• Title/Summary/Keyword: Medical Expert Systems

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Medical Data Base Controlled By Medical Knowledge Base

  • Chernyakhovskaya, Mery Y.;Gribova, Valeriya V.;Kleshchev, Alexander S.
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2001.01a
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    • pp.343-351
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    • 2001
  • World practice is evidence of that computer systems of an intellectual support of medical activities bound up with examination of patients, their diagnosis, therapy and so on are the most effective means for attainment of a high level of physician\`s qualification. Such systems must contain large knowledge bases consistent with the modern level of science and practice. To from large knowledge bases for such systems it is necessary to have a medical ontology model reflecting contemporary notions of medicine. This paper presents a description of an observation ontology, knowledge base for the physician of general tipe, architecture, functions and implementation of problem independent shell of the system for intellectual supporting patient examination and mathematical model of the dialog. The system can be used by the following specialist: therapeutist, surgeon, gynecologist, urologist, otolaryngologist, ophthalmologist, endocrinologist, neuropathologist and immunologist. The system supports a high level of examination of patients, delivers doctors from routine work upon filling in case records and also automatically forms a computer archives of case records. The archives can be used for any statistical data processing, for producing accounts and also for debugging of knowledge bases of expert systems. Besides that, the system can be used for rise of medical education level of students, doctors in internship, staff physicians and postgraduate students.

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Development of a knowledge-based medical expert system to infer supportive treatment suggestions for pediatric patients

  • Ertugrul, Duygu Celik;Ulusoy, Ali Hakan
    • ETRI Journal
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    • v.41 no.4
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    • pp.515-527
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    • 2019
  • This paper discusses the design, implementation, and potential use of an ontology-based mobile pediatric consultation and monitoring system, which is a smart healthcare expert system for pediatric patients. The proposed system provides remote consultation and monitoring of pediatric patients during their illness at places distant from medical service areas. The system not only shares instant medical data with a pediatrician but also examines the data as a smart medical assistant to detect any emergency situation. In addition, it uses an inference engine to infer instant suggestions for performing certain initial medical treatment steps when necessary. The applied methodologies and main technical contributions have three aspects: (a) pediatric consultation and monitoring ontology, (b) semantic Web rule knowledge base, and (c) inference engine. Two case studies with real pediatric patients are provided and discussed. The reported results of the applied case studies are promising, and they demonstrate the applicability, effectiveness, and efficiency of the proposed approach.

Dynamic Knowledge Map and SQL-based Inference Architecture for Medical Diagnostic Systems

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.1
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    • pp.101-107
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    • 2006
  • In this research, we propose a hybrid inference architecture for medical diagnosis based on dynamic knowledge map (DKM) and relational database (RDB). Conventional expert systems (ES) and developing tools of ES has some limitations such as, 1) time consumption to extend the knowledge base (KB), 2) difficulty to change the inference path, 3) inflexible use of inference functions and operators. To overcome these Limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. The DKM also can help the knowledge engineers to change the inference path rapidly and easily. Then, RDB and its management systems help us to transform the relationships from diagram to relational table.

Design the Expert Systems for the Stroke Early Diagnosis based in Web Environment (Web환경기반의 뇌졸중 초기진단 전문가시스템 설계)

  • 이주원;정원근;박성록;이건기
    • Proceedings of the IEEK Conference
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    • 2002.06e
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    • pp.269-272
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    • 2002
  • In this study, we designed the expert system for the diagnosis of stroke. The causes of stroke in central nervous systems are very diverse, so a doctor who treats the patients with stroke must have the expert knowledge for the quick and correct diagnosis and for the adequate medical management. But the primary physician who engaged in the primary care of the patient with stroke does not have the export knowledge for the stroke. So, we need to develop the expert system for assisting the diagnosis of stroke. Also the diagnosis system can be used as simulator for the medical students who study the neurology. In this study, we developed the diagnosis expert system that offer a pathological name provided by artificial neural networks. And we designed the inference engine and interfaces. The artificial neural network is a system that provide a possible diagnosis of stroke. We implemented the system using Windows2000 Server, IIS5.0 and ASP.

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A Development of Forward Inference Engine and Expert Systems based on Relational Database and SQL

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09b
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    • pp.49-52
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    • 2003
  • In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

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Development of Expert Systems based on Dynamic Knowledge Map and DBMS (동적지식도와 데이터베이스관리시스템 기반의 전문가시스템 개발)

  • Jin Sung, Kim
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.10a
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    • pp.568-571
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    • 2004
  • In this study, we propose an efficient expert system (ES) construction mechanism by using dynamic knowledge map (DKM) and database management systems (DBMS). Generally, traditional ES and ES developing tools has some limitations such as, 1) a lot of time to extend the knowledge base (KB), 2) too difficult to change the inference path, 3) inflexible use of inference functions and operators. First, to overcome these limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. Then, elation database (RDB) and its management systems will help to transform the relationships from diagram to relational table. Therefore, our mechanism can help the ES or KBS (Knowledge-Based Systems) developers in several ways efficiently. In the experiment section, we used medical data to show the efficiency of our mechanism. Experimental results with various disease show that the mechanism is superior in terms of extension ability and flexible inference.

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RDB-based Automatic Knowledge Acquisition and Forward Inference Mechanism for Self-Evolving Expert Systems

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.6
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    • pp.743-748
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    • 2003
  • In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database (RDB) and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert system. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently. and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

A Study on Reasoning for Medical Expert Systems (의료용 전문가 시스템에서 추론에 관한 연구)

  • Kim, Jin-Sang;Shin, Yang-Kyu
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.2
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    • pp.359-367
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    • 1999
  • We investigate a logical approach to represent medical knowledge, reason deductively and diagnostically. It is suggested that medical knowledge-bases can be formulated as a set of sentences stated in classical logic where each sentence reflects a doctor's knowledge about the human anatomy or his/her view of patient's symptoms. It is also suggested that a form of temporal reasoning can be captured within the same framework because each sentence can have a different truth value based on time. We apply our logical framework to formalize diagnostic reasoning, where the primary cause of illness is chosen among the set of minimal causation on the basis of abductive hypotheses. Most of our examples are given in the context of medical expert systems.

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The Internet based Expert System for Gastroenteritis (소화기 질환을 위한 인터넷 기반 전문가 시스템)

  • 하상호;유일대;천인국;박상흠;김선주
    • Journal of Korea Multimedia Society
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    • v.6 no.6
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    • pp.1079-1087
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    • 2003
  • A lot of expert systems have been developed in the area of medical diagnosis and prescription, but there has been relatively little effort to develop medical expert systems using domestic technologies. In this paper, a medical expert system for gastroenteritis is developed, which can be easily used on the Internet. This system is implemented using Java technologies and Jess, which is a Java expert system shell. Therefore. the system can be used independently from a specific platform on the Internet. In addition, facts and rules of a knowledge base in the system ate represented separately making the system updatable and expandable with ease.

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Knowledge Based Recommender System for Disease Diagnostic and Treatment Using Adaptive Fuzzy-Blocks

  • Navin K.;Mukesh Krishnan M. B.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.2
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    • pp.284-310
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    • 2024
  • Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.