ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS
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ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS

Оценка 4.7
Научные работы
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10.02.2023
ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS
ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS
AbdumanonovAA-ActaCAMU-1-2022.pdf

MEDIC

SINCE

ISSN: 2181-4155

ActaCAMU

Scientific journal 1 (1 ) 2022

MERIDIAN

DIAONOST'C HOSPtTAL

ISSN: 2181-4155

ActaCAMU

Ilmiy-amaliy jurnal NI (1) 2022

Bosh muharrir

Mamasadikov Nurillo Shukrullayevich — CAMI_J rektori Bosh muharrir o'rinbosari dotsent Normatova Shahnoza Anvarovna (Farg*ona) Tahrir hay'ati:


prof Nishonov Yusibjon Nishonovich (Farg•ona) prof. Ermatov Nizom Jumaqulovich (Toshkentj prof Ro'ziyev Sherzod Ibodullayevich (Toshkent) prof Abdullayev Ravshanbek Babajonovich (Urganch) dotsent Botirov Murodjon Turg'unboyevich (Farg'ona) profGuIzoda Qurbonshox Muxammadali (Tojiklston) prof Ibodzoda Saidmuqim Tilloyevich (Tojikiston) prof Popov Vyacheslav Leonidovich (Rossiya) prof Al-Shukri Sálman Xasunovich (Rossiya) prof. Privalova Irina Leonidovna (Rossiyaj dotsent Suxinin Andrey Anatolyevich (Rossiya) prof Ned]det Unyuvar (Turkiya) prof Ayxan Attar (Turkiya) prof Chukanov Aleksey Nikolayevich (Belorus) prof. Rubnikovich Sergey Petrovich (Belorus) prof MII' Axmad Manzur (Hindiston) prof Beylarov Rauf Orundj oglu (Ozarbayjon) prof Taychiyev Imamnazar Taychiyevich (Qirg'iziston) prof. Shatmanov Suynali Toktonamrovich (Qirg'iziston) dotsent Abdumanonov Ahrorjon Adxamjonovich (Farg Ona)

2

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Scientjfvc јоигла!                                                                                                                          МЕТОIАН

MUNDARIJA


ПОВЫШЕНИЕ БИОЛОГИЧЕСКОЙ СОВМЕСТИМОСТИ ИМПЛАНТОВ НА

ОСНОВЕ Сп-Сге Ботпров М.  Ш.,А., Мопа.жонов ММ», Минченя

СОВЕРШЕНСТВОВАНИЕ ЛАПАРОСКОПИЧЕСКОГО ДОСТУПА ПРИ ЛЕЧЕНИИ ПАЦИЕНТОК С ОБРАЗОВАНИЯМИ БОЛЬШИХ РАЗМЕРОВ В МАТКЕ. Н А.

14

РОЛЬ ФАКТОРОВ РИСКА В РАЗВИТИИ БРОНХИАЛЬНОЙ АСТМЫ В

ДЕТСКОМ ВОЗРАСТЕ (П ш-А.,

КамотовиД,А. 25

ФРАНЦУЗСКИЙ ПАРАДОКС: ДА? НЕТ? МОЖЕТ БЫТЬ? (Предварительный обзор по данным зарубежной печати). Сухинин А. А.» Онбыш Т, Е.»

Норматова Ш, А., Боптров М, Т, 31

АХОЛИ УРТАЧА КАЁт ДАВОМИЙЛИГИ УЗГАРИШИНИ КЕЛГУСИДАГИ

ТЕНДЕНЦИЯЛАРИНИ БАХОЛАШ. НИЛ, Манаризпе«ХО.,

Ш А., Эртштов НЭК60

МАСШТАБЫ РАСПРОСТРАНЕННОСТИ ГЕЛЬМИНТОЗОВ СРЕДИ

НАСЕЛЕНИЯ АЗЕРБАЙДЖАНА. Хатирп Н. Х., Рузиматова 71

С,ЕРАТТТ В l<ASALLlGINl DAVOLASHDA DORIVOR 0'SlMLIkLARDAN

OLJNADIGAN BIOLOGlk FAOL MODDALARM JIGAR HUJAYRASlGA TA'SlRlNI

0'RGANISH, B01irav М. Т., NormaIova „Sh.A.e kuranmtova Sh.A. ...............................ВЗ

ARTIFlClAL 1NTELLlGENCE lN MEDICAL DIAGNOSTICS. А.А.,

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ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS

HCIO'CCTBEHHb1V1 UHTEAJIEKT B MEAUUUHCKOM AHAVHOCTHKE

TIBBIY DIAGNOSTIKADA SUN'IY INTELTEKT

Ábdumanonov,4. .4v,

Associate Professor ofCentral Asian Medical University, ahror79Qnbox.ru /?uziev Sh./„

Professor ofTashkent Pedicaric Medical Institute,

Abstract: The paper discusses the prospects for the use of artificial intelligence technologies in healthcare. medico/ diagnostics and treatment to improve the quality and reliability ofpatient treatment, Artificial intelligence as a separaðe science Of information technology is also developing in the Weld ofgrain storage and is used in various fields of medicine. The use of artifìcia/ intelligence in medical information systems is provided with a specially compiled database of problema\ie and their signs for each area of medicine, as well' as knowledge base on the corresponding medical action. The paper considers a class of in\e//igeni systems thai solve the information problem of recognition and monitoring, presents a system focused on solving the problem of recognizing an emergency condition upon presentation Of' Signs Of' diseases, which should be under.Ýð00d as anamnes/ic, clinical and laboratory  The use Of artificial intelligence in the diagnosiic process Wii/ increase the re/iabi/iïy of diagnostic support and increase the reliability ofclinical diagnostics in diagnostic decision suppor\ systems,

Key words: healthcare. medicine, intelligence, medico/ information sy,$/ejns, knowledge base.

Armoma«un: B cmanu,e

G 3òpaaoarpanenun.

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89


             R.woqedbfe cnoaa: 3òpaaooxpaneuue,        .ueðuguna, ucwyccmeeH1/bðù

Annotatsiya: Maqolada sun 'hy inÍe//ekt texnologiyu/aridan sog'/iqnj saq/ash, tibbiy diagnosÍika va davo/ashda bemorlarga xizmai ko'rsa/ish Mfati va ishonch/i/igini oshirish uchun foydolanish istiqbollari  qi/inodi.

inrellekt axboro/ rexnologwa/arining alohida fan/ sif01ida libbiyraning soha/arida qo'//ani/adi hamda qish/oq xo'Ja/igi .sohasida ham rivoJ/anmoqda. Tibby axboro/ tizim/arida sun"y inte//ekfdanfoydalanish /ibbyotning har bir uniq sohasi muammo/i vaziyatlar va Warning helgi/arining tuzilgan shuningdek, legishli /ibþiy haraka//ar bo'yicha bilim/ar þa:asi bi/an ia'minlanadi. Maqo/ada aniq/ash va monitoring qi/ishning axhomr muammosini ha/ qi/adrgan aqlli /i:unlar sinfì ko'rib c/nqi/adi, anamnestlk, k/inik va laboratoriya ko trinish/ari tushuni/ishi kerak bo t/gan kasa//ik belgilari paydo ho 'Igandafavqulodda Olish ha/ qi/ishga qarðð/i/gan li:im taqdñn etiladi. Diagnostika Jorayonida ih/ellek/dun foydalanish diagnostik yordamning ishonch/i/igini oshiradi va diagnosÍika qaror/arini qo'//abquvvailash tizim/arida klinik diagnosÍika ishonch/iligini os/nradi,

Kalit so'zlar: sog'liqni saqlash, /ibbiyot, sun tiy intellek/, tibbiy li:imlari,  bilimlar ba:asi.

Recently, there has been an increasing interest in artificial intelligence, caused by increased requirements for information systems, We are steadily moving towards a new information revolution, comparable in scale to the development of the global set, whose name is artificial intelligence,

Artificial Intelligence (Al) is a field of research that aims to study and model the principles and mechanisms of human intellectual activity. Al as a science is located at the Intersection of computer science, linguistics, psychology and philosophy. In addition, specific specialized knowledge from the relevant field is also used in the areas of Al application, such as natural sciences, law, economics, and medicine.

The idea of creating an altificial human likeness to solve complex problems and simulate the human mind has been in the air since ancient times. For the first time, the idea of creating an artificial human likeness was expressed by R. Lully (c, 1235-c. 1315), who in the X]V century tried to create a machine for solving vafious problems based on the universal classification of concepts [91.

In the eighteenth century; G, Leibniz (1646-1716) and R. Descartes (15961650) independently developed this idea by proposing universal classification languages for all sciences, These ideas formed the basis of theoretical developments in the field of creating afiificial intelligence, The birth of artificial intelligence as a scientific field occurred only after the creation of computers in the 40s of the XX century. At the same time, Norbert Wiener created his seminal works on the new science of cybernetics,


The term afilficial intelligence was proposed in 1956 at a seminar With the same name at Stanford University (USA), The seminar was devoted to the development of logical, not computational problems. Thus, research in the field of artificial intelligence is focused on the development and implementation of computer programs that can emulate those areas of human activity that thinking, a certam skill and accumulated experience. These include tasks of decision-making, pattern recognition, and understanding human language.

Researchers working in the field of Al have found that they are grappling with veuy complex problems that go far beyond traditional computer science. It turned out, that pnmary it is necessary to understand the mechamsms of the learmng process, the nature of language and sensory perception. It turned out that to create machines that mimic the of the human brain, you need to understand how billions of its interconnected neurons work Most of the researchers came to the conclusion that perhaps the most difficult problem facing modern science is to understand the processes of functioning of the human mind and not just imitate its worka which directly affected the fundamentål theoretical problems of psychological science.

Indeed, It is difficult for scientists to even come to a single pomt of view regarding the very subject of their research - intelligence, Some believe that intelligence is the ability to solve complex problems; others consider it as the ability io learn, genera/i:e and analogize; still others - as the ability to interact with the ouÍMde world through communication, perception and awareness of what is perceived.

Nevertheless, many Al researchers are inclined to accept the machine intelligence test proposed in the early 50s by the outstanding English mathematician and computer scientist Alan Turing A computer can be considered intelligent, Turing argued, if it can make us believe that we are not dealing with a machine, but with a person.


However, it was only after the Second World War that devices appeared that seemed to be suitable for achieving the cherished goal of modeling intelligent behavior; these were electronic digital computers. The "'electronic brain," as the computer was then enthusiastically called, startled US television viewers in 1952 by accurately predicting the results of the presidential election hours before the final data was received, This" feat " of the computer only confirmed the conclusion that many scientists came to at that time: the day will come when automatic computers, so quickly, tirelessly and accurately performing automatic actions, will be able to imitate non-computational processes inherent in human thinking, including perception and learning, pattern recognition, understanding everyday speech, etc. letters, making decisions in uncertain situations when not all the facts are known. This is how a kind of '*social order" for the development of Al systems formed "in absentia" [1-4

Currently there are six main areas of Al research:

*        knowledge representation;  manipulation or knowledge and search for solutions;  communication systems;

*        perception systems.

*        machine learning;  modeling intelligent behavior.


The emergence of Al as a scientific field is associated with an increase in the capabilities of computer technology and increased requirements for the mathematical support of computers. Many scientists consider Arthur Turing as the founder, In 1950 Turing's article "Computing Machinery and

English magazine) offers a criterion for whether a machine has thinking abillties (Turing test).


The problem domain of an intelligent system is defined by the subject area and the tasks solved in it. A subject area can be characterized by describing the area in tenns of the user, and tasks can be characterized by their type. From the developer's point of view, static and dynamic subject areas are distinguished, A subject area is called static, if the source data describing it does not change over time. In this case, derived data (derived from the original data) can reappear and change (without changing the original data). If the initial data describing the subject area changes during the task solution, then the subject area is called dynamic

Medical diagnostics refers to the dynamic and clinical diagnosis process is vital', if developers do not understand the diagnostic process correctly, then it is no less likely that they may develop any used software in medical diagnostics. A clinical diagnosis is specialized knowledge that can only be understood correctly with some essential medical knowledge- Doctors study the diagnostic process using knowledge from many medical branches such as human anatomy, medical biochemistry, medical physiology; pathology, microbiology, parasitology, fonensic medicine, medicine, epidemiology, surgery, ENT, ophthalmology, pediatrics, radiology, pharmacology, and forensic medicine, Ideally, the developer should have some knowledge in these medical industries. Although there are many defined principles and guidelines for medical diagnosis, the diagnostic process is still seen as an an form that can only be learned through medical knowledge and experience. Doctors' help in the development process is very important, Choice of clinical situations, if you want to develop a clinical diagnostic system that solves all medical diagnostic processes (for example, requesting a patient, examining a patient, taking ultrasound images, X-rays and processing them, and so on), you will probably never need to release a product until you have an infinite number of resources Il I, 12, 171

Choosing a clinical area is very important, Selecting a small area results in developing a small tool; while selecting a very large clinical area and domain will be difficult to develop and require a huge amount of resources and decades of human life. Commercial suppliers should analyze the commercial value of the product. At the moment, clinical diagnosis support systems are not very successful in the commercial software marketi Even if it is most ma]or clinical trials are suppofied151,


The analysis of modern literature has shown that currently, there is experience in creating technologies and designing, and creating expert systems (ESL including systems related to solving problems of medical and technical diagnostics. Goodall A. states, that in a typical ES, approximately 70% of the time is spent on developing the interface, which takes up an average of 44% of the code, while the logical output machine takes up 8% of the code, the knowledge base 22%. The reasons for these are the presence ora large number of domain concepts that characterize the initial data or the problem, as well as the joint design of the interface with the knowledge base and the output machine 1101, A typical ideal ES should have the following basic properties: competence; the ability to reason; the ability to solve non-trivial unformalized problems from real subject areas; the ability to self-awareness.

The architecture of a typical expert system includes the following main components: a knowledge base (KB); database (DB); a goal base; a working memoty or working knowledge base; a decision inference stibsystem; intelligent interface subsystem; a support and debugging subsystem; a digital modeling subsystem; a decision explanation subsystem; and a coordination and management subsystem

The main task of intelligent systems is knowledge processing. Most often, intelligent systems are used to solve complex problems, where the main complexity of the solution is associated with the use of poorly formalized knowledge of practitioners, and where logical (or semantic) information processing prevails over computational. For example, understanding natural language, analyzing visual information, supporting decision-making in difficult situations, making a diagnosis, and making recommendations on treatment methods 19, I l

12, 18, 20, 211,


The question of what should be the fomal model of knowledge representation in an intelligent system is not fully resolved, For example, the created "empty" ES, which initially contained well-known methods (frames, products, or semantic networks) for representing knowledge and the reasoning mechanism, were not effective and not so convenient in software implementations. Studies have shown that each problem area should have its own rigid definition of the model of knowledge representation and the method of obtaining a solution, Knowledge representation models deal with information received fmm experts, which is often qualitative and often contradictory. However, due to the specific functioning of the computer, such information should be reduced to an unambiguous formalized form. Knowledge representation as a methodology for modeling and formalizing conceptual knowledge, focused on computer processing, is one ofthe main and most important topics related to knowledge engineering,

One of the most important problems is the problem of knowledge representation, This is explained by the fact that the form of knowledge representation has a significant impact on the characteristics and properties of the system, In the natural and technical sciences, the following traditional way of presenting knowledge is adopted. In natural language, the basic concepts and relations between them are introduced. In this previously defined concepts and relationships are used, the meaning of which is already known, Next, a


correspondence is established between the characteristics (most often quantitative) ofthe concepts ofknowledge and the appropriate mathematical model.

Knowledge may not always be described accurately - so-called "fuzzy" knowledge is often encountered, When trying to formalize human knowledge, researchers soon encountered a problem that made it difficult to use traditional mathematical tools to describe it, There is a whole class of descriptions that use qualitative charactenstics of objects: many; few, strong¼ velY strong, and so on, These charactenstics are usually vague and cannot be clearly intemreted, but they contain important information, such as" One possible sign of tick-borne encephalitis is a high temperature,


In addition, problems solved by intelligent systems often involve using inaccurate knowledge that cannot be intemreted as completely true or false. There is knowledge, the reliability of which is expressed by some intermediate figure To represent fuzzy knowledge in the early 70s, the American mathematician Lofty Zadeh proposed the fonnal apparatus of fuzzy algebra and fuzzy logic (L. A. Zadeh, Fuzzy Sets, Information and Control), L. Zadeh's theory is based on a subjective fact - sub]ective ideas about the goal are always fuzzy. But he also takes the next step-he believes that all the subject's estimates and constraints that he works with are also usually fuzzy, and sometimes even devoid of quantitative characteristics in their initial form- L. Zadeh introduced one of the main concepts in fuzzy logic - the concept of a linguistic variable. A linguistic variable is a variable whose value is detemnined by a set of verbal characteristics of a cenain property, For example, the linguistic variable pain" is defined as a set (ear, cutting, aching, shooting, sharp, blunt, burning), There are many different ways to perform operations with fuzzy knowledge expressed using linguistic variables. These methods are mostly heuristics. Strengthening or weakening of linguistic concepts is achieved by introducing special quantifiers. For inference on fuzzy sets, special relations and operations on them are used, One of them is

Approximate Theory.

The theolY of approximate reasoning was created by Lofty Zadeh in 1979. This theolY provides a powerful tool for implementing logical inference with fuzzy and uncertain information. The central idea of this theory is to represent logical statements in the form of statements that assign fuzzy sets to variables as values

[91,


Despite the above studies, most of the clinical cases still rely on manual clinical diagnosis. Manual clinical diagnosis is a very complex, cumbersome and error-prone process; even veuy experienced doctors are sometimes unable to correctly diagnose a clinical condition at an early stage. The purpose of this paper is to investigate software suppott for automating diagnostic decision- making in medical information systems for healthcare and medicine, and an algorithm-based approach that can improve clinical systems support diagnostic decision-making, also examines the diagnostic process, clinical reliability support for diagnostic decision-making system software, and explores and suggests a new path the development will improve and increase the reliability of clinical diagnostics in system support of diagnostic decision-making,

At the initial stage of intellectuallzatlon of medlcal information systems (MIS), we, together with experienced doctors in the relevant fields of medicine, created and implemented in clinical practice a system for intellectual support of the doctor during the examination of patients similar to 119L but for urgent pathologies, At the same time, we have developed BZS of this system for the areas of emergency abdominal surgery and emergency medicine that operate in a multidisciplinary hospital,

Taking into account the fact that B timely and prompt identification (forecasting)is of paramount importance in emergency medicine- life-threatening emergency condition-problem situation (I'S), as well as taking adequate emergency measures to eliminate them, we have developed methods, algorithms and software for automatic detection of PS in the patient's body from the data of his electronic medical history (EIB), as well the implementation of intellectual support for medical services in the next stage of the MIS intellectualization process solutions to get out of this situation, Naturally, such situations have different natures in different pathological conditions of organs and the body as a whole and require appropriate adequate approaches to their elimination,

Such developments are relevant and necessary especially for the fields of medicine, where the elements or subjectivity are very significant, and the responsibility for making decisions is great, which is typical, in  for surgeuy, especially for emergency abdominal surgery.

The paper considers a class of intelligent systems that solve the information problem of recognizing, monitorong a problem situation, and selecting the level of assistance from available resources that would ensure the minimum probability of


focused on solving the problem of recognizing an emergency condition in children in terms of a syndrome or several syndromes (characterizing their conditions, which reflect the degree of seventy of the syndrome) when presenting signs of diseases, which should be understood as anamnestic, climcal and lab018tory manifestations, In the most general case, the intellectualization of decision making processes involves providing existing medical information systems with the following additional capabilities:

      plannmg a chain of events from the current state of the patient to the desired result (recovery) for a given treatment program;  assessment of information on the degree of materiality and their corresponding sotting under the specified criteria for selecting information;

      search for medical solutions in the context of incomplete and unclear information, using the specified "heuristics" and the experience of subject area experts.

As a result of the operation of the smart module, three types of situations can be identified,


   I                        

      the first type of situations when there is an objective need for urgent adoption of an appropriate specific medical decision, taking into account the patient's condition;

      the second type of situation, when it is predicted that some medical decision will need to be made urgently in order to prevent the occurrence of the first type of situation in the future;

      the third type of situation is when there is no need to make a medical decision, and the doctor simply takes note of the results of the analysis of information,

If the first two types of situations can be formalized in order to automatically detect such situations and develop a certain set of optimal medical decisions, then the third type of situations is difficult to formalize, since the doctor is not to make any decision,

To detect PS, the existing medical information system must be supplemented with a subsystem with the following modules:

      modules for entering and correcting signs of PS, as well as corresponding sets of medical decisions;

      PS detection module;  a module for selecting a set of optimal medical decisions and, in some cases, making (executing) these decisions;  module for evaluating the effectiveness of decisions made;  a module for maintaining a history for each specific PSS and if necessa1Y, l•sr it in the future when making a decision,

The functioning of the support system is provided by a specially compiled database of problem situations and their signs, for each specific field of medicine, as well as a knowledge base on relevant medical actions. The main task in building the system of intellectualization of MIS, of course, is to create a database

containing infomtation about the relationship of signs of the patient's body with certain problem situations [13-171

When creating the database and BZ of the system developed by us, we used the features of the clinical picture in various PS identlfied as a result of analyzing the medical history of 388 patients operated on in the emergency department of abdominal Surgery of the Ferghana branch of the Reptiblican Center for Emergency Medical Care in the period 2007-2012. They present the identified PS and their relationship with various signs of the body's condition. Out of 388 patients with abdominal acute surgical diseases that thneaten the patient's life, PS occurred in 76, including 65, associated with the development of various complications of the underlying disease, such as acute diffuse purulent peritonitis, bleeding, post-hemorrhagic or post-traumatic shock, hypovolemic shock, acute cardiovascular, respiratory failure, liver failure, etc. 11 patients with lifethreatening PS developed non-underlying pathologies associated with anaphylactic shock or concomitant pathologies such as CHI), diabetes mellitus, chronic nonspecific lung disease, etc. In 37 patients with PS, a life-threatening condition of the patient occurred preoperatively, 38 patients in the postoperative period, The number of signs in the form of precursors of PS was 96, reliable signs including complaints or patients, objective data and results or instrumental and laboratory studies were 502. The clinical manifestation of life-threatening PS in the patient was characterized mainly by a sham drop in blood pressure, cardiac arrhythmia, tachycardia, bradycardia, respiratory anhythmia, convulsive state, loss of consciousness, hypefihermia, hypovolemia and also a corresponding change in laboratory criteria.

In the process of functioning of the subsystem "Intellectualization or medical decisions", the main source of primary and reference information is the electronic patient history data contained in the MIS. At the same time, the subsystem maintains its own database, where the following data is stored:

      a list of all registered pss in the system;

signs that can be used to detect PS-

      a set of optimal medical solutions for the resolution of a specific PS;

      criteria that should be used to evaluate the effectiveness of individual solutions to elimmate a specific PS;  hist01Y of medical decisions made for each specific PSE

When entering a new PS into the system, first of all, you must specify the signs by which the PS detection module would be able to recognize this PS, The next step when entering a new PS into the knowledge base is to establish a specific set of medical solutions to eliminate this problem.

To implement the above methodology, algorithms for detecting PS were compiled from the patient's medical history data and a BZ for clinically justified optimal medical actions in these situations was developed. has been created that implements the functioning of the developed subsystem in the MIS structure, An automated workplace of an expert doctor with a user-friendly interface has been developed, and the previously created doctor's workstation has been supplemented With a special dialog for the doctor to interact with the system.

With the inclusion of the subsystem "Intellectualization of medical decisions" in the MIS, the technology of solving medical and diagnostic problems is radically changing, If with traditional technology, a doctor starts searching for solutions only after a certain PS occurs and, as a rule, in conditions or an acute shonage of time, then in intelligent information systems, a specialist doctor can form a knowledge base on PS in advance With the help of an expert doctor's workstation developed by us, without haste and thoroughly, and in the future the system itself will monitoring the occurrence of PS and the search for acceptable medical solutions [51, At the same time, a problem entered into the knowledge base can be solved repeatedly as it occurs. Moreover; taking into account the results of previous solutions, the system gradually becomes not only more experienced, but even "smartn with the expansion of the knowledge base.

This approach to the intellectualization of medical decision-making processes makes It possible to make significant changes in the technology of developing intelligent systems to support the doctofls activities. Now it is enough for developers to create tools that allow a specialist from any field of climcal medicine-an expert'to create and maintain their own database and knowledge on PS. And, finally• following the ramous thesis that it is necessary to give the machine-the machine, the person-the human, the proposed intelligent information system provides for an optimal distribution of responsibilities in the decision, making process between the system and the doctor, Medical specialists form databases of data and knowledge in their field and make the final decision, and the system will do all the other complex computational, analytical and logical work, The main advantages of the proposed approach to intellectualization of medical decision-making processes are:

      development of a single tool for solving many non-standard problems in various areas of medicine;

      during the operation of information systems, self-learning of the system With the replenishment of the knowledge base also takes place;

      flexibility in solving problems by combining ready-made and proven medical solutions;

      the implementation of the intellectualization module does not require stopping or redesigning the existing information system.

Thus, the *'intelligent support" system by us, which contains two databases and KB and two software blocks, using clinical information from the MIS patient's electronic medical history database, provides prediction and recognition of problematic situations in the patient's body, in particular with abdominal emergency surgical pathology, and contributes to making effective medical decisions to eliminate them or prevention,

The experience of recent years has shown that applying one method to solve complex problems and problems does not always lead to success, In a hybrid

architecture that combines multiple paradigms, the effectiveness of one approach can compensate for the weakness of the other, By combining diffenent approaches, you can avoid the disadvantages inherent in each of them individually, Therefore, one of the leading trends in modern computer science is the development of integrated, hybrid and synergistic systems. Such systems consist of various elements (components) combined to achieve their goals. Integration and hybridization of various methods and technologies allows you to solve complex problems that cannot be solved on the basis of any individual methods or technologies.

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MEDIC SINCE ISSN: 2181-4155

MEDIC SINCE ISSN: 2181-4155

MERIDIAN DIAONOST'C HOSPtTAL

MERIDIAN DIAONOST'C HOSPtTAL

Rubnikovich Sergey Petrovich (Belorus) prof

Rubnikovich Sergey Petrovich (Belorus) prof

Ш А., Эртштов НЭК 60 МАСШТАБЫ

Ш А., Эртштов НЭК 60 МАСШТАБЫ

The paper considers a class of in\e//igeni systems thai solve the information problem of recognition and monitoring, presents a system focused on solving the problem…

The paper considers a class of in\e//igeni systems thai solve the information problem of recognition and monitoring, presents a system focused on solving the problem…

R.woqedbfe cnoaa: 3òpaaooxpaneuue,

R.woqedbfe cnoaa: 3òpaaooxpaneuue,

The idea of creating an altificial human likeness to solve complex problems and simulate the human mind has been in the air since ancient times

The idea of creating an altificial human likeness to solve complex problems and simulate the human mind has been in the air since ancient times

Mde world through communication, perception and awareness of what is perceived

Mde world through communication, perception and awareness of what is perceived

The emergence of Al as a scientific field is associated with an increase in the capabilities of computer technology and increased requirements for the mathematical…

The emergence of Al as a scientific field is associated with an increase in the capabilities of computer technology and increased requirements for the mathematical…

Choosing a clinical area is very important,

Choosing a clinical area is very important,

The question of what should be the fomal model of knowledge representation in an intelligent system is not fully resolved,

The question of what should be the fomal model of knowledge representation in an intelligent system is not fully resolved,

Knowledge may not always be described accurately - so-called "fuzzy" knowledge is often encountered,

Knowledge may not always be described accurately - so-called "fuzzy" knowledge is often encountered,

The central idea of this theory is to represent logical statements in the form of statements that assign fuzzy sets to variables as values [91,

The central idea of this theory is to represent logical statements in the form of statements that assign fuzzy sets to variables as values [91,

Such developments are relevant and necessary especially for the fields of medicine, where the elements or subjectivity are very significant, and the responsibility for making…

Such developments are relevant and necessary especially for the fields of medicine, where the elements or subjectivity are very significant, and the responsibility for making…

I • the first type of situations when there is an objective need for urgent adoption of an appropriate specific medical decision, taking into account…

I • the first type of situations when there is an objective need for urgent adoption of an appropriate specific medical decision, taking into account…

The main task in building the system of intellectualization of

The main task in building the system of intellectualization of

PS- • a set of optimal medical solutions for the resolution of a specific

PS- • a set of optimal medical solutions for the resolution of a specific

This approach to the intellectualization of medical decision-making processes makes

This approach to the intellectualization of medical decision-making processes makes

The experience of recent years has shown that applying one method to solve complex problems and problems does not always lead to success,

The experience of recent years has shown that applying one method to solve complex problems and problems does not always lead to success,

K0Mapuoga Jl.r., Kan1/HKOB Ll.C

K0Mapuoga Jl.r., Kan1/HKOB Ll.C

МЕТОIАН 6. Попов ЭВ. и др. Искусственный интеллект-

МЕТОIАН 6. Попов ЭВ. и др. Искусственный интеллект-

SJIF 2021 - 7492 - 1227-1242 р,

SJIF 2021 - 7492 - 1227-1242 р,

Искусственный интеллект в медицине

Искусственный интеллект в медицине
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