L. A. Leung, J. Slagle, S. Finkelstein, W. Warwick
{"title":"Temporal reasoning in medicine with an example in cystic fibrosis patient management-artificial intelligence mini-tutorial. 2","authors":"L. A. Leung, J. Slagle, S. Finkelstein, W. Warwick","doi":"10.1109/ECBS.1988.5443","DOIUrl":null,"url":null,"abstract":"For pt.1 see ibid., p.33-42. A brief history of temporal reasoning in medical applications is given. An expert system called Monitor is described that can perform the following tasks: the incremental evaluation of the data; retrieval of historical data for comparison purposes; determination of the validity of the data cross references over time; assessment of the disease progression by abstracting trends and changes from the time-ordered data; and extraction of duration information from the symptom data. An initial evaluation of Monitor using 111 cases has shown that the system can classify 88% of the cases correctly.<<ETX>>","PeriodicalId":291071,"journal":{"name":"Proceedings of the Symposium on the Engineering of Computer-Based Medical","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Symposium on the Engineering of Computer-Based Medical","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECBS.1988.5443","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
For pt.1 see ibid., p.33-42. A brief history of temporal reasoning in medical applications is given. An expert system called Monitor is described that can perform the following tasks: the incremental evaluation of the data; retrieval of historical data for comparison purposes; determination of the validity of the data cross references over time; assessment of the disease progression by abstracting trends and changes from the time-ordered data; and extraction of duration information from the symptom data. An initial evaluation of Monitor using 111 cases has shown that the system can classify 88% of the cases correctly.<>