KEY TECHNOLOGIES FOR THE FORMATION OF ARTIFICIAL INTELLIGENCE IN MEDICINE

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 3347-3356 © TJPR Pvt. Ltd.

KEY TECHNOLOGIES FOR THE FORMATION OF ARTIFICIAL INTELLIGENCE IN MEDICINE RUSTAMOVA RANO PARPIEVNA1 & OHUNOV RAVSHAN ZAFAROVICH2 1

National University of Uzbekistan, Department of Life Safety, Uzbekistan 2

Head of Departament of Life Safety, Uzbekistan

ABSTRACT This paper provides a detailed analysis of the problems faced by developers of expert systems on the path to creating artificial intelligence in the field of medicine are being developed; options for solving them are proposed. The main emphasis placed on creating a unified database of medical knowledge and technologies that used in everyday clinical practice.

KEYWORDS: Medicine, Artificial Intelligence & Electronic Databases

Received: Apr 08, 2020; Accepted: Apr 28, 2020; Published: Jul 13, 2020; Paper Id.: IJMPERDJUN2020319

Artificial intelligence begins its rebirth since the advent of first electronic computers in the 40-ies with the appearance of computers began to be conditions of the possibility of creating artificial intelligence. There are questions about whether it is possible to make a machine that will have the same intellectual capabilities as humans (or far beyond). Scientists in 50 years did experiments in the construction of equipment, which would mimic the human brain. Such attempts have failed, because it was completely unusable software and hardware tools. In 1956,

Original Article

INTRODUCTION

the seminar on the development of logical problems and ways to automate their solution. It was first introduced the term artificial intelligence. After the workshop, the artificial intelligence recognized as a separate branch of science.

After 25 years, Baer and Feigenbaum offer a more precise definition of artificial intelligence, which States that Artificial intelligence (AI) is the area of computer science that develops intelligent computer systems: understanding language, learning, ability to reason, solve problems, etc. Bar and Feigenbaum highlight key moments in artificial intelligence, to attained by the creators in the course of work on the AI. From the very beginning came the division of the field of artificial intelligence into 2 parts: Cybernetics and Neurocybernetics.

The main idea of Neurocybernetics is the confirmation of the uniqueness of the human brain, which can think.

Therefore, AI should be similar on the brain. of Neurocybernetics details began to study hardware in the loop structures similar to the structure of the human brain. They took upon themselves the work of creating the delicious system of elements, similar to neurons. Such systems were called neural networks. Cybernetics sided renunciation of the idea of the whole perekopirovannye thought processes of man. For them, no matter how "thinking" device.

Importantly, on these issues the device and the man reacted in the same way. As everything happens inside the machine, Cybernetics was not interested. That is why they got the name Cybernetics "black box". In the mid-70s instead of finding, a unique algorithm of thinking originated the idea of modeling accurate knowledge. This was a crucial breakthrough artificial intelligence. The new approach consisted in knowledge representation. Created DENDRAL and MYCIN – a classic expert system for medicine and chemistry.In the mid 80-ies, the

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commercialization of artificial intelligence.Grows annual cash contribution to the field created an expert system, increases comprehensive interest. On the creation of products, using artificial intelligence for the health region employs developers from various companies (Microsoft, Apple, Google, etc.). Now there is the use of artificial intelligence in the field of drug development, medical imaging, genome research, disease diagnosis. The device can be trained. So they self can perform a huge volume of the information received to the conclusion and make the decision that saves time, money and to produce the maximum effect in the care of patients. To identify correctly the cause of the disease and give proper treatment, you need to learn information about the patient: view the history of the disease, tests, pictures and more. Sometimes the most experienced doctors may not deliver a confident diagnosis because you can't see the full picture of the disease. Analytical data from Google, every tenth patient suffers due to a wrong diagnosis. Many resorted to the theory that AI will help to resolve the problem. In some hospitals in the UK already use the development from Deep mind Health. It handles all the information about the patient, all of his symptoms and gives a list of recommendations to the attending physician, which puts accurate, definitive diagnosis. There are systems that can give their results-not the doctor, and immediately to the patient. One of such systems is Ada. She advises the patient, gives advice, tells to what doctor should contact, and offers remote consultations with a specialist [1]. For people who recently completed a long term treatment or have a chronic disease, invented Sense.ly. The system provides warnings about the advent of medication, the need for supervision by a doctor, structures the data on the patient's condition and sends the statistics to your doctor. Now there is a system of genetic analysis, which reveal the tendency of the patient to various diseases, help to understand the root cause of the disease and indicate what effect you can expect from a particular drug. Scientists have learned how to apply artificial intelligence in the creation of medicines. Finding the right chemical formula for them is a large amount of time. The results do not always get the desired result. Even many samples, inspections and tests are not always able to give an absolute guarantee that the medication will work. So have resorted to artificial intelligence, which in turn creates the correct chemical formulas of drugs. Sometimes cancer patients people only last hope for treatment. IBM Watson supercomputer was created in 2011.

Now it module Watson for Oncology is applicable for the diagnosis and treatment of cancer [2]. The main mission of the super computer to find the information in the database and give the user. In the case of Watson for Oncology, this database includes more than 600 thousands of medical reports and diagnoses, as well as two million pages of texts from medical journals and clinical trials in Oncology. The neural network can offer you several treatment options; the doctor can choose the best. The doctor as needed, you may add information about the patient and the computer at this point would be to look for a new course of treatment in accordance with the recorded information and after a short period will give a refined diagnosis. In 2016, the AI has identified a 60-year-old female patient, which was initially misdiagnosed, a rare form of leukemia. For this, the system for ten minutes, examined 20 million scientific articles about cancer. One proven form of artificial intelligence in medicine —QTrobot: robot therapy for children with autism spectrum disorders. These patients can hardly communicate with others: they are almost unable to perceive other people's emotions and have trouble expressing my. The older the person becomes, the harder he has to, because there is a worsening of the problem. So if you do not pay enough attention to this disease at an early age, subsequently to be hard to cope with it. QTrobot designed for children aged four years. Communicate with the patient through words, gestures and various facial expressions. This robot helps the child to learn to recognize the moods of others, can be taught to communicate. According to the experience of 2018 found that, children with autism pay more attention to the robot than the doctor. On average, they looked at it twice as long. While the robot is still not on sale, it passes various tests in the medical institutions. The use of AI in medicine will help make diagnosis more accurate, can effectively predict and prevent disease. AI will help rescue more patients and improve the

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efficiency of medical institutions, and facilitate the work of doctors.

In the modern world in connection with the development of high technology and the accumulation of a large body of information in this multidisciplinary area of medicine, there is an urgent need to create artificial intelligence, able to store ever-increasing amounts of knowledge and to process this information, giving as result the optimal algorithm of patient management [1]. Scientists of various fields have raised audacious goal: to develop information technology capable to replace partly or greatly facilitate intelligent human labor, i.e. to create artificial intelligence. To this decision of the developers in the field of biomedical Informatics is pushing a number of health problems, determining the disparity between the actual quality of care and possibilities of modern technologies and achievements of science. First, it is a problem of updating of medical knowledge and the impossibility of full-fledged evaluation of clinical practice, leading to the loss of large amounts of data containing important information not accounted for properly. The so-called big data in medicine. It is necessary to analyze in real time (the collection, storage, formalization, Continuous updating, analysis, interpretation) with the creation of a regularly updated electronic database and clinical registers.

Lack of skills and low awareness of health professionals about the principles of evidence-based medicine lead to inefficient choice of diagnostic procedures and treatment methods, undue - values of drugs, as well as to inadequate prediction of clinical outcomes.

On the other hand, the high workload of doctors limits the ability to make the right decisions in difficult cases, because it requires a time-consuming to search and read relevant literature. This, in turn, contributes to the emergence of medical errors in clinical practice at different stages of patient management. In addition, the high employment rate does not allow the doctor on time and in full to deportirovat about the side effects and cases of ineffectiveness of the applied therapy.

Further, one of the major problems affecting the quality of medical care is the lack of monitoring of the process and outcomes of treatment for outpatient management of the patient after discharge from hospital, as well as the complexity of monitoring clinical outcomes in patients in different medical institutions single - core or multi-disciplinary doctors. The introduction of artificial intelligence in the healthcare system is designed to increase the information awareness and qualifications of the doctor, to help in the rapid and informed clinical decision-making by providing expert opinions and recommendations. Artificial intelligence is integrated into the medical information systems of medical institutions of any profile and scale. In the background, the system analyzes hundreds of thousands of electronic medical records per second, predicts the risks of complications and notifies doctors, offering the necessary preventive measures.

Finally, the system supports the clinical diagnosis process, reveals the early development of pathological processes and, using a personalized approach to the selection of treatment methods, recommends optimal treatment taking into account the individual characteristics of each patient.

Artificial intelligence in medicine — high-tech system that solves the problems of securing and improving the quality of care at the national level. It is a daily physician assistant, free from the influence of stakeholders and "aggressive" marketing pharmaceuticscall companies that raise the level of education in the field of evidence-based medicine, allowing you to save time specialist and acquainting him with the latest results of clinical practice in a given area. The system also enhances the participation of a physician in the practice of pharmaceutics allows you to compare clinical and economic efficiently use various medical technologies, including original and generic pharmaceuticals. Such information technology should instantly give the doctor the result of analytical processing of massive information, which is www.tjprc.org

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formed from case histories in the format of the electron ion of a register of clinical data on a given issue (the choice of diagnostic tools, treatment methods, etc.). Based on the results of the analysis of the clinical practice system of formation of the anticipated recommendations for the selection of diagnostic statistical tools and/or methods of treatment, the most suitable for the Dantion of the patient, given his diagnosis and the clinical picture, but also predicts clinical outcome of therapy. Analysis of application of a particular medical technology conducted for the entire period of its existence — the first mention in the clinical materials. Thus, it becomes possible to combine and strengthen the evidence on efficiency and safety of application of medical technologies in rare clinical cases for example when an orphan disease. A prerequisite for intelligent systems is integration into the medical information system and the existence of a machine module of the analysis of medical texts, which provides background monitoring of the actions of the doctor and "read" the electronic health record without user input and extra effort in data entry.

In addition, each integrated electronic health record must have a unique identification code of the patient, which is stored in the treatment and prevention institution and/or from the patient. This code allows to obtain information about the status of the patient's medical history and individual characteristics, can be used when receiving patient medical care at another institution (in the future — the institution of another country having an agreement on cooperation in the field of medical care) for doctors making clinical decisions, as well as for data logging question clinical observations and intermediate results of prescribed therapy. Additionally, PA - the patient in the described system must be created specific module, perform reinforces following functions: 

reminder to the patient about visits to the doctor, about taking medications;

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providing the patient with adapted information about the prescribed methods of diagnosis and treatment, including pharmacotherapy;

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informing the patient about the requirements for the prescribed medical procedures and conditions, methods and regimen of medication;

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search for pharmacies with an assigned drug with a ranking function according to the criteria of the price and location of the pharmacy;

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ability to remotely monitor the functional state of the patient. The development of artificial intelligence in medicine can significantly improve the quality of medical care.

The use of such "reasonable" will save the doctor from work for the performance of additional paper forms (in the

formation of applications for inclusion of medicines in the formulary list, making spontaneous messages) and frees more time for the treatment process, which will also lead to improved quality of care.

TASKS AND SOLUTIONS Task 1.

The model of knowledge representation that can describe often-conflicting knowledge from different fields of medicine.

Expert opinions usually formed intuitively with fuzzy evaluation of the facts that need to brought to a common denominator with objective data obtained through clinical trials. Not less problematic to integrate knowledge with the development of complementary facts from different Obdomains of medicine and biology, presents a diverse array

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according to the structure of the ontologies. Even more difficult for - to militate pathological processes when the requirements for the choice of model of knowledge representation increase dramatically [2].

Specifically for these tasks developed a new model performancewhich can describe any field of medicine. Unlike traditional models of knowledge representation, where semantic networks based on graph theory are used, the proposed model is based on a concept where side connections are used. That is, it is possible to connect vertices with edges and edges together, the model diagram is shown in Figure 1.

Figure 1: Medical Knowledge Representation Model.

(Lateral Relations between Graph Elements as an Addition to Graph Theory) The vertices of the graph V1 and V2 connected by the edge e1, which simultaneously forms a new entity or relation e1 = {V1, V2}. The edge e1 can connect to another vertex V3, forming a new edge e2 = {e1, V3}, which, in turn, is also an entity or relation. This is just one element of a multifaceted model that provides tremendous benefits in modeling medical knowledge.

Using the principles of fuzzy logic in the new model made it possible to present complex, poorly formalized medical knowledge, to accurately model risk factors, etiology, pathogenesis of the disease (probability, time of manifestation and sequence of pathological signs at each stage of the development of the disease), and treatment and prevention methods. The concept provides an opportunity to clarify the many conditions for the formation of pathological and compensatory mechanisms, for example, signs characterizing the personal characteristics of the body (gender, age, immune status, chronic diseases, etc.), and many external and internal pathogenic factors, each of which to one degree or another has influence on the development of the disease. It is also easy to model the structural and functional characteristics of the body and their relationships, for example, compensatory mechanisms, reflexes, complex anatomical structures, all the features of variant anatomy and other signs, on the basis of which the reactivity and resistance of the body are formed. All this is very important for the system to work in personified medicine mode.

Task 2 Crowd sourcing for gathering and formalizing a large amount of expert knowledge from different fields of me - wildlybiological Sciences that are necessary for the effective launch and operation of the expert system Yes, in one highly specialized field of medicine.

Especially great difficulties arises principally at the stage of transfer of knowledge that the experts possess. Most experts, successfully using everyday activity his extensive knowledge, have trouble with their formalization and www.tjprc.org

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systematization.To solve this problem, a certain style of thinking is needed, combining a tendency to analysis and systematization, which is more inherent in programmers than doctors. Thus, the development of an expert system requires the involvement of specialists with a certain set of knowledge and performing the functions of “intermediaries” between experts in the subject area and programmers, or it requires a special technological solution with which it is possible to distribute tasks among experts, and experts answering a specific question, they will be able to train the system [2].

Today, this problem mainly solved by crowd sourcing. For example, to effectively collect and formalize knowledge in the field of medicine and related disciplines, you can use a special technology that applies the principles of crowdsourcing to collect knowledge directly from experts. The system distributes tasks among experts (doctors, pharmacists and biologists) and from the incoming pieces of information forms a semantic network. Information in the system entered not only in text form in different languages, but also in the form of logical connections with one or another probability. The ability to build neural networks in the concept of fuzzy logic allows the system to combine the views of an unlimited number of doctors, creating a collective mind. The presence of the “ontology constructor” module in the system provides the opportunity to create the necessary ontologies for formalizing knowledge in any field of medicine and biology [2].

Task 3 Medical ontology. To represent (store) domain knowledge in the form of links and the most accurate descriptions of phenomena of designed a special ontology. Medical ontology divided into two large groups: 

The ontology of the formation of the medical characteristics of the basic terms (the rules of forming of structural, functional, parametric, pathological and other medical - biological characteristics).

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The ontology of the description of pathological processes and events (rules for the simulation of risk factors, prevalence, etiology, pathogenesis, clinical picture, diagnosis, differential diagnosis, treatment, prevention, outcome of the pathological processes, drug interactions, limits, etc.). Most ontologies allow for modeling the dynamics of the process and personalization of relationships.

Ontologies are the cornerstone for the main modules of the developed expert system. For example, a machine with

the extraction of facts from the test, structuring of knowledge, the formation of classifiers and the algorithms of the expert system.

Task 4 A flexible system of classifiers that can automatically expand in the course of its use.An integral part of any knowledge base underlying expert systems is classifiers. Classifiers are nothing like the frame knowledge base, and its elements are the "points of crystallization" for the entire semantic network.

Medicine is constantly evolving, and with evolution medical terminology and improved classifiers. Built a knowledge base of classifications of diseases constantly have to reconstruct and expand following the development of fundamental and applied medicine. During the reconstruction of the classifiers have to reorganize a huge part of the semantic web.

The usual format is a hierarchical representation of the classifier is sometimes insufficient for detailed descriptions of all stages of the disease. You need to create dynamic classifier is able to evolve automatically within a more Impact Factor (JCC): 8.8746

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grassroots rules. Detailed medical classifiers for automatic extraction of facts from medical texts.

This frame created knowledge base, which is a detailed systematic classifier of medical concepts, terms and their synonyms in the Russian language. Key concepts are associated with frequently used attributes (archetypes). Complex concepts are divided into simple elements according to the principle of binary fission, which increases the efficiency of the search. The advantage of the classifier is the ability to generate concepts according to the rules of medical ontologies. This allows you to create essential health concepts in the process of using the classifier in clinical work. The possibility of creating complementarity with other classifications and clinical terminologies. This classifier allows to formalize and index textual information at the time of her entry in the medical card of the patient. It has a number of advantages over foreign analogues: 

formation of new concepts takes place in real time when using the classifier in clinical work;

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search concepts carried out by key phrase, it uses a medical ontology;

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C) today, the classifier contains the greatest number of unique concepts (terms, not counting synonyms), used in medicine, more than 2.5 million But that is nothing compared to what can automatically be created in the process of its use;

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for many of the key concepts formed the context of possible and frequently used attributes (so-called archetypes).

Basically, it refers to the symptoms of typical pathological processes and parametric characteristics.

Task 5 Technology of machine learning through the analysis of medical texts. Information extraction from medical texts is a very complicated technological task. Today technologies of linguistic analysis of texts very much [1], but as shown, the analysis of the text only at the level of linguistic rules is not enough to correctly extract facts from the medical literature. To do this, the system must lay the necessary basic knowledge in medicine. To better present the problem, it is possible to give a simple example. If you ask a specialist in linguistics, non-medical, read about some disease in the textbook of internal medicine, even the overall picture of the disease he will not develop, not to mention the proper interpretation of the etiology, pathogenesis and clinical picture of the disease. For a proper understanding of the meaning of the linguist, need at least basic knowledge in medicine, physiology, pathophysiology and pharmacology.

That is why expert system taught systematically to fill the knowledge base layer by layer, just as they teach a student in medical school. Since all basic information is in the textbooks, the system must learn to read and understand medical literature.

For efficient extraction of facts from text knowledge base filled with minimum basic information, including medical ontologies, nomenclatures, systematic knowledge in the field of anatomy, physiology and pathophysiology of man.

Task 6 The module of formation of degree of evidence and the reliability of the data. Acute problem of quality of knowledge provided to the system, and their reliability, as well as the flexibility of the system and its ability to self-training [2].

According to the principles of evidence-based medicine, the main measure of the evidence are randomized controlled

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clinical trials [2]. However, the results of such studies are not so obviously and transparently as it might seem at first glance. There are many limitations for the extrapolation of data obtained in clinical trials in terms of clinical practice. A number of problems determines them: The problem of transfer of results obtained during clinical studies (randomized controlled trials, which are considered the "gold standard" of clinical trial methodology and, accordingly, the results of which are evaluated highly on a scale of levels of evidence): 

Inconsistency of standards of management and treatment of patients "Protocol" realities of clinical practice;

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a relatively "pure" sample of patients participating in the study, in terms of comorbidities, stage of the underlying disease, clinical condition at the time of inclusion in the study;

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high patient compliance compared to "normal" patients.

Problems of publication of results:

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unsatisfactory results of the study may not be communicated to the wider medical community;

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reliability and quality of published data should be very strictly analyzed to determine the degree of evidence;

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undeclared conflict of interest does not always allow the authors to take an objective approach to the interpretation of the results of the study when it is published. The above problems are offset, provided further analysis of the data obtained in the process of treatment of patients, i.e., post-registration stage, after the release of the drug in wide usage.

The module determine the degree of validity and reliability of the data. One of the features of data representation

in the system is that any knowledge coming to the base, always contain information about their source. The system includes a tool based on the reputation and credibility of the expert. Having the ability to store an unlimited number of views of experts regarding a single fact, the system can combine the viewpoints of different specialists. In the analysis of knowledge, centered around one fact, the system using methods of statistical analysis and identifies probable errors, deviations from the General opinion, then sends them to moderatorow check. In the process of clinical application of the database is the rating of the expert is later used to evaluate the significance of the opinions entered into the system. The degree of trust and the importance of expert opinion is directly proportional to the number of his correct answers.

To solve this problem developed a special classifier of medical concepts (terms) that can automatically expand in the process of its use in clinical work [2].

Task 7 Joint base medical knowledge as a common core to develop expert systems in medicine. Based on the technology created by joint base medical Task 8 Service knowledge base and the constant updating of information. The complexity arising from the service knowledge base, is the need for constant updating of information after the rapid development of medicine, which requires the involvement of a large number of experts from different fields of medical science and the development of special

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technologies of analysis and data collection from primary sources. In the end, this translates into large financial and time costs. According to most experts, this problem causes high investment risk of such projects [1].

To solve this problem it is necessary to develop an intelligent system that needs to analyze the archive of electronic medical records, organize the medical history (including subformation text fields), carry out statistical processing of clinical material and to create registers of clinical practice according to the tasks.

In the end, the output is in the form of a report with the possibility of detailed visualization of the stages of analysis, as well as original registers, constructed in accordance with the tasks.

CONCLUSIONS This technology will solve not only the problem of constant updating of the knowledge base, but also in the future will allow to cope with the following tasks: •

create registers of the effectiveness of methods for the diagnosis, treatment and prevention of various diseases and develop optimal treatment standards for specific clinical cases;

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to identify the depth of causal relationships between risk factors and pathological processes on the basis of multivariate correlation analysis of a large amount of medical data;

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conduct a retrospective epidemiological analysis with the identification of patterns of occurrence and spread of diseases of various etiologies.

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