Diagnostic Expert System Research Articles

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

BackgroundThe usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work.MethodsThe database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound.ResultsThe architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%.ConclusionThe results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.

Implementation on ADHD Diagnostic Expert System based on DSM Diagnostic Criteria

Implementation on ADHD Diagnostic Expert System based on DSM Diagnostic Criteria

A Prioritizing for the Evidence-Based Nursing Practice Guidelines Development

A Prioritizing for the Evidence-Based Nursing Practice Guidelines Development

Implementation on ADHD Diagnostic Expert System based on DSM Diagnostic Criteria

Implementation on ADHD Diagnostic Expert System based on DSM Diagnostic Criteria

An expert system for diagnostics and estimation of steam turbine components condition

The report describes an expert system of probability type for diagnostics and state estimation of steam turbine technological subsystems components. The expert system is based on Bayes’ theorem and permits to troubleshoot the equipment components, using expert experience, when there is a lack of baseline information on the indicators of turbine operation. Within a unified approach the expert system solves the problems of diagnosing the flow steam path of the turbine, bearings, thermal expansion system, regulatory system, condensing unit, the systems of regenerative feed-water and hot water heating. The knowledge base of the expert system for turbine unit rotors and bearings contains a description of 34 defects and of 104 related diagnostic features that cause a change in its vibration state. The knowledge base for the condensing unit contains 12 hypotheses and 15 evidence (indications); the procedures are also designated for 20 state parameters estimation. Similar knowledge base containing the diagnostic features and faults hypotheses are formulated for other technological subsystems of turbine unit. With the necessary initial information available a number of problems can be solved within the expert system for various technological subsystems of steam turbine unit: for steam flow path it is the correlation and regression analysis of multifactor relationship between the vibration parameters variations and the regime parameters; for system of thermal expansions it is the evaluation of force acting on the longitudinal keys depending on the temperature state of the turbine cylinder; for condensing unit it is the evaluation of separate effect of the heat exchange surface contamination and of the presence of air in condenser steam space on condenser thermal efficiency performance, as well as the evaluation of term for condenser cleaning and for tube system replacement and so forth. With a lack of initial information the expert system enables to formulate a diagnosis, calculating the probability of faults hypotheses, given the degree of the expert confidence in estimation of turbine components operation parameters.

New Measurements Results Achieve for Proactive Maintenace with Vibro-Expert Diagnosis System

Abstract One important objectives of proactive maintenance are the knowledge of the technical state of machinery, the intelligent planning of interventions according to the diagnosis technique, identification and correction of assembling errors. The aim of this paper is to present a new case on the vibrations diagnosis of hydro-aggregate CAMPELA - CHE Dubasari (MOLDOVA). The VIBRO EXPERT diagnostic system used on the CAPMELA hydro-aggregate is designed to measure, monitor, diagnose, and analyze the technical and functional parameters in order to safe use of the hydro-aggregate turbine, using the vibrations data measurement. The proactive maintenance concept is being used more and more thanks to the development of these intelligent monitoring and control systems, in order to prevent interruptions due to major failures.

Scopus as a Meta-Source of Knowledge About Turbine Blade Damage in the Aspect of Designing an Expert Diagnostic System

Abstract The paper presents the concept of using the Scopus as a meta-source of knowledge about turbine blade damage in the aspect of designing an expert diagnostic system. In the first stage, the search was limited to the scope of the general term “turbine engine”, followed by a refinement of the search terms within the area of rotary machines components degradation including their construction, manufacturing, repair technology and diagnostic methods. By using EndNote software in semi-automatic mode, specific issue groups have been designated. In the second stage, a query focused on the main causes of turbine blade damage and diagnostic methods was proposed. Using the Scopus-based search and archiving tools, one can systematically update the knowledge.

Evaluation to determine the efficiency for the diagnosis search formation method of failures in automated systems

This paper describes the results of work in the field of failure self-diagnostics for automated systems in real time to increase the efficiency of their operation. We describe the method developed of a diagnosis search formation space by applying to the Expert System Knowledge Base to diagnose failures in automated systems. The input data for the Expert Diagnostic System is a conflicting set of diagnostic codes generated by the automated system over the time interval ∆t during its operation. We proposed mathematical methods to work with a data structure “m-tuples based on ordinary sets of arbitrary cardinality n” to process the input data. We conducted a comparative analysis to estimate the execution time of algorithms for the diagnosis search formation space using sequential access to the Boolean of input data and using the method developed. The analysis showed that the application of the proposed method changes the functional dependency of the execution time estimation of the algorithm in accordance with the number of its input data n from exponential to cubic. The application of the method developed allows us to minimize the time needed to establish the diagnosis to real time. The method presented to diagnose automated systems allows creating methods and algorithms for automatic self-recovery of their operability after reversible failures in real time

Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics

A large amount of data is generated through monitoring, maintenance, repair and diagnostics of power transformer. However, all these data cannot preindicate the exact type and probability of failure. To overcome the problem this paper presents artificial intelligence based methodology for power transformers fault detection and classification. The possibility of presented monitoring methodology is to assist the operator’s engineers in decision making about urgency of intervention and type of maintenance of power transformer. The article analyzes the application of Mamdani-model and Sugeno-model in fuzzy expert system for fault diagnosis based on the current state of the power transformer. Paper presents two case studies with one unique and five separate controllers. In the first case inputs of controller are results of on-line and off-line transformer tests: age, the overheating temperature of the hot spot, frequency response analysis, temperature of insulation, dissolved gas-in-oil analysis, tgδ and polarization index. Second case study in addition to the existing inputs includes previous measurements. A fuzzy controller (FC) is designed to characterize the operating condition and to determine the urgency of intervention with possibility to indicate probability of specific type of failure. Cumulative probability of occurrence of the faults is also observed in second case study. FCs are tested based on real measurements from Serbian transmission system. The results show acceptable effectiveness in detecting different faults and might serve as a good orientation in the power transformer condition monitoring.

Expert System for Diagnostics of Faults of Gas Turbines at Gas-Compressor Stations

Описано существующее состояние дел в области диагностики неисправностей газоперекачивающих агрегатов (ГПА) на компрессорных станциях, показано, что используемое программное обеспечение своевременно диагностирует неисправности ГПА, но не определяет причины их появления и, следовательно, не повышает эффективность их устранения. В статье предложен способ решения проблемы диагностики причин неисправностей ГПА за счет внедрения экспертной системы GASDETECT, разработанной на основе экспертных и ретроспективных данных ООО «Газпром трансгаз Саратов», описана структура системы, приведены примеры нечетких правил, для принятия решения используется нечеткий алгоритм Мамдани.

Построение прототипа экспертно-диагностической системы анализа траекторной измерительной информации КА

Построение прототипа экспертно-диагностической системы анализа траекторной измерительной информации КА

Keyword spotting in doctor's handwriting on medical prescriptions

In this paper, we propose a word spotting based information retrieval approach for medical prescriptions/reports written by doctors. Sometimes due to almost illegible handwriting, it is difficult to understand the medication reports of doctors. This often confuses the patients about the actual medicine/disease names written by doctors and as a consequence they suffer. A medical prescription is generally partitioned into two parts, a printed letterhead part containing the doctor's name, designation, organization name, etc. and a handwritten part where the doctor writes patient's name and report his/her findings and suggests medicine names. There are many significance impacts of the proposed work. For example, such work can be used (i) to develop expert diagnostic systems (ii) to extract information from patient history that can be obtained by this proposed method (iii) to detect wrong medication (iv) to make different statistical analysis of the medicines prescribed by the doctors etc. To extract the information from such document images, first we extract the domain specific knowledge of doctors by identifying department names from the printed text that appears in letterhead part. From the letterhead text, the specialty/expertise of doctors is understood and this helps us to search only relevant prescription documents for word spotting in handwritten portion. Word spotting in letterhead part as well as in handwritten part has been performed using Hidden Markov Model. An efficient MLP (Multilayer Perceptron) based Tandem feature is proposed to improve the performance. From the experiment with 500 prescriptions, we have obtained encouraging results. Information from printed letterhead part improved the word spotting performance in handwritten part, significantly.

The expert system virtual ophthalmologist

The article is dedicated to creating expert systems in the field of ophthalmology. "Virtual ophthalmology" is a Practice-Based Diagnostic Expert System covering the initial diagnostics of outpatient patients in the field of ophthalmology expert systems The adjustment of time series of tax revenues and relevant computer simulation of them was conducted by the fuzzy numbers according to the expert evaluation.

IPES: An Image Processing-Enabled Expert System for the Detection of Breast Malignant Tumors

Mammography is an influential screening tool for preliminary detection of breast carcinoma. However, Interpreting mammograms is exhausting, particularly in the screening context. Moreover, sensitivity of mammography based screening is influenced by image quality and the experience level of radiologist. Thus, computer-aided diagnosis (CAD) programs can be utilized as a second-opinion tools that enhance the performance of radiologists, by consolidating sensitivity rates in contrast with those taken by double readings. The current paper is directed towards the integration of image processing and the rule-based reasoning into a diagnostic expert system for breast tumors. A proposed system termed IPES (Image Processing-enabled Expert System) is developed for the detection of breast malignant tumors, appear in mammography in three steps: (1) segmentation of mammographic masses from both pectoral muscle region and breast tissues, (2) Characterization of segmented masses upon the standards of Breast Imaging Reporting and Data System (BI-RADS) by: (i) shape-relevant features; (ii) margin characteristics; and (iii) density features, and (3) diagnosis of mass type upon some inference rules. The data set used for testing IPES contained 540 samples obtained from the standard Digital Database for Screening Mammography (DDSM). The Receiver Operator Characteristic (ROC) curves have been employed to evaluate the sensitivities and specificities of the system. Finally, the results reveal the efficacy of IBES in discriminating both malignant and benign breast masses.

Експертне діагностування комп'ютерних систем з використанням нейронечіткої бази знань

The functional diagnosis expert system based on knowledge base in the form of neuro-fuzzy network was proposed. Current values of diagnostic parameters are measured by sensors for technical object. The hybrid expert diagnostic system with neuro-fuzzy network knowledge base supports decisions in the situation when the diagnosis algorithm is not known and is formed from the initial data in the form of production rules. Sensors use to automate the process of accumulation of knowledge in the expert system. Structuring raw data is performed using the temporal decision trees. The need to take decisions in real time results the number of trees corresponding to incoming data, equal to the number of samples during the observation period. The problem of large amounts of data in determining the technical condition of the complex technical object is solved by using this data as a training sample for the knowledge base. .The expert diagnosis system for assessing the PC performance was considered how the example.

A Set Covering-Based Diagnostic Expert System to Economic and Financial Applications

This paper considers the identification of problems which generate anomalies at firms through the observed symptoms on the basis of fuzzy relations and Zadeh's compositional rule of inference. A procedure for determining the fuzzy cause vector of an economic and financial diagnosis problem is proposed, which consists of the design of fuzzy relational matrix and the resolution of a system of fuzzy relational equations. An efficient algorithm for solving fuzzy relational equations in terms of the associated set covering problem is introduced. It utilizes a back-tracking method to generate each minimal covering, where no duplicate or non-minimal coverings exist. A numerical example of firms' insolvency causes diagnosis is also included.

The results of rehabilitation of the patients presenting with arterial hypertension: the types of adaptation, the vegetative status, and intracardial hemodynamics

The objective the present work was to study the changes of the types of adaptation in the patients presenting with arterial hypertension under the influence of the spa and health resort-based rehabilitative treatment. The study included 50 patients with essential hypertension at the age from 54.0 to 62.0 (mean 58.0) years given a course of the spa and health resort-based rehabilitative treatment. All the patients underwent electrocardiographic and echocardiographic examination for the assessment of the systolic and diastolic left ventricular functions. Cardiointervalography with the use of the ORTO Expert diagnostic system was performed in the patients at rest and during the active orthostatic test (AOT); the types of the adaptation were evaluated. The patients were divided into 2 groups depending on the results of repeated AOT obtained after rehabilitation. Group 1 was comprised of the patients showing the improvement of adaptation (n=24), group 2 consisted of the patients in whom the level of adaptation remained unaltered (n=26). The structural characteristics of echocardiography estimated in both groups were not significantly different. The number of patients in group 1 showing the satisfactory level of adaptation intensity in the absence of its failures increased significantly. In group 2, a significant decrease of the following AOT parameters was documented: the tension index of the regulatory system (p=0.02), the standard deviation of NN-intervals (SDNN) (p=0.03), and root mean square of successive differences (RMSSD) (p=0.02). The multiple logistic regression analysis showed that the factors associated with the positive effect of rehabilitation were the normal and low degree of regulatory system tension (p=0.01) and the initially adequate or reduced response in the orthostatic test (p=0.04). The improvement of rehabilitation of the adaptive capacity was documented in as many as 48% of the patients who had undergone the spa and health resort-based rehabilitative treatment. The absence of positive dynamics or deterioration of adaptation were associated with the increase of the sympathetic activity at rest and in response to the AOT. The beneficial effect of rehabilitation was observed in the patients with the initially normal or low tension of regulatory systems and with the adequate or reduced baseline response in AOT.

Construction features of procedural simulator for objective control expert systems management devices of rocket and space technology ground infrastructure objects

Paper refers to the construction of procedural simulators for expert systems management devices of rocket and space technology ground infrastructure objects. A classification of courses, which allows to realize the development of skills with both hardware and software components of a diagnostic expert systems. A method of calculating the integrated assessment of the student mastery of the material.

Analytical Comparison of Mathematical Modeling in the Diagnostic Expert Systems

Analytical Comparison of Mathematical Modeling in the Diagnostic Expert Systems

THE DEVELOPMENT OF OPTIMIZATION METHODS FOR KNOWLEDGE BASE ENRICHMENT PROCESSES

The paper presents the concept of approach to the research and evaluation of the processes of intellectual activity associated with the enrichment of the knowledge base. A feature of the research of the process dynamics is the need of simultaneous consideration of such diverse factors as the complexity of information perception, the presence of the deviations of the response from the standard in the process of reproduction and accounting of the test time. A significant influence on the methods of optimization of the knowledge base enrichment process is exerted by a considerable duration of the task learning process. This causes the use of the multifactor experimental design theory to accelerate the progress towards the optimum. The research results can be used in the development of technologies for efficient knowledge assimilation, automation of skills, and also in the development of expert systems for diagnostics of the processes of intellectual activity.