2012 on patients aged above 18 years and suspected with blunt abdominal trauma (BAT) admitted to the emergency department (ED) of Imam Hussein Hospital and Shohadaye Hafte Tir Hospital. All patients were assessed and treated based on Advanced Trauma Life Support and ED protocol. Diagnosis was done according to CT scan findings, which was considered as the gold standard. Data were gathered based on patient's history, physical exam, ultrasound and CT scan findings by a general practitioner who was not blind to this study. Chi-square test and logistic regression were done. Factors with significant relationship with CT scan were imported in multivariate regression models, where a coefficient (β) was given based on the contribution of each of them. Scoring system was developed based on the obtained total β of each factor. Altogether 261 patients (80.1% male) were enrolled (48 cases of IAI). A 24-point blunt abdominal trauma scoring system (BATSS) was developed. Patients were divided into three groups including low (score<8), moderate (8≤score<12) and high risk (score≥12). In high risk group immediate laparotomy should be done, moderate group needs further assessments, and low risk group should be kept under observation. Low risk patients did not show positive CT-scans (specificity 100%). Conversely, all high risk patients had positive CT-scan findings (sensitivity 100%). The receiver operating characteristic curve indicated a close relationship between the results of CT scan and BATSS (sensitivity=99.3%). The present scoring system furnishes a high precision and reproducible diagnostic tool for BAT detection and has the potential to reduce unnecessary CT scan and cut unnecessary costs.

The Development of a General Auxiliary Diagnosis System for Common Disease of Animal

NASA Astrophysics Data System (ADS)

Xiao, Jianhua; Wang, Hongbin; Zhang, Ru; Luan, Peixian; Li, Lin; Xu, Danning

In order to development one expert system for animal disease in china, and this expert system can help veterinary surgeon diagnose all kinds of disease of animal. The design of an intelligent medical system for diagnosis of animal diseases is presented in this paper. The system comprises three major parts: a disease case management system (DCMS), a Knowledge management system (KMS) and an Expert System (ES). The DCMS is used to manipulate patient data include all kinds of data about the animal and the symptom, diagnosis result etc. The KMS is used to acquire knowledge from disease cases and manipulate knowledge by human. The ES is used to perform diagnosis. The program is designed in N-layers system; they are data layer, security layer, business layer, appearance layer, and user interface. When diagnosis, user can select some symptoms in system group by system. One conclusion with three possibilities (final diagnosis result, suspect diagnosis result, and no diagnosis result) is output. By diagnosis some times, one most possible result can be get. By application, this system can increased the accurate of diagnosis to some extent, but the statistics result was not compute now.

RSNA Diagnosis Live: A Novel Web-based Audience Response Tool to Promote Evidence-based Learning.

PubMed

Awan, Omer A; Shaikh, Faiq; Kalbfleisch, Brian; Siegel, Eliot L; Chang, Paul

2017-01-01

Audience response systems have become more commonplace in radiology residency programs in the last 10 years, as a means to engage learners and promote improved learning and retention. A variety of systems are currently in use. RSNA Diagnosis Live™ provides unique features that are innovative, particularly for radiology resident education. One specific example is the ability to annotate questions with subspecialty tags, which allows resident performance to be tracked over time. In addition, deficiencies in learning can be monitored for each trainee and analytics can be provided, allowing documentation of resident performance improvement. Finally, automated feedback is given not only to the instructor, but also to the trainee. Online supplemental material is available for this article. © RSNA, 2017.

[Smart therapeutics based on synthetic gene circuits].

PubMed

Peng, Shuguang; Xie, Zhen

2017-03-25

Synthetic biology has an important impact on biology research since its birth. Applying the thought and methods that reference from electrical engineering, synthetic biology uncovers many regulatory mechanisms of life systems, transforms and expands a series of biological components. Therefore, it brings a wide range of biomedical applications, including providing new ideas for disease diagnosis and treatment. This review describes the latest advances in the field of disease diagnosis and therapy based on mammalian cell or bacterial synthetic gene circuits, and provides new ideas for future smart therapy design.

An auxiliary classification diagnosis software development of cervical cancer medical data based on various artificial neural networks

NASA Astrophysics Data System (ADS)

Qi, Yong; Lei, Kai; Zhang, Lizeqing; Xing, Ximing; Gou, Wenyue

2018-06-01

This paper introduced the development of a self-serving medical data assisted diagnosis software of cervical cancer on the basis of artificial neural network (SVN, FNN, KNN). The system is developed based on the idea of self-service platform, supported by the application and innovation of neural network algorithm in medical data identification. Furthermore, it combined the advanced methods in various fields to effectively solve the complicated and inaccurate problem of cervical canceration data in the traditional manual treatment.

A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis.

PubMed

Sahan, Seral; Polat, Kemal; Kodaz, Halife; Güneş, Salih

2007-03-01

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

Clinical review: Riedel's thyroiditis: a clinical review.

PubMed

Hennessey, James V

2011-10-01

Riedel's thyroiditis is a rare inflammatory process involving the thyroid and surrounding cervical tissues and is associated with various forms of systemic fibrosis. Riedel's presentation is complex, including a thyroid mass associated with local symptoms, characteristic biochemical abnormalities such as hypocalcemia and hypothyroidism, as well as the involvement of a wide range of other organ systems. Diagnosis of Riedel's thyroiditis requires histopathological confirmation, but due to high complication rates, the role of surgical intervention is limited to airway decompression and diagnostic tissue retrieval. Unique among processes of the thyroid, Riedel's is commonly treated with long-term antiinflammatory medications to arrest progression and maintain a symptom-free course. Due to its rarity, Riedel's may not be immediately diagnosed, so clinicians benefit from recognizing the constellation of findings that should make prompt diagnosis possible. A review of print and electronic reviews was conducted. Source references were identified, and available literature was reviewed. A search of the PubMed database using the search term "Riedel's thyroiditis" was cross-referenced with associated clinical findings, systemic fibrosis diagnoses, and therapeutic search terms. Because most of the literature consisted of case reports and very small series, inclusion of identified articles was based on clinical descriptions of the subjects included and the criteria for diagnosis reported. More weight was attributed to series, using contemporary criteria for diagnosis. Case reports were included if the diagnosis was clear and clinical presentation was unique to illustrate the spectrum of disease. Because the majority of therapeutic intervention data were based upon case reports and very small series, an evidence-based approach was problematic, but information is presented as objectively and with as much balance as the limited quality of the data allows. Clinical awareness of the characteristic presentations of Riedel's thyroiditis should enhance our ability to make this diagnosis in a timely and focused manner. Recognition of certain clinical finding patterns will increase the likelihood of recognizing Riedel's thyroiditis promptly. Local restrictive or infiltrative symptoms out of proportion to a demonstrable mass or simultaneous biochemical deficiencies especially of calcium should lead the clinician to consider this diagnosis. Likewise in this setting, the surgeon alert to this possibility may minimize overly aggressive surgical intervention, thus avoiding complications. Once Riedel's thyroiditis is diagnosed, the application of antiinflammatory therapies may greatly enhance the clinical outcome. Understanding the pathophysiological relationship of this entity with other forms of systemic fibrosis and the role that IgG4 may play in this process should result in enhanced diagnostic and therapeutic tools in the future.

An artificial intelligence approach to onboard fault monitoring and diagnosis for aircraft applications

NASA Technical Reports Server (NTRS)

Schutte, P. C.; Abbott, K. H.

1986-01-01

Real-time onboard fault monitoring and diagnosis for aircraft applications, whether performed by the human pilot or by automation, presents many difficult problems. Quick response to failures may be critical, the pilot often must compensate for the failure while diagnosing it, his information about the state of the aircraft is often incomplete, and the behavior of the aircraft changes as the effect of the failure propagates through the system. A research effort was initiated to identify guidelines for automation of onboard fault monitoring and diagnosis and associated crew interfaces. The effort began by determining the flight crew's information requirements for fault monitoring and diagnosis and the various reasoning strategies they use. Based on this information, a conceptual architecture was developed for the fault monitoring and diagnosis process. This architecture represents an approach and a framework which, once incorporated with the necessary detail and knowledge, can be a fully operational fault monitoring and diagnosis system, as well as providing the basis for comparison of this approach to other fault monitoring and diagnosis concepts. The architecture encompasses all aspects of the aircraft's operation, including navigation, guidance and controls, and subsystem status. The portion of the architecture that encompasses subsystem monitoring and diagnosis was implemented for an aircraft turbofan engine to explore and demonstrate the AI concepts involved. This paper describes the architecture and the implementation for the engine subsystem.

Accuracy of 'My Gut Feeling:' Comparing System 1 to System 2 Decision-Making for Acuity Prediction, Disposition and Diagnosis in an Academic Emergency Department.

PubMed

Cabrera, Daniel; Thomas, Jonathan F; Wiswell, Jeffrey L; Walston, James M; Anderson, Joel R; Hess, Erik P; Bellolio, M Fernanda

2015-09-01

Current cognitive sciences describe decision-making using the dual-process theory, where a System 1 is intuitive and a System 2 decision is hypothetico-deductive. We aim to compare the performance of these systems in determining patient acuity, disposition and diagnosis. Prospective observational study of emergency physicians assessing patients in the emergency department of an academic center. Physicians were provided the patient's chief complaint and vital signs and allowed to observe the patient briefly. They were then asked to predict acuity, final disposition (home, intensive care unit (ICU), non-ICU bed) and diagnosis. A patient was classified as sick by the investigators using previously published objective criteria. We obtained 662 observations from 289 patients. For acuity, the observers had a sensitivity of 73.9% (95% CI [67.7-79.5%]), specificity 83.3% (95% CI [79.5-86.7%]), positive predictive value 70.3% (95% CI [64.1-75.9%]) and negative predictive value 85.7% (95% CI [82.0-88.9%]). For final disposition, the observers made a correct prediction in 80.8% (95% CI [76.1-85.0%]) of the cases. For ICU admission, emergency physicians had a sensitivity of 33.9% (95% CI [22.1-47.4%]) and a specificity of 96.9% (95% CI [94.0-98.7%]). The correct diagnosis was made 54% of the time with the limited data available. System 1 decision-making based on limited information had a sensitivity close to 80% for acuity and disposition prediction, but the performance was lower for predicting ICU admission and diagnosis. System 1 decision-making appears insufficient for final decisions in these domains but likely provides a cognitive framework for System 2 decision-making.

Flu Diagnosis System Using Jaccard Index and Rough Set Approaches

NASA Astrophysics Data System (ADS)

Efendi, Riswan; Azah Samsudin, Noor; Mat Deris, Mustafa; Guan Ting, Yip

2018-04-01

Jaccard index and rough set approaches have been frequently implemented in decision support systems with various domain applications. Both approaches are appropriate to be considered for categorical data analysis. This paper presents the applications of sets operations for flu diagnosis systems based on two different approaches, such as, Jaccard index and rough set. These two different approaches are established using set operations concept, namely intersection and subset. The step-by-step procedure is demonstrated from each approach in diagnosing flu system. The similarity and dissimilarity indexes between conditional symptoms and decision are measured using Jaccard approach. Additionally, the rough set is used to build decision support rules. Moreover, the decision support rules are established using redundant data analysis and elimination of unclassified elements. A number data sets is considered to attempt the step-by-step procedure from each approach. The result has shown that rough set can be used to support Jaccard approaches in establishing decision support rules. Additionally, Jaccard index is better approach for investigating the worst condition of patients. While, the definitely and possibly patients with or without flu can be determined using rough set approach. The rules may improve the performance of medical diagnosis systems. Therefore, inexperienced doctors and patients are easier in preliminary flu diagnosis.

Basic physiological systems indicator's informative assessment for children and adolescents obesity diagnosis tasks

NASA Astrophysics Data System (ADS)

Marukhina, O. V.; Berestneva, O. G.; Emelyanova, Yu A.; Romanchukov, S. V.; Petrova, L.; Lombardo, C.; Kozlova, N. V.

2018-05-01

The healthcare computerization creates opportunities to the clinical decision support system development. In the course of diagnosis, doctor manipulates a considerable amount of data and makes a decision in the context of uncertainty basing upon the first-hand experience and knowledge. The situation is exacerbated by the fact that the knowledge scope in medicine is incrementally growing, but the decision-making time does not increase. The amount of medical malpractice is growing and it leads to various negative effects, even the mortality rate increase. IT-solution's development for clinical purposes is one of the most promising and efficient ways to prevent these effects. That is why the efforts of many IT specialists are directed to the doctor's heuristics simulating software or expert-based medical decision-making algorithms development. Thus, the objective of this study is to develop techniques and approaches for the body physiological system's informative value assessment index for the obesity degree evaluation based on the diagnostic findings.

Detection of Kaposi's Sarcoma Associated Herpesvirus Nucleic Acids Using a Smartphone Accessory

PubMed Central

Mancuso, Matthew; Cesarman, Ethel; Erickson, David

2014-01-01

Kaposi's sarcoma (KS) is an infectious cancer occurring in immune-compromised patients, caused by Kaposi's sarcoma associated herpesvirus (KSHV). Our vision is to simplify the process of KS diagnosis through the creation of a smartphone based point-of-care system capable of yielding an actionable diagnostic readout starting from a raw biopsy sample. In this work we develop the sensing mechanism for the overall system, a smartphone accessory capable of detecting KSHV nucleic acids. The accessory reads out microfluidic chips filled with a colorimetric nanoparticle assay targeted at KSHV. We calculate that our final device can read out gold nanoparticle solutions with an accuracy of .05 OD, and we demonstrate that it can detect DNA sequences from KSHV down to 1 nM. We believe that through integration with our previously developed components, a smartphone based system like the one studied here can provide accurate detection information, as well as a simple platform for field based clinical diagnosis and research. PMID:25117534

On-line and real-time diagnosis method for proton membrane fuel cell (PEMFC) stack by the superposition principle

NASA Astrophysics Data System (ADS)

Lee, Young-Hyun; Kim, Jonghyeon; Yoo, Seungyeol

2016-09-01

The critical cell voltage drop in a stack can be followed by stack defect. A method of detecting defective cell is the cell voltage monitoring. The other methods are based on the nonlinear frequency response. In this paper, the superposition principle for the diagnosis of PEMFC stack is introduced. If critical cell voltage drops exist, the stack behaves as a nonlinear system. This nonlinearity can explicitly appear in the ohmic overpotential region of a voltage-current curve. To detect the critical cell voltage drop, a stack is excited by two input direct test-currents which have smaller amplitude than an operating stack current and have an equal distance value from the operating current. If the difference between one voltage excited by a test current and the voltage excited by a load current is not equal to the difference between the other voltage response and the voltage excited by the load current, the stack system acts as a nonlinear system. This means that there is a critical cell voltage drop. The deviation from the value zero of the difference reflects the grade of the system nonlinearity. A simulation model for the stack diagnosis is developed based on the SPP, and experimentally validated.

Concordance between gambling disorder diagnoses in the DSM-IV and DSM-5; Results from the National Epidemiological Survey of Alcohol and Related Disorders

PubMed Central

Petry, Nancy M.; Blanco, Carlos; Jin, Chelsea; Grant, Bridget F.

2015-01-01

The fifth edition of the Diagnostic and Statistic Manual for Mental Disorders (DSM-5) eliminates the committing illegal acts criterion and reduces the threshold for a diagnosis of gambling disorder to four of nine criteria. This study compared the DSM-5 “4 of 9” classification system to the “5 of 10” DSM-IV system, as well as other permutations (i.e., just lowing the threshold to four criteria or just eliminating the illegal acts criterion) in 43,093 respondents to the National Epidemiological Survey of Alcohol and Related Conditions. Subgroups were analyzed to ascertain if changes will impact differentially diagnoses based on gender, age or race/ethnicity. In the full sample and each subpopulation, prevalence rates were higher when the DSM-5 classification system was employed relative to the DSM-IV system, but the hit rate between the two systems ranged from 99.80% to 99.96%. Across all gender, age and racial/ethnic subgroups, specificity was greater than 99% when the DSM-5 system was employed relative to the DSM-IV system, and sensitivity was 100%. Results from this study suggest that eliminating the illegal acts criterion has little impact on diagnosis of gambling disorder, but lowering the threshold for diagnosis does increase the base rate in the general population and each subgroup, even though overall rates remain low and sensitivity and specificity are high. PMID:24588275

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

PubMed

Al-Masni, Mohammed A; Al-Antari, Mugahed A; Park, Jeong-Min; Gi, Geon; Kim, Tae-Yeon; Rivera, Patricio; Valarezo, Edwin; Choi, Mun-Taek; Han, Seung-Moo; Kim, Tae-Seong

2018-04-01

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions. Copyright © 2018 Elsevier B.V. All rights reserved.

On-Site Molecular Detection of Soil-Borne Phytopathogens Using a Portable Real-Time PCR System

PubMed Central

DeShields, Joseph B.; Bomberger, Rachel A.; Woodhall, James W.; Wheeler, David L.; Moroz, Natalia; Johnson, Dennis A.; Tanaka, Kiwamu

2018-01-01

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management strategies that reduce the impact of the disease. Presently in many diagnostic laboratories, the polymerase chain reaction (PCR), particularly real-time PCR, is considered the most sensitive and accurate method for plant pathogen detection. However, laboratory-based PCRs typically require expensive laboratory equipment and skilled personnel. In this study, soil-borne pathogens of potato are used to demonstrate the potential for on-site molecular detection. This was achieved using a rapid and simple protocol comprising of magnetic bead-based nucleic acid extraction, portable real-time PCR (fluorogenic probe-based assay). The portable real-time PCR approach compared favorably with a laboratory-based system, detecting as few as 100 copies of DNA from Spongospora subterranea. The portable real-time PCR method developed here can serve as an alternative to laboratory-based approaches and a useful on-site tool for pathogen diagnosis. PMID:29553557

Particle Filters for Real-Time Fault Detection in Planetary Rovers

NASA Technical Reports Server (NTRS)

Dearden, Richard; Clancy, Dan; Koga, Dennis (Technical Monitor)

2001-01-01

Planetary rovers provide a considerable challenge for robotic systems in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have on-board fault detection and diagnosis capabilities in order to determine the actual state of the vehicle, and decide what actions are safe to perform. Traditional model-based diagnosis techniques are not suitable for rovers due to the tight coupling between the vehicle's performance and its environment. Hybrid diagnosis using particle filters is presented as an alternative, and its strengths and weakeners are examined. We also present some extensions to particle filters that are designed to make them more suitable for use in diagnosis problems.

Study on user interface of pathology picture archiving and communication system.

PubMed

Kim, Dasueran; Kang, Peter; Yun, Jungmin; Park, Sung-Hye; Seo, Jeong-Wook; Park, Peom

2014-01-01

It is necessary to improve the pathology workflow. A workflow task analysis was performed using a pathology picture archiving and communication system (pathology PACS) in order to propose a user interface for the Pathology PACS considering user experience. An interface analysis of the Pathology PACS in Seoul National University Hospital and a task analysis of the pathology workflow were performed by observing recorded video. Based on obtained results, a user interface for the Pathology PACS was proposed. Hierarchical task analysis of Pathology PACS was classified into 17 tasks including 1) pre-operation, 2) text, 3) images, 4) medical record viewer, 5) screen transition, 6) pathology identification number input, 7) admission date input, 8) diagnosis doctor, 9) diagnosis code, 10) diagnosis, 11) pathology identification number check box, 12) presence or absence of images, 13) search, 14) clear, 15) Excel save, 16) search results, and 17) re-search. And frequently used menu items were identified and schematized. A user interface for the Pathology PACS considering user experience could be proposed as a preliminary step, and this study may contribute to the development of medical information systems based on user experience and usability.

Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis.

PubMed

Garnavi, Rahil; Aldeen, Mohammad; Bailey, James

2012-11-01

This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimised selection and integration of features derived from textural, borderbased and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundaryseries model of the lesion border and analysing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimised selection of features is achieved by using the Gain-Ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, Support Vector Machine, Random Forest, Logistic Model Tree and Hidden Naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation and test image sets. The system achieves and accuracy of 91.26% and AUC value of 0.937, when 23 features are used. Other important findings include (i) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and (ii) higher contribution of texture features than border-based features in the optimised feature set.

Accident diagnosis system based on real-time decision tree expert system

NASA Astrophysics Data System (ADS)

Nicolau, Andressa dos S.; Augusto, João P. da S. C.; Schirru, Roberto

2017-06-01

Safety is one of the most studied topics when referring to power stations. For that reason, sensors and alarms develop an important role in environmental and human protection. When abnormal event happens, it triggers a chain of alarms that must be, somehow, checked by the control room operators. In this case, diagnosis support system can help operators to accurately identify the possible root-cause of the problem in short time. In this article, we present a computational model of a generic diagnose support system based on artificial intelligence, that was applied on the dataset of two real power stations: Angra1 Nuclear Power Plant and Santo Antônio Hydroelectric Plant. The proposed system processes all the information logged in the sequence of events before a shutdown signal using the expert's knowledge inputted into an expert system indicating the chain of events, from the shutdown signal to its root-cause. The results of both applications showed that the support system is a potential tool to help the control room operators identify abnormal events, as accidents and consequently increase the safety.

Statin use after esophageal cancer diagnosis and survival: A population based cohort study.

PubMed

Cardwell, Chris R; Spence, Andrew D; Hughes, Carmel M; Murray, Liam J

2017-06-01

A recent epidemiological study of esophageal cancer patients concluded statin use post-diagnosis was associated with large (38%) and significant reductions in cancer-specific mortality. We investigated statin use and cancer-specific mortality in a large population-based cohort of esophageal cancer patients. Newly diagnosed [2009-2012] esophageal cancer patients were identified from the Scottish Cancer Registry and linked with the Prescribing Information System and Scotland Death Records (to January 2015). Time-dependent Cox regression models were used to calculate hazard ratios (HR) for cancer-specific mortality and 95% confidence intervals (CIs) by post-diagnostic statin use (using a 6 month lag to reduce reverse causation) and to adjust these HRs for potential confounders. 1921 esophageal cancer patients were included in the main analysis, of whom 651 (34%) used statins after diagnosis. There was little evidence of a reduction in esophageal cancer-specific mortality in statin users compared with non-users after diagnosis (adjusted HR=0.93, 95% CI, 0.81, 1.07) and no dose response associations were seen. However, statin users compared with non-users in the year before diagnosis had a weak reduction in esophageal cancer-specific mortality (adjusted HR=0.88, 95% CI, 0.79, 0.99). In this large population-based esophageal cancer cohort, there was little evidence of a reduction in esophageal cancer-specific mortality with statin use after diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Galileo PPS expert monitoring and diagnostic prototype

NASA Technical Reports Server (NTRS)

Bahrami, Khosrow

1989-01-01

The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text.

Electromechanical impedance-based health diagnosis for tendon and anchorage zone in a nuclear containment structure

NASA Astrophysics Data System (ADS)

Min, Jiyoung; Shim, Hyojin; Yun, Chung-Bang

2012-04-01

For a nuclear containment structure, the structural health monitoring is essential because of its high potential risk and grave social impact. In particular, the tendon and anchorage zone are to be monitored because they are under high tensile or compressive stress. In this paper, a method to monitor the tendon force and the condition of the anchorage zone is presented by using the impedance-based health diagnosis system. First, numerical simulations were conducted for cases with various loose tensile forces on the tendon as well as damages on the bearing plate and concrete structure. Then, experimental studies were carried out on a scaled model of the anchorage system. The relationship between the loose tensile force and the impedance-based damage index was analyzed by a regression analysis. When a structure gets damaged, the damage index increases so that the status of damage can be identified. The results of the numerical and experimental studies indicate a big potential of the proposed impedance-based method for monitoring the tendon and anchorage system.

Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

PubMed

Sudha, M

2017-09-27

As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

Fault detection and diagnosis of photovoltaic systems

NASA Astrophysics Data System (ADS)

Wu, Xing

The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on fault diagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in fault diagnosis.

Case mix measures and diagnosis-related groups: opportunities and threats for inpatient dermatology.

PubMed

Hensen, P; Fürstenberg, T; Luger, T A; Steinhoff, M; Roeder, N

2005-09-01

The changing healthcare environment world-wide is leading to extensive use of per case payment systems based on diagnosis-related groups (DRG). The aim of this study was to examine the impact of application of different DRG systems used in the German healthcare system. We retrospectively analysed 2334 clinical data sets of inpatients discharged from an academic dermatological inpatient unit in 2003. Data were regarded as providing high coding quality in compliance with the diagnosis and procedure classifications as well as coding standards. The application of the Australian AR-DRG version 4.1, the German G-DRG version 1.0, and the German G-DRG version 2004 was considered in detail. To evaluate more specific aspects, data were broken down into 11 groups based on the principle diagnosis. DRG cost weights and case mix index were used to compare coverage of inpatient dermatological services. Economic impacts were illustrated by case mix volumes and calculation of DRG payments. Case mix index results and the pending prospective revenues vary tremendously from the application of one or another of the DRG systems. The G-DRG version 2004 provides increased levels of case mix index that encourages, in particular, medical dermatology. The AR-DRG version 4.1 and the first German DRG version 1.0 appear to be less suitable to adequately cover inpatient dermatology. The G-DRG version 2004 has been greatly improved, probably due to proceeding calculation standards and DRG adjustments. The future of inpatient dermatology is subject to appropriate depiction of well-established treatment standards.

Introduction of the Bethesda System to Mainland China with a Web-based tutorial.

PubMed

Yuan, Qin; Chang, Alexander Russell; Ng, Ho Keung

2003-01-01

To validate the use of a Web-based tutorial to introduce the Bethesda System (TBS) to Mainland Chinese laboratories. Digitized color images of the diagnostic features in 20 Pap smears were displayed on a Web page. Participants were asked to give each smear a diagnosis using the reporting nomenclature employed in their laboratory or one that was familiar to them. This was followed by teaching images of each smear accompanied by text in English and Chinese that highlighted important features for making a diagnosis using TBS. Participants then reviewed the 20 original Pap smears and rendered a diagnosis using TBS. Pathologists and cytotechnologists at 17 laboratories located in 10 cities completed the exercise. The average diagnostic accuracy for the 20 Pap smears before and after the tutorial was 76% and 88%, respectively. Web-based tutorials can be used for disseminating cytologic information to widely dispersed laboratories in China and help enhance the practice of cytology, currently an underutilized diagnostic technique. However, such difficulties as lack of Internet connections in the laboratory, outdated computers and a lack of interest in cytology need to be overcome to ensure success.

Noise test system of rotating machinery in nuclear power station based on microphone array

NASA Astrophysics Data System (ADS)

Chang, Xincai; Guan, Jishi; Qi, Liangcai

2017-12-01

Rotating machinery plays an important role in all walks of life. Once the equipment fails, equipment maintenance and shutdown will cause great social harm and economic losses. Equipment safety operations at nuclear power stations have always been of top priority. It is prone to noise when the equipment is out of order or aging. Failure to find or develop equipment at the initial stage of equipment failure or ageing will pose a serious threat to the safety of the plant’s equipment. In this paper, sound imaging diagnosis technology is applied as a supplementary method to the condition monitoring and diagnosis system of rotating machinery in nuclear power stations. It provides a powerful guarantee for the condition monitoring and fault diagnosis of rotating machinery in nuclear power stations.

Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

NASA Astrophysics Data System (ADS)

Gadsden, S. Andrew; Kirubarajan, T.

2017-05-01

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

Guidelines of the Brazilian Dermatology Society for diagnosis, treatment and follow up of primary cutaneous melanoma - Part I*

PubMed Central

Castro, Luiz Guilherme Martins; Messina, Maria Cristina; Loureiro, Walter; Macarenco, Ricardo Silvestre; Duprat Neto, João Pedreira; Giacomo, Thais Helena Bello Di; Bittencourt, Flávia Vasques; Bakos, Renato Marchiori; Serpa, Sérgio Schrader; Stolf, Hamilton Ometto; Gontijo, Gabriel

2015-01-01

The last Brazilian guidelines on melanoma were published in 2002. Development in diagnosis and treatment made updating necessary. The coordinators elaborated ten clinical questions, based on PICO system. A Medline search, according to specific MeSH terms for each of the 10 questions was performed and articles selected were classified from A to D according to level of scientific evidence. Based on the results, recommendations were defined and classified according to scientific strength. The present Guidelines were divided in two parts for editorial and publication reasons. In the first part, the following clinical questions were answered: 1) The use of dermoscopy for diagnosis of primary cutaneous melanoma brings benefits for patients when compared with clinical examination? 2) Does dermoscopy favor diagnosis of nail apparatus melanoma? 3) Is there a prognostic difference when incisional or excisional biopsies are used? 4) Does revision by a pathologist trained in melanoma contribute to diagnosis and treatment of primary cutaneous melanoma? What margins should be used to treat lentigo maligna melanoma and melanoma in situ? PMID:26734867

Computer decision support system for the stomach cancer diagnosis

NASA Astrophysics Data System (ADS)

Polyakov, E. V.; Sukhova, O. G.; Korenevskaya, P. Y.; Ovcharova, V. S.; Kudryavtseva, I. O.; Vlasova, S. V.; Grebennikova, O. P.; Burov, D. A.; Yemelyanova, G. S.; Selchuk, V. Y.

2017-01-01

The paper considers the creation of the computer knowledge base containing the data of histological, cytologic, and clinical researches. The system is focused on improvement of diagnostics quality of stomach cancer - one of the most frequent death causes among oncologic patients.

Layered clustering multi-fault diagnosis for hydraulic piston pump

NASA Astrophysics Data System (ADS)

Du, Jun; Wang, Shaoping; Zhang, Haiyan

2013-04-01

Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump. In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.

Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation

PubMed Central

Domínguez Hernández, Karem R.; Aguilar Lasserre, Alberto A.; Posada Gómez, Rubén; Palet Guzmán, José A.; González Sánchez, Blanca E.

2013-01-01

Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN. PMID:23690881

Hospital payment systems based on diagnosis-related groups: experiences in low- and middle-income countries

PubMed Central

Wittenbecher, Friedrich

2013-01-01

Abstract Objective This paper provides a comprehensive overview of hospital payment systems based on diagnosis-related groups (DRGs) in low- and middle-income countries. It also explores design and implementation issues and the related challenges countries face. Methods A literature research for papers on DRG-based payment systems in low- and middle-income countries was conducted in English, French and Spanish through Pubmed, the Pan American Health Organization’s Regional Library of Medicine and Google. Findings Twelve low- and middle-income countries have DRG-based payment systems and another 17 are in the piloting or exploratory stage. Countries have chosen from a wide range of imported and self-developed DRG models and most have adapted such models to their specific contexts. All countries have set expenditure ceilings. In general, systems were piloted before being implemented. The need to meet certain requirements in terms of coding standardization, data availability and information technology made implementation difficult. Private sector providers have not been fully integrated, but most countries have managed to delink hospital financing from public finance budgeting. Conclusion Although more evidence on the impact of DRG-based payment systems is needed, our findings suggest that (i) the greater portion of health-care financing should be public rather than private; (ii) it is advisable to pilot systems first and to establish expenditure ceilings; (iii) countries that import an existing variant of a DRG-based system should be mindful of the need for adaptation; and (iv) countries should promote the cooperation of providers for appropriate data generation and claims management. PMID:24115798

Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis.

PubMed

Braido, Fulvio; Santus, Pierachille; Corsico, Angelo Guido; Di Marco, Fabiano; Melioli, Giovanni; Scichilone, Nicola; Solidoro, Paolo

2018-01-01

The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive lung disease (COLD). A questionnaire and a WebFlex code were developed and validated in silico. An expert panel pilot validation on 60 cases and a clinical validation on 241 cases were performed. The developed questionnaire and code validated in silico resulted in a suitable tool to support the medical diagnosis. The clinical validation of the ES was performed in an academic setting that included six different reference centers for respiratory diseases. The results of the ES expressed as a score associated with the risk of suffering from COLD were matched and compared with the final clinical diagnoses. A set of 60 patients were evaluated by a pilot expert panel validation with the aim of calculating the sample size for the clinical validation study. The concordance analysis between these preliminary ES scores and diagnoses performed by the experts indicated that the accuracy was 94.7% when both experts and the system confirmed the COLD diagnosis and 86.3% when COLD was excluded. Based on these results, the sample size of the validation set was established in 240 patients. The clinical validation, performed on 241 patients, resulted in ES accuracy of 97.5%, with confirmed COLD diagnosis in 53.6% of the cases and excluded COLD diagnosis in 32% of the cases. In 11.2% of cases, a diagnosis of COLD was made by the experts, although the imaging results showed a potential concomitant disorder. The ES presented here (COLD ES ) is a safe and robust supporting tool for COLD diagnosis in primary care settings.

Expert System Shells for Rapid Clinical Decision Support Module Development: An ESTA Demonstration of a Simple Rule-Based System for the Diagnosis of Vaginal Discharge

PubMed Central

2012-01-01

Objectives This study demonstrates the feasibility of using expert system shells for rapid clinical decision support module development. Methods A readily available expert system shell was used to build a simple rule-based system for the crude diagnosis of vaginal discharge. Pictures and 'canned text explanations' are extensively used throughout the program to enhance its intuitiveness and educational dimension. All the steps involved in developing the system are documented. Results The system runs under Microsoft Windows and is available as a free download at http://healthcybermap.org/vagdisch.zip (the distribution archive includes both the program's executable and the commented knowledge base source as a text document). The limitations of the demonstration system, such as the lack of provisions for assessing uncertainty or various degrees of severity of a sign or symptom, are discussed in detail. Ways of improving the system, such as porting it to the Web and packaging it as an app for smartphones and tablets, are also presented. Conclusions An easy-to-use expert system shell enables clinicians to rapidly become their own 'knowledge engineers' and develop concise evidence-based decision support modules of simple to moderate complexity, targeting clinical practitioners, medical and nursing students, as well as patients, their lay carers and the general public (where appropriate). In the spirit of the social Web, it is hoped that an online repository can be created to peer review, share and re-use knowledge base modules covering various clinical problems and algorithms, as a service to the clinical community. PMID:23346475

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity

PubMed Central

Wittenberg, Leah A.; Jonsson, Nina J.; Chan, RV Paul; Chiang, Michael F.

2014-01-01

Presence of plus disease in retinopathy of prematurity (ROP) is an important criterion for identifying treatment-requiring ROP. Plus disease is defined by a standard published photograph selected over 20 years ago by expert consensus. However, diagnosis of plus disease has been shown to be subjective and qualitative. Computer-based image analysis, using quantitative methods, has potential to improve the objectivity of plus disease diagnosis. The objective was to review the published literature involving computer-based image analysis for ROP diagnosis. The PubMed and Cochrane library databases were searched for the keywords “retinopathy of prematurity” AND “image analysis” AND/OR “plus disease.” Reference lists of retrieved articles were searched to identify additional relevant studies. All relevant English-language studies were reviewed. There are four main computer-based systems, ROPtool (AU ROC curve, plus tortuosity 0.95, plus dilation 0.87), RISA (AU ROC curve, arteriolar TI 0.71, venular diameter 0.82), Vessel Map (AU ROC curve, arteriolar dilation 0.75, venular dilation 0.96), and CAIAR (AU ROC curve, arteriole tortuosity 0.92, venular dilation 0.91), attempting to objectively analyze vessel tortuosity and dilation in plus disease in ROP. Some of them show promise for identification of plus disease using quantitative methods. This has potential to improve the diagnosis of plus disease, and may contribute to the management of ROP using both traditional binocular indirect ophthalmoscopy and image-based telemedicine approaches. PMID:21366159

[Utility of axial images in an early Alzheimer disease diagnosis support system (VSRAD)].

PubMed

Goto, Masami; Aoki, Shigeki; Abe, Osamu; Masumoto, Tomohiko; Watanabe, Yasushi; Satake, Yoshiroh; Nishida, Katsuji; Ino, Kenji; Yano, Keiichi; Iida, Kyohhito; Mima, Kazuo; Ohtomo, Kuni

2006-09-20

In recent years, voxel-based morphometry (VBM) has become a popular tool for the early diagnosis of Alzheimer disease. The Voxel-Based Specific Regional Analysis System for Alzheimer's Disease (VSRAD), a VBM system that uses MRI, has been reported to be clinically useful. The able-bodied person database (DB) of VSRAD, which employs sagittal plane imaging, is not suitable for analysis by axial plane imaging. However, axial plane imaging is useful for avoiding motion artifacts from the eyeball. Therefore, we created an able-bodied person DB by axial plane imaging and examined its utility. We also analyzed groups of able-bodied persons and persons with dementia by axial plane imaging and reviewed the validity. After using the DB of axial plane imaging, the Z-score of the intrahippocampal region improved by 8 in 13 instances. In all brains, the Z-score improved by 13 in all instances.

Porous TiO₂-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis.

PubMed

Galstyan, Vardan

2017-12-19

Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO₂, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO₂-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO₂ may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material.

Porous TiO2-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis

PubMed Central

2017-01-01

Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO2, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO2-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO2 may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material. PMID:29257076

Establishment and validation of the scoring system for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma.

PubMed

Liu, Wen; Cheng, Ruochuan; Ma, Yunhai; Wang, Dan; Su, Yanjun; Diao, Chang; Zhang, Jianming; Qian, Jun; Liu, Jin

2018-05-03

Early preoperative diagnosis of central lymph node metastasis (CNM) is crucial to improve survival rates among patients with papillary thyroid carcinoma (PTC). Here, we analyzed clinical data from 2862 PTC patients and developed a scoring system using multivariable logistic regression and testified by the validation group. The predictive diagnostic effectiveness of the scoring system was evaluated based on consistency, discrimination ability, and accuracy. The scoring system considered seven variables: gender, age, tumor size, microcalcification, resistance index >0.7, multiple nodular lesions, and extrathyroid extension. The area under the receiver operating characteristic curve (AUC) was 0.742, indicating a good discrimination. Using 5 points as a diagnostic threshold, the validation results for validation group had an AUC of 0.758, indicating good discrimination and consistency in the scoring system. The sensitivity of this predictive model for preoperative diagnosis of CNM was 4 times higher than a direct ultrasound diagnosis. These data indicate that the CNM prediction model would improve preoperative diagnostic sensitivity for CNM in patients with papillary thyroid carcinoma.

[Medical expert systems and clinical needs].

PubMed

Buscher, H P

1991-10-18

The rapid expansion of computer-based systems for problem solving or decision making in medicine, the so-called medical expert systems, emphasize the need for reappraisal of their indication and value. Where specialist knowledge is required, in particular where medical decisions are susceptible to error these systems will probably serve as a valuable support. In the near future computer-based systems should be able to aid the interpretation of findings of technical investigations and the control of treatment, especially where rapid reactions are necessary despite the need of complex analysis of investigated parameters. In the distant future complete support of diagnostic procedures from the history to final diagnosis is possible. It promises to be particularly attractive for the diagnosis of seldom diseases, for difficult differential diagnoses, and in the decision making in the case of expensive, risky or new diagnostic or therapeutic methods. The physician needs to be aware of certain dangers, ranging from misleading information up to abuse. Patient information depends often on subjective reports and error-prone observations. Although basing on problematic knowledge computer-born decisions may have an imperative effect on medical decision making. Also it must be born in mind that medical decisions should always combine the rational with a consideration of human motives.

Diagnosis support system based on clinical guidelines: comparison between case-based fuzzy cognitive maps and Bayesian networks.

PubMed

Douali, Nassim; Csaba, Huszka; De Roo, Jos; Papageorgiou, Elpiniki I; Jaulent, Marie-Christine

2014-01-01

Several studies have described the prevalence and severity of diagnostic errors. Diagnostic errors can arise from cognitive, training, educational and other issues. Examples of cognitive issues include flawed reasoning, incomplete knowledge, faulty information gathering or interpretation, and inappropriate use of decision-making heuristics. We describe a new approach, case-based fuzzy cognitive maps, for medical diagnosis and evaluate it by comparison with Bayesian belief networks. We created a semantic web framework that supports the two reasoning methods. We used database of 174 anonymous patients from several European hospitals: 80 of the patients were female and 94 male with an average age 45±16 (average±stdev). Thirty of the 80 female patients were pregnant. For each patient, signs/symptoms/observables/age/sex were taken into account by the system. We used a statistical approach to compare the two methods. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Accuracy and Reliability of Eye-Based vs Quadrant-Based Diagnosis of Plus Disease in Retinopathy of Prematurity.

PubMed

Kim, Sang Jin; Campbell, J Peter; Kalpathy-Cramer, Jayashree; Ostmo, Susan; Jonas, Karyn E; Choi, Dongseok; Chan, R V Paul; Chiang, Michael F

2018-06-01

Presence of plus disease in retinopathy of prematurity is the most critical element in identifying treatment-requiring disease. However, there is significant variability in plus disease diagnosis. In particular, plus disease has been defined as 2 or more quadrants of vascular abnormality, and it is not clear whether it is more reliably and accurately diagnosed by eye-based assessment of overall retinal appearance or by quadrant-based assessment combining grades of 4 individual quadrants. To compare eye-based vs quadrant-based diagnosis of plus disease and to provide insight for ophthalmologists about the diagnostic process. In this multicenter cohort study, we developed a database of 197 wide-angle retinal images from 141 preterm infants from neonatal intensive care units at 9 academic institutions (enrolled from July 2011 to December 2016). Each image was assigned a reference standard diagnosis based on consensus image-based and clinical diagnosis. Data analysis was performed from February 2017 to September 2017. Six graders independently diagnosed each of the 4 quadrants (cropped images) of the 197 eyes (quadrant-based diagnosis) as well as the entire image (eye-based diagnosis). Images were displayed individually, in random order. Quadrant-based diagnosis of plus disease was made when 2 or more quadrants were diagnosed as indicating plus disease by combining grades of individual quadrants post hoc. Intragrader and intergrader reliability (absolute agreement and κ statistic) and accuracy compared with the reference standard diagnosis. Of the 141 included preterm infants, 65 (46.1%) were female and 116 (82.3%) white, and the mean (SD) gestational age was 27.0 (2.6) weeks. There was variable agreement between eye-based and quadrant-based diagnosis among the 6 graders (Cohen κ range, 0.32-0.75). Four graders showed underdiagnosis of plus disease with quadrant-based diagnosis compared with eye-based diagnosis (by McNemar test). Intergrader agreement of quadrant-based diagnosis was lower than that of eye-based diagnosis (Fleiss κ, 0.75 [95% CI, 0.71-0.78] vs 0.55 [95% CI, 0.51-0.59]). The accuracy of eye-based diagnosis compared with the reference standard diagnosis was substantial to near-perfect, whereas that of quadrant-based plus disease diagnosis was only moderate to substantial for each grader. Graders had lower reliability and accuracy using quadrant-based diagnosis combining grades of individual quadrants than with eye-based diagnosis, suggesting that eye-based diagnosis has advantages over quadrant-based diagnosis. This has implications for more precise definitions of plus disease regarding the criterion of 2 or more quadrants, clinical care, computer-based image analysis, and education for all ophthalmologists who manage retinopathy of prematurity.

Teleradiology Via The Naval Remote Medical Diagnosis System (RMDS)

NASA Astrophysics Data System (ADS)

Rasmussen, Will; Stevens, Ilya; Gerber, F. H.; Kuhlman, Jayne A.

1982-01-01

Testing was conducted to obtain qualitative and quantitative (statistical) data on radiology performance using the Remote Medical Diagnosis System (RMDS) Advanced Development Models (ADMs)1. Based upon data collected during testing with professional radiologists, this analysis addresses the clinical utility of radiographic images transferred through six possible RMDS transmission modes. These radiographs were also viewed under closed-circuit television (CCTV) and lightbox conditions to provide a basis for comparison. The analysis indicates that the RMDS ADM terminals (with a system video resolution of 525 x 256 x 6) would provide satisfactory radiographic images for radiology consultations in emergency cases with gross pathological disorders. However, in cases involving more subtle findings, a system video resolution of 525 x 512 x 8 would be preferable.

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

NASA Technical Reports Server (NTRS)

Rozier, Kristin Y.; Schumann, Johann; Ippolito, Corey

2015-01-01

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

[Dementia in Patients with Central Nervous System Mycosis].

PubMed

Morita, Akihiko; Ishihara, Masaki; Konno, Michiko

2016-04-01

Central nervous system (CNS) mycosis is a potentially life-threatening but treatable neurological emergency. CNS mycoses progress slowly and are sometimes difficult to distinguish from dementia. Though most patients with CNS mycosis have an underlying disease, such as human immunodeficiency virus (HIV) infection, cancer, diabetes mellitus, and/or use of immunosuppressants, cryptococcosis can occur in non-immunosuppressed persons. One of the major difficulties in accurate diagnosis is to detect the pathogen in patients' cerebrospinal fluid (CSF) cultures. Thus, the clinical diagnosis is often made by combining circumstantial evidence, including mononuclear cell-dominant pleocytosis with low glucose and protein elevation in the CSF, as well as positive results from an antigen-based assay and a (1-3)-beta-D-glucan assay using plasma and/or CSF. Polymerase chain reaction (PCR)-based diagnostics, which are not performed as routine examinations and are mostly performed as part of academic research in Japan, are sensitive tools for the early diagnosis of CNS mycosis. Mognetic resonance imaging (MRI) is useful to assess the complications of fungal meningitis, such as abscess, infarction, and hydrocephalus. Clinicians should realize the advantages and disadvantages of these diagnostic tools. Early and accurate diagnosis, including identification of the particular fungal species, enables optimal antifungal treatment that produces good outcomes in patients with CNS mycosis.

Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

PubMed

Dessouky, Mohamed M; Elrashidy, Mohamed A; Taha, Taha E; Abdelkader, Hatem M

2016-05-01

The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer's disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques. © The Author(s) 2015.

Diagnosis and management of primary central nervous system lymphoma.

PubMed

Han, Catherine H; Batchelor, Tracy T

2017-11-15

Primary central nervous system lymphoma (PCNSL) is a rare and aggressive extranodal non-Hodgkin lymphoma (NHL) that is confined to the brain, eyes, spinal cord, or leptomeninges without systemic involvement. The overall prognosis, diagnosis, and management of PCNSL differ from those for other types of NHL. Prompt diagnosis and initiation of treatment are vital for improving clinical outcomes. PCNSL is responsive to radiation therapy; however, whole-brain radiotherapy (WBRT) inadequately controls the disease when it is used alone, and its delayed neurotoxicity causes neurocognitive impairment, especially in elderly patients. High-dose methotrexate (HD-MTX)-based induction chemotherapy with or without autologous stem cell transplantation (ASCT) or reduced-dose WBRT leads to durable disease control and less neurotoxicity. The optimal treatment has yet to be defined; however, HD-MTX-based induction chemotherapy is considered standard for newly diagnosed PCNSL. Ongoing randomized trials are addressing the roles of rituximab and consolidative treatment with ASCT or reduced-dose WBRT. Despite high tumor response rates with the initial treatment, many patients relapse with a very poor prognosis. The optimal treatment for refractory or relapsed PCNSL is poorly defined. The choice of salvage treatment depends on a patient's age, previous treatment and response, performance status, and comorbidities at the time of relapse. This review provides an overview of the clinical features, diagnosis, pathology, and management of PCNSL in immunocompetent patients, and it focuses on recent advances in treatment. Cancer 2017;123:4314-24. © 2017 American Cancer Society. © 2017 American Cancer Society.

Model–Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning

PubMed Central

Twellmann, Thorsten; Meyer-Baese, Anke; Lange, Oliver; Foo, Simon; Nattkemper, Tim W.

2008-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent an important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogeneity of cancerous tissue, these techniques reveal signals with malignant, benign and normal kinetics. They also provide a regional subclassification of pathological breast tissue, which is the basis for pseudo-color presentations of the image data. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging. PMID:19255616

Multi-Agent Diagnosis and Control of an Air Revitalization System for Life Support in Space

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Kowing, Jeffrey; Nieten, Joseph; Graham, Jeffrey s.; Schreckenghost, Debra; Bonasso, Pete; Fleming, Land D.; MacMahon, Matt; Thronesbery, Carroll

2000-01-01

An architecture of interoperating agents has been developed to provide control and fault management for advanced life support systems in space. In this adjustable autonomy architecture, software agents coordinate with human agents and provide support in novel fault management situations. This architecture combines the Livingstone model-based mode identification and reconfiguration (MIR) system with the 3T architecture for autonomous flexible command and control. The MIR software agent performs model-based state identification and diagnosis. MIR identifies novel recovery configurations and the set of commands required for the recovery. The AZT procedural executive and the human operator use the diagnoses and recovery recommendations, and provide command sequencing. User interface extensions have been developed to support human monitoring of both AZT and MIR data and activities. This architecture has been demonstrated performing control and fault management for an oxygen production system for air revitalization in space. The software operates in a dynamic simulation testbed.

Smartphone-based sensing system using ZnO and graphene modified electrodes for VOCs detection.

PubMed

Liu, Lei; Zhang, Diming; Zhang, Qian; Chen, Xing; Xu, Gang; Lu, Yanli; Liu, Qingjun

2017-07-15

Volatile organic compounds (VOCs) detection is in high demand for clinic treatment, environment monitoring, and food quality control. Especially, VOCs from human exhaled breath can serve as significant biomarkers of some diseases, such as lung cancer and diabetes. In this study, a smartphone-based sensing system was developed for real-time VOCs monitoring using alternative current (AC) impedance measurement. The interdigital electrodes modified with zinc oxide (ZnO), graphene, and nitrocellulose were used as sensors to produce impedance responses to VOCs. The responses could be detected by a hand-held device, sent out to a smartphone by Bluetooth, and reported with concentration on an android program of the smartphone. The smartphone-based system was demonstrated to detect acetone at concentrations as low as 1.56ppm, while AC impedance spectroscopy was used to distinguish acetone from other VOCs. Finally, measurements of the exhalations from human being were carried out to obtain the concentration of acetone in exhaled breath before and after exercise. The results proved that the smartphone-based system could be applied on the detection of VOCs in real settings for healthcare diagnosis. Thus, the smartphone-based system for VOCs detection provided a convenient, portable and efficient approach to monitor VOCs in exhaled breath and possibly allowed for early diagnosis of some diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

Development and Comparative Evaluation of a Plate Enzyme-Linked Immunosorbent Assay Based on Recombinant Outer Membrane Antigens Omp28 and Omp31 for Diagnosis of Human Brucellosis

PubMed Central

Tiwari, Sapana; Kumar, Ashu; Mangalgi, Smita; Rathod, Vedika; Prakash, Archana; Barua, Anita; Arora, Sonia; Sathyaseelan, Kannusamy

2013-01-01

Brucellosis is an important zoonotic infectious disease of humans and livestock with worldwide distribution and is caused by bacteria of the genus Brucella. The diagnosis of brucellosis always requires laboratory confirmation by either isolation of pathogens or detection of specific antibodies. The conventional serological tests available for the diagnosis of brucellosis are less specific and show cross-reactivity with other closely related organisms. These tests also necessitate the handling of Brucella species for antigen preparation. Therefore, there is a need to develop reliable, rapid, and user-friendly systems for disease diagnosis and alternatives to vaccine approaches. Keeping in mind the importance of brucellosis as an emerging infection and the prevalence in India, we carried out the present study to compare the recombinant antigens with the native antigens (cell envelope and sonicated antigen) of Brucella for diagnosis of human brucellosis by an indirect plate enzyme-linked immunosorbent assay (ELISA). Recombinant outer membrane protein 28 (rOmp28) and rOmp31 antigens were cloned, expressed, and purified in the bacterial expression system, and the purified proteins were used as antigens. Indirect plate ELISAs were then performed and standardized for comparison of the reactivities of recombinant and native antigens against the 433 clinical samples submitted for brucellosis testing, 15 culture-positive samples, and 20 healthy donor samples. The samples were separated into four groups based on their positivity to rose bengal plate agglutination tests (RBPTs), standard tube agglutination tests (STATs), and 2-mercaptoethanol (2ME) tests. The sensitivities and specificities of all the antigens were calculated, and the rOmp28 antigen was found to be more suitable for the clinical diagnosis of brucellosis than the rOmp31 antigen and native antigens. The rOmp28-based ELISA showed a very high degree of agreement with the conventional agglutination tests and promising results for further use in clinical screening and serodiagnosis of human brucellosis. PMID:23761658

Investigating the quality of expectorated sputum for tuberculosis diagnosis in Bolivia.

PubMed

Hernández, P; Punchak, M; Camacho, M; Hepple, P; McNerney, R

2015-09-01

A low-power microscope-based cytological system to assess the quality of expectorated sputum provided for tuberculosis (TB) diagnosis was piloted in Bolivia. A total of 3688 samples were subjected to visual and cytological examination in nine laboratories: of these, 591 (16%) were misclassified by visual examination and 294 (8%) were found to be degraded. The degree of discordance varied between locations, and laboratories received a higher number of degraded specimens from isolated health clinics. Cytological assessment of sputum was found to be feasible and identified areas for improvement in the Bolivian diagnostic system for TB.

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.

Bacterial detection: from microscope to smartphone.

PubMed

Gopinath, Subash C B; Tang, Thean-Hock; Chen, Yeng; Citartan, Marimuthu; Lakshmipriya, Thangavel

2014-10-15

The ubiquitous nature of bacteria enables them to survive in a wide variety of environments. Hence, the rise of various pathogenic species that are harmful to human health raises the need for the development of accurate sensing systems. Sensing systems are necessary for diagnosis and epidemiological control of pathogenic organism, especially in the food-borne pathogen and sanitary water treatment facility' bacterial populations. Bacterial sensing for the purpose of diagnosis can function in three ways: bacterial morphological visualization, specific detection of bacterial component and whole cell detection. This paper provides an overview of the currently available bacterial detection systems that ranges from microscopic observation to state-of-the-art smartphone-based detection. Copyright © 2014 Elsevier B.V. All rights reserved.

The accuracy of prehospital diagnosis of acute cerebrovascular accidents: an observational study.

PubMed

Karliński, Michał; Gluszkiewicz, Marcin; Członkowska, Anna

2015-06-19

Time to treatment is the key factor in stroke care. Although the initial medical assessment is usually made by a non-neurologist or a paramedic, it should ensure correct identification of all acute cerebrovascular accidents (CVAs). Our aim was to evaluate the accuracy of the physician-made prehospital diagnosis of acute CVA in patients referred directly to the neurological emergency department (ED), and to identify conditions mimicking CVAs. This observational study included consecutive patients referred to our neurological ED by emergency physicians with a suspicion of CVA (acute stroke, transient ischemic attack (TIA) or a syndrome-based diagnosis) during 12 months. Referrals were considered correct if the prehospital diagnosis of CVA proved to be stroke or TIA. The prehospital diagnosis of CVA was correct in 360 of 570 cases. Its positive predictive value ranged from 100% for the syndrome-based diagnosis, through 70% for stroke, to 34% for TIA. Misdiagnoses were less frequent among ambulance physicians compared to primary care and outpatient physicians (33% vs. 52%, p < 0.001). The most frequent mimics were vertigo (19%), electrolyte and metabolic disturbances (12%), seizures (11%), cardiovascular disorders (10%), blood hypertension (8%) and brain tumors (5%). Additionally, 6% of all admitted CVA cases were referred with prehospital diagnoses other than CVA. Emergency physicians appear to be sensitive in diagnosing CVAs but their overall accuracy does not seem high. They tend to overuse the diagnosis of TIA. Constant education and adoption of stroke screening scales may be beneficial for emergency care systems based both on physicians and on paramedics.

The accuracy of prehospital diagnosis of acute cerebrovascular accidents: an observational study

PubMed Central

Gluszkiewicz, Marcin; Członkowska, Anna

2015-01-01

Introduction Time to treatment is the key factor in stroke care. Although the initial medical assessment is usually made by a non-neurologist or a paramedic, it should ensure correct identification of all acute cerebrovascular accidents (CVAs). Our aim was to evaluate the accuracy of the physician-made prehospital diagnosis of acute CVA in patients referred directly to the neurological emergency department (ED), and to identify conditions mimicking CVAs. Material and methods This observational study included consecutive patients referred to our neurological ED by emergency physicians with a suspicion of CVA (acute stroke, transient ischemic attack (TIA) or a syndrome-based diagnosis) during 12 months. Referrals were considered correct if the prehospital diagnosis of CVA proved to be stroke or TIA. Results The prehospital diagnosis of CVA was correct in 360 of 570 cases. Its positive predictive value ranged from 100% for the syndrome-based diagnosis, through 70% for stroke, to 34% for TIA. Misdiagnoses were less frequent among ambulance physicians compared to primary care and outpatient physicians (33% vs. 52%, p < 0.001). The most frequent mimics were vertigo (19%), electrolyte and metabolic disturbances (12%), seizures (11%), cardiovascular disorders (10%), blood hypertension (8%) and brain tumors (5%). Additionally, 6% of all admitted CVA cases were referred with prehospital diagnoses other than CVA. Conclusions Emergency physicians appear to be sensitive in diagnosing CVAs but their overall accuracy does not seem high. They tend to overuse the diagnosis of TIA. Constant education and adoption of stroke screening scales may be beneficial for emergency care systems based both on physicians and on paramedics. PMID:26170845

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

PubMed Central

Tebani, Abdellah; Abily-Donval, Lenaig; Afonso, Carlos; Marret, Stéphane; Bekri, Soumeya

2016-01-01

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era. PMID:27447622

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

EPA Science Inventory

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

Development of a New Diagnostic System for Human Liver Diseases Based on Conventional Ultrasonic Diagnostic Equipment

NASA Astrophysics Data System (ADS)

Kikuchi, Tsuneo; Nakazawa, Toshihiro; Harada, Akimitsu; Sato, Hiroaki; Maruyama, Yukio; Sato, Sojun

2001-05-01

In this paper, the authors present the experimental results of using a quantitative ultrasonic diagnosis technique for human liver diseases using the fractal dimension (FD) of the shape of the power spectra (PS) of RF signals. We have developed an experimental system based on a conventional ultrasonic diagnostic system. As a result, we show that normal livers, fatty livers and liver cirrhosis can be identified using the FD values.

Brazilian Mothers with HIV: Experiences with Diagnosis and Treatment in a Human Rights Based Health Care System

PubMed Central

Jerome, Jessica; Galvao, Marli Teresinha Gimeniz; Lindau, Stacy Tessler

2012-01-01

Drawing on in-depth interviews with a group of urban poor HIV positive mothers in Northeastern Brazil, this essay examines their experiences with HIV medical diagnosis and treatment. It argues that strong social and religious networks as well as the Universal HIV treatment program provide Northeastern Brazilian mothers with forms of support that may be absent in other countries. It further suggests that more research be done to determine how particular forms of health care, such as the human rights based approach Brazil has taken to HIV/AIDS, inform patient-provider relationships. PMID:22150016

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Qualitative mechanism models and the rationalization of procedures

NASA Technical Reports Server (NTRS)

Farley, Arthur M.

1989-01-01

A qualitative, cluster-based approach to the representation of hydraulic systems is described and its potential for generating and explaining procedures is demonstrated. Many ideas are formalized and implemented as part of an interactive, computer-based system. The system allows for designing, displaying, and reasoning about hydraulic systems. The interactive system has an interface consisting of three windows: a design/control window, a cluster window, and a diagnosis/plan window. A qualitative mechanism model for the ORS (Orbital Refueling System) is presented to coordinate with ongoing research on this system being conducted at NASA Ames Research Center.

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

NASA Technical Reports Server (NTRS)

Ong, James C.; Remolina, Emilio; Breeden, David; Stroozas, Brett A.; Mohammed, John L.

2012-01-01

Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking.

Multimedia consultation session recording and playback using Java-based browser in global PACS

NASA Astrophysics Data System (ADS)

Martinez, Ralph; Shah, Pinkesh J.; Yu, Yuan-Pin

1998-07-01

The current version of the Global PACS software system uses a Java-based implementation of the Remote Consultation and Diagnosis (RCD) system. The Java RCD includes a multimedia consultation session between physicians that includes text, static image, image annotation, and audio data. The JAVA RCD allows 2-4 physicians to collaborate on a patient case. It allows physicians to join the session via WWW Java-enabled browsers or stand alone RCD application. The RCD system includes a distributed database archive system for archiving and retrieving patient and session data. The RCD system can be used for store and forward scenarios, case reviews, and interactive RCD multimedia sessions. The RCD system operates over the Internet, telephone lines, or in a private Intranet. A multimedia consultation session can be recorded, and then played back at a later time for review, comments, and education. A session can be played back using Java-enabled WWW browsers on any operating system platform. The JAVA RCD system shows that a case diagnosis can be captured digitally and played back with the original real-time temporal relationships between data streams. In this paper, we describe design and implementation of the RCD session playback.

Distributed Knowledge Base Systems for Diagnosis and Information Retrieval.

DTIC Science & Technology

1983-11-01

social system metaphors State University. for distributed problem solving: Introduction to the issue. IEEE Newell. A. and Simon, H. A. (1972) Human...experts and Sriram Mahalingam wha-helped think out the probLema associated with building Auto-Mech. Research on diagnostic expert systemas for the

Candidate Causes. Sediments. In: Causal Analysis, Diagnosis Decision Information System, USEPA Website

EPA Science Inventory

CADDIS is an online application that helps scientists and engineers in the Regions, States, and Tribes find, access, organize, use, and share information to conduct causal evaluations in aquatic systems. It is based on the USEPA stressor identification process, a formal method fo...

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

NASA Technical Reports Server (NTRS)

Hayden, Sandra C.; Sweet, Adam J.; Shulman, Seth

2005-01-01

The Livingstone 2 (L2) model-based diagnosis software is a reusable diagnostic tool for monitoring complex systems. In 2004, L2 was integrated with the JPL Autonomous Sciencecraft Experiment (ASE) and deployed on-board Goddard's Earth Observing One (EO-1) remote sensing satellite, to monitor and diagnose the EO-1 space science instruments and imaging sequence. This paper reports on lessons learned from this flight experiment. The goals for this experiment, including validation of minimum success criteria and of a series of diagnostic scenarios, have all been successfully net. Long-term operations in space are on-going, as a test of the maturity of the system, with L2 performance remaining flawless. L2 has demonstrated the ability to track the state of the system during nominal operations, detect simulated abnormalities in operations and isolate failures to their root cause fault. Specific advances demonstrated include diagnosis of ambiguity groups rather than a single fault candidate; hypothesis revision given new sensor evidence about the state of the system; and the capability to check for faults in a dynamic system without having to wait until the system is quiescent. The major benefits of this advanced health management technology are to increase mission duration and reliability through intelligent fault protection, and robust autonomous operations with reduced dependency on supervisory operations from Earth. The work-load for operators will be reduced by telemetry of processed state-of-health information rather than raw data. The long-term vision is that of making diagnosis available to the onboard planner or executive, allowing autonomy software to re-plan in order to work around known component failures. For a system that is expected to evolve substantially over its lifetime, as for the International Space Station, the model-based approach has definite advantages over rule-based expert systems and limit-checking fault protection systems, as these do not scale well. The model-based approach facilitates reuse of the L2 diagnostic software; only the model of the system to be diagnosed and telemetry monitoring software has to be rebuilt for a new system or expanded for a growing system. The hierarchical L2 model supports modularity and expendability, and as such is suitable solution for integrated system health management as envisioned for systems-of-systems.

Sclerosing Cholangitis: Clinicopathologic Features, Imaging Spectrum, and Systemic Approach to Differential Diagnosis.

PubMed

Seo, Nieun; Kim, So Yeon; Lee, Seung Soo; Byun, Jae Ho; Kim, Jin Hee; Kim, Hyoung Jung; Lee, Moon-Gyu

2016-01-01

Sclerosing cholangitis is a spectrum of chronic progressive cholestatic liver disease characterized by inflammation, fibrosis, and stricture of the bile ducts, which can be classified as primary and secondary sclerosing cholangitis. Primary sclerosing cholangitis is a chronic progressive liver disease of unknown cause. On the other hand, secondary sclerosing cholangitis has identifiable causes that include immunoglobulin G4-related sclerosing disease, recurrent pyogenic cholangitis, ischemic cholangitis, acquired immunodeficiency syndrome-related cholangitis, and eosinophilic cholangitis. In this review, we suggest a systemic approach to the differential diagnosis of sclerosing cholangitis based on the clinical and laboratory findings, as well as the typical imaging features on computed tomography and magnetic resonance (MR) imaging with MR cholangiography. Familiarity with various etiologies of sclerosing cholangitis and awareness of their typical clinical and imaging findings are essential for an accurate diagnosis and appropriate management.

Modular expert system for the diagnosis of operating conditions of industrial anaerobic digestion plants.

PubMed

Lardon, L; Puñal, A; Martinez, J A; Steyer, J P

2005-01-01

Anaerobic digestion (AD) plants are highly efficient wastewater treatment processes with possible energetic valorisation. Despite these advantages, many industries are still reluctant to use them because of their instability in the face of changes in operating conditions. To the face this drawback and to enhance the industrial use of anaerobic digestion, one solution is to develop and to implement knowledge base (KB) systems that are able to detect and to assess in real-time the quality of operating conditions of the processes. Case-based techniques and heuristic approaches have been already tested and validated on AD processes but two major properties were lacking: modularity of the system (the knowledge base system should be easily tuned on a new process and should still work if one or more sensors are added or removed) and uncertainty management (the assessment of the KB system should remain relevant even in the case of too poor or conflicting information sources). This paper addresses these two points and presents a modular KB system where an uncertain reasoning formalism is used to combine partial and complementary fuzzy diagnosis modules. Demonstration of the interest of the approach is provided from real-life experiments performed on an industrial 2,000 m3 CSTR anaerobic digester.

A modular neural network scheme applied to fault diagnosis in electric power systems.

PubMed

Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

2014-01-01

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

PubMed Central

Flores, Agustín; Morant, Francisco

2014-01-01

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis-Taguchi system

NASA Astrophysics Data System (ADS)

Chen, Junxun; Cheng, Longsheng; Yu, Hui; Hu, Shaolin

2018-01-01