Mathematical Models for the Differential Diagnosis of Various Forms of Pyelonephritis by Expert Systems in Urology

2020 ◽  
Vol 53 (6) ◽  
pp. 437-439
Author(s):  
N. А. Korenevskiy ◽  
G. V. Siplivyi ◽  
D. S. Rodionov ◽  
T. N. Govorukhina ◽  
V. V. Dmitrieva
Keyword(s):  
Differential Diagnosis ◽  
Expert Systems ◽  
Mathematical Models

Building an Expert Critiquing System: ESSENTIAL-ATTENDING

1986 ◽  
Vol 25 (02) ◽  
pp. 71-78 ◽  
Author(s):  
P. L. Miller
Keyword(s):  
Differential Diagnosis ◽  
Expert Systems ◽  
Patient Care ◽  
Medical Management ◽  
System Building ◽  
Building System ◽  
Critiquing Systems

SummaryThis paper describes a “system-building system”, ESSENTIAL-ATTENDING (E-ATTEND-ING), designed to assist in the implementation of expert systems which critique a physician’s plan for patient care. E-ATTENDING has been refined during the implementation of several developmental critiquing systems and may be applied in areas of medical management, patient workup, and differential diagnosis. E-ATTENDING is currently designed to help implement a class of critiquing systems in a subset of possible critiquing domains. It can also be augmented in various ways by interested users to accommodate critiquing domains with more complexity.

DEVELOPMENT OF A SUBSYSTEM FOR DIAGNOSTICS OF GYNECOLOGICAL DISEASES BASED ON STATISTICAL AND SIMULATION MODELING

2021 ◽  
pp. 94-98
Author(s):  
Екатерина Ивановна Новикова ◽  
Анастасия Юрьевна Корниенко
Keyword(s):  
Differential Diagnosis ◽  
Mathematical Models ◽  
Simulation Modeling ◽  
Clinical Laboratory ◽  
Software Product ◽  
Gynecological Diseases ◽  
Medical Structure ◽  
Diagnostic Signs ◽  
Attending Physician ◽  
Probabilistic Nature

В статье рассматриваются методы кластерного и дискриминантного анализа для построения математических моделей диагностики гинекологических заболеваний. Гинекологические патологии занимают значительное место в структуре заболеваемости у женщин. Между тем, точная дифференциальная диагностика патологий зачастую бывает, невозможна, так как гинекологические заболевания носят вероятностный характер, большинство диагностических признаков выражаются качественными показателями, которые индивидуальны для каждой пациентки. Лечащему врачу приходится решать сложную задачу по анализу клинических, лабораторных и инструментальных признаков для постановки точного диагноза. С применением аппарата сетей Петри произведено построения модели дифференциальной диагностики гинекологических заболеваний. На основе полученных математических моделей, сформирована структура и информационно-программное обеспечение для системы диагностики гинекологических заболеваний в медицинских организациях. Внедрение разработанного программного продукта в медицинскую структуру позволит уменьшить вероятность врачебной ошибки, а также повысить эффективность и точность постановки диагноза пациенткам The article discusses the methods of cluster and discriminant analysis for constructing mathematical models for the diagnosis of gynecological diseases. Gynecological pathologies occupy a significant place in the structure of morbidity in women. Meanwhile, accurate differential diagnosis of pathologies is often impossible, since gynecological diseases are of a probabilistic nature, most of the diagnostic signs are expressed in qualitative indicators that are individual for each patient. The attending physician has to solve a complex task of analyzing clinical, laboratory and instrumental signs to make an accurate diagnosis. Using the apparatus of Petri nets, a model for the differential diagnosis of gynecological diseases was constructed. On the basis of the obtained mathematical models, the structure and information software for the system of diagnostics of gynecological diseases in medical organizations was formed. The introduction of the developed software product into the medical structure will reduce the likelihood of medical error, as well as increase the efficiency and accuracy of diagnosing patients

Improvement of Methods for the Study of Population Health Using Algebraic Models of Constructive Logic

2015 ◽  
Vol 22 (3) ◽  
pp. 8-14 ◽  
Author(s):  
Хромушин ◽  
Viktor Khromushin ◽  
Китанина ◽  
K. Kitanina ◽  
Аверьянова ◽  
...  
Keyword(s):  
Expert Systems ◽  
Mathematical Models ◽  
Population Health ◽  
Algebraic Model ◽  
Compact Representation ◽  
Constructive Logic ◽  
Algebraic Models ◽  
Source Data ◽  
Support Of Research ◽  
The Mathematical Model

Algebraic model of constructive logic is developed in Russia and is used for many years in medicine and biology for multivariate analysis and for building expert systems. In the process of improving the algorithm of the algebraic model of constructive logic and software, the methods of the study of population health with the use of these models are improved. The tasks of providing a compact representation of the mathematical model are solved, the version of algorithms and programs with different reaction to incomplete source data is created, an analytical and methodological support of research is developed. The article presents the results of practical work to improve the working methods of the study of population health. It covers the issues of verification of source data, an obtainment a compact mathematical models, the valuation and completeness of the source data, the main highlight of the resulting components, the exclusion of inconsistencies in the source data, the absorption of the analyzed factors, the principles of the analysis of the factors in mathematical models and principles of construction of expert systems. The authors showed that the classical and modernized versions of the algebraic model of constructive logic have their applications and are not exclusive of each other. This article also provides recommendations and explanations that facilitate the realization of analytical studies using algebraic models of constructive logic.

DIFFERENTIAL DIAGNOSIS OF PANCREATIC PATHOLOGY ACCORDING TO ENDOSCOPIC ULTRASONOGRAPHY BASED ON FUZZY MATHEMATICAL MODELS

2021 ◽  
pp. 102-110
Author(s):  
Владимир Анатольевич Белозеров ◽  
Николай Алексеевич Кореневский
Keyword(s):  
Differential Diagnosis ◽  
Mathematical Models ◽  
Endoscopic Ultrasonography ◽  
Statistical Tests ◽  
Ductal Adenocarcinoma ◽  
Pancreatic Tumors ◽  
Ultrasound Diagnostics ◽  
Adenocarcinoma Of The Pancreas ◽  
Pancreatic Pathology

Эндоскопическая ультрасонография, являясь высокоинформативным и уточняющим методом диагностики патологии поджелудочной железы остается в значительной мере субъективным и операторзависимым исследованием. Целью работы явилось повышение качества дифференциальной диагностики очаговой патологии поджелудочной железы на основе анализа эталонных эндосонограмм за счет использования гибридных нечетких математических моделей, при синтезе которых формальные процедуры строятся с использованием клинического опыта высококвалифицированных специалистов ультразвуковой диагностики. В отделении эндоскопии Курской областной клинической больницы эндоскопическая ультрасонография выполнена у 729 пациентов. Опухоли поджелудочной железы выявлены у 139 (19,1%) пациентов, различные формы хронического панкреатита диагностированы у 141 (19,3%) больного. По результатам исследований выделены 6 типов эталонных эндосонограмм, соответствующих дифференцируемой патологии, на основании которых, с использованием гибридных нечетких математических решающих правил, разработанных в Юго-Западном государственном университете, получена нечеткая гибридная модель дифференциальной диагностики хронического очагового панкреатита и протоковой аденокарциномы поджелудочной железы. По результатам математического моделирования и статистических испытаний на репрезентативных контрольных выборках было показано, что полученная модель дифференциальной диагностики, использующая только эталонные эндосонограммы, обеспечивает уверенность в искомом диагнозе на уровне 0,6. Дополнительная информация, получаемая при анализе эндосонограмм, повышает диагностическую уверенность до величины 0,9. Таким образом, полученная модель дифференциальной диагностики хронического очагового панкреатита и протоковой аденокарциномы поджелудочной железы обеспечивает приемлемое для клинической практики качество принимаемых решений Endoscopic ultrasonography is a highly informative and clarifying method for diagnosing pancreatic pathology. At the same time, it remains to a large extent subjective and operator-dependent research. The aim of this work is to improve the quality of differential diagnosis of focal pancreatic pathology based on the analysis of reference endosonograms through the use of hybrid fuzzy mathematical models. When synthesizing these models, formal procedures are built using the clinical experience of highly qualified specialists in ultrasound diagnostics. In the endoscopy department of the Kursk Regional Clinical Hospital, endoscopic ultrasonography was performed in 729 patients. Pancreatic tumors were detected in 139 (19.1%) patients, various forms of chronic pancreatitis were diagnosed in 141 (19.3%) patients. Based on the research results, 6 types of reference endosonograms corresponding to differentiable pathology were identified. On the basis of endosonograms, using hybrid fuzzy mathematical decision rules developed at Southwestern State University, a fuzzy hybrid model of differential diagnosis of chronic focal pancreatitis and ductal adenocarcinoma of the pancreas was obtained. Based on the results of mathematical modeling and statistical tests on representative control samples, it was shown that the resulting model of differential diagnosis, using only reference endosonograms, provides confidence in the desired diagnosis at the level of 0.6. Additional information obtained from the analysis of endosonograms increases the diagnostic confidence to a value of 0.9. Thus, the model obtained for the differential diagnosis of chronic focal pancreatitis and ductal adenocarcinoma of the pancreas provides an acceptable quality of decisions for clinical practice

Building an Expert Critiquing System: ESSENTIAL-ATTENDING

1986 ◽  
Vol 25 (02) ◽  
pp. 71-78
Author(s):  
P. L. Miller
Keyword(s):  
Differential Diagnosis ◽  
Expert Systems ◽  
Patient Care ◽  
Medical Management ◽  
System Building ◽  
Building System ◽  
Critiquing Systems

SummaryThis paper describes a “system-building system”, ESSENTIAL-ATTENDING (E-ATTEND-ING), designed to assist in the implementation of expert systems which critique a physician’s plan for patient care. E-ATTENDING has been refined during the implementation of several developmental critiquing systems and may be applied in areas of medical management, patient workup, and differential diagnosis. E-ATTENDING is currently designed to help implement a class of critiquing systems in a subset of possible critiquing domains. It can also be augmented in various ways by interested users to accommodate critiquing domains with more complexity.

scholarly journals Predicting various forms of endometriosis using artificial neural networks

2020 ◽  
Vol 35 (4) ◽  
pp. 143-149
Author(s):  
N. G. Sazonova ◽  
T. A. Makarenko ◽  
A. N. Narkevich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Differential Diagnosis ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Computer System ◽  
Clinical Manifestations ◽  
Diagnostic System ◽  
Artificial Neural ◽  
Computer Diagnostic System

Introduction. Endometriosis is a difficult-to-diagnose pathology due to the diversity of clinical manifestations and the lack of high-precision markers necessary for rapid noninvasive diagnosis and timely administration of pathogenetically justified treatment.The aim of this work was to develop a computer system that allows us to assess the probability of endometriosis with various localizations in women, based on artificial neural networks.Material and Methods. The neural network mathematical models were constructed and tested based on data from 110 patients with morphologically pre-confirmed endometriosis. Patients were divided into training and test samples. The models were built based on anamnestic data and results of proteomic and enzyme immunoassays in blood plasma samples.Results and Discussion. In the course of the study, four mathematical models of neural networks were constructed to predict the presence or absence of endometriosis in a woman and its localization if present. Based on these mathematical models, a computer system “Differential diagnosis of endometriosis” was developed. This system allowed to assess the probability and localization of endometriosis in a patient based on parameters obtained as a result of neural network training.Conclusion. The developed computer diagnostic system allowed predicting the presence of endometriosis and its localization with a probability over 80%, depending on the predicted localization, based on data about the patient and the results of her examination. This system may be used for differential diagnosis of endometriosis from other diseases of the female reproductive system, as well as for differential diagnosis of various endometriosis localizations.

scholarly journals Differential Diagnostics Focal Pancreatics Masses Based on the Analysis of the Echographic Texture of Endosonograms Using Fuzzy Mathematical Models

2021 ◽  
Vol 4 (3) ◽  
pp. 64-73
Author(s):  
V. A. Belozerov ◽  
O. I. Ohotnikov ◽  
N. A. Korenevskij ◽  
S. N. Grigorev
Keyword(s):  
Differential Diagnosis ◽  
Mathematical Models ◽  
Hybrid Model ◽  
Endoscopic Ultrasonography ◽  
Statistical Tests ◽  
Decision Rules ◽  
Ductal Adenocarcinoma ◽  
Differential Diagnostics ◽  
Pancreatic Masses ◽  
Adenocarcinoma Of The Pancreas

Relevance: Differential diagnosis of focal formations of the pancreas is particularly difficult due to the similarity of their echosemiotics. One of the ways to objectify and improve the accuracy of ultrasound data is to use artificial intelligence methods to interpret images.Purpose: Improving the quality of diagnosis of focal pancreatic pathology according to endoscopic ultrasonography based on the analysis of the echographic texture using fuzzy mathematical models.Material and methods: In the Kursk Regional Clinical Hospital, endoscopic ultrasonography was performed in 272 patients for pancreaticobiliary diseases. The endoscopic video system of the company Olympus EVIS EXERA II with the ultrasonic processor EU-ME1 was used. Solid tumors of the pancreas were detected in 109 (40.1 %) patients, focal pancreatic masses were diagnosed in 40 (14.7 %) patients. Based on the research results, the main types of reference endosonograms corresponding to differentiable pathology were identified. On the basis of endosonograms, using hybrid fuzzy mathematical decision rul, a fuzzy hybrid model of differential diagnosis of chronic focal pancreatitis and ductal adenocarcinoma of the pancreas was obtained.Results: The selected source of information and the method of synthesis of hybrid fuzzy decision rules made it possible to obtain a fuzzy hybrid model of differential diagnosis of chronic focal pancreatitis and ductal adenocarcinoma of the pancreas. Based on the results of mathematical modeling and statistical tests on representative control samples, it was shown that the resulting model of differential diagnosis, using reference endosonograms reflecting the echographic texture of focal pancreatic masses, provides confidence in the desired diagnosis at the level of 0.6. The additional information used in the analysis of endosonograms increases the diagnostic confidence to a value of 0.9.Conclusions: The resulting model of differential diagnosis of chronic focal pancreatitis and ductal adenocarcinoma of the pancreas provides confidence in decision-making no worse than 0.9, which is an acceptable quality indicator in clinical practice in conditions of insufficient statistics with poorly formalized data structure. 

scholarly journals The use of expert systems on the differential diagnosis of urinary incontinence

2009 ◽  
Vol 43 (3) ◽  
pp. 704-710 ◽  
Author(s):  
Maria Helena Baena de Moraes Lopes ◽  
Heimar de Fátima Marin ◽  
Neli Regina Siqueira Ortega ◽  
Eduardo Massad
Keyword(s):  
Differential Diagnosis ◽  
Urinary Incontinence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Health Professionals ◽  
Special Equipment ◽  
Urodynamic Testing ◽  
Trained Personnel ◽  
Definition Of

The differential diagnosis of urinary incontinence classes is sometimes difficult to establish. As a rule, only the results of urodynamic testing allow an accurate diagnosis. However, this exam is not always feasible, because it requires special equipment, and also trained personnel to lead and interpret the exam. Some expert systems have been developed to assist health professionals in this field. Therefore, the aims of this paper are to present the definition of Artificial Intelligence; to explain what Expert System and System for Decision Support are and its application in the field of health and to discuss some expert systems for differential diagnosis of urinary incontinence. It is concluded that expert systems may be useful not only for teaching purposes, but also as decision support in daily clinical practice. Despite this, for several reasons, health professionals usually hesitate to use the computer expert system to support their decision making process.

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