{"title":"Kohonen self organizing maps and expert system for blood classification","authors":"N. Elfadil, M. K. Hani, S. M. Nor, S. Hussein","doi":"10.1109/TENCON.2001.949575","DOIUrl":null,"url":null,"abstract":"Information gathering in medicine generally follows a set of sequence: an interview with the patient, an examination, and one or more laboratory tests to support the working diagnosis. Building a knowledge base from observing a medical examination, however, is risky. Medical decision-making relies on imprecise information gathered in a variety of ways and interpreted in a largely intuitive fashion. This paper proposes a novel method that integrates neural network and expert system paradigms to produce an automated knowledge acquisition system. This system will produce symbolic knowledge from medical data automatically.","PeriodicalId":358168,"journal":{"name":"Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON.2001.949575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Information gathering in medicine generally follows a set of sequence: an interview with the patient, an examination, and one or more laboratory tests to support the working diagnosis. Building a knowledge base from observing a medical examination, however, is risky. Medical decision-making relies on imprecise information gathered in a variety of ways and interpreted in a largely intuitive fashion. This paper proposes a novel method that integrates neural network and expert system paradigms to produce an automated knowledge acquisition system. This system will produce symbolic knowledge from medical data automatically.