{"title":"基于粗糙集的肉芽学检测疾病并发症的可能性","authors":"S. Tsumoto","doi":"10.1109/NAFIPS.2003.1226794","DOIUrl":null,"url":null,"abstract":"One of the most important problems with medical expert systems is that they cannot make a differential diagnosis with complicated cases. This paper reviews reasoning about complications from the viewpoint of information granulation and proposes an approach to extracting rules for diagnosis of complications from clinical datasets. The illustrative example show that rough set based granular computing gives a nice framework to detect the complications.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"265 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting possibility of complications of diseases using rough set based granulation\",\"authors\":\"S. Tsumoto\",\"doi\":\"10.1109/NAFIPS.2003.1226794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the most important problems with medical expert systems is that they cannot make a differential diagnosis with complicated cases. This paper reviews reasoning about complications from the viewpoint of information granulation and proposes an approach to extracting rules for diagnosis of complications from clinical datasets. The illustrative example show that rough set based granular computing gives a nice framework to detect the complications.\",\"PeriodicalId\":153530,\"journal\":{\"name\":\"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003\",\"volume\":\"265 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.2003.1226794\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2003.1226794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting possibility of complications of diseases using rough set based granulation
One of the most important problems with medical expert systems is that they cannot make a differential diagnosis with complicated cases. This paper reviews reasoning about complications from the viewpoint of information granulation and proposes an approach to extracting rules for diagnosis of complications from clinical datasets. The illustrative example show that rough set based granular computing gives a nice framework to detect the complications.