{"title":"概率诊断推理:迈向提高诊断效率","authors":"G. Provan","doi":"10.1109/CAIA.1994.323642","DOIUrl":null,"url":null,"abstract":"The author describes a new approximation method which can significantly improve the computational efficiency of Bayesian networks. He applies this technique to the diagnosis of acute abdominal pain, with good results. This approach is based on using a reduced set of the model parameters for diagnostic reasoning. The tradeoffs in diagnostic accuracy required to obtain increased computational efficiency (due to the smaller models) are carefully specified using a variety of statistical metrics.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probabilistic diagnostic reasoning: towards improving diagnostic efficiency\",\"authors\":\"G. Provan\",\"doi\":\"10.1109/CAIA.1994.323642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The author describes a new approximation method which can significantly improve the computational efficiency of Bayesian networks. He applies this technique to the diagnosis of acute abdominal pain, with good results. This approach is based on using a reduced set of the model parameters for diagnostic reasoning. The tradeoffs in diagnostic accuracy required to obtain increased computational efficiency (due to the smaller models) are carefully specified using a variety of statistical metrics.<<ETX>>\",\"PeriodicalId\":297396,\"journal\":{\"name\":\"Proceedings of the Tenth Conference on Artificial Intelligence for Applications\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Tenth Conference on Artificial Intelligence for Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAIA.1994.323642\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIA.1994.323642","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Probabilistic diagnostic reasoning: towards improving diagnostic efficiency
The author describes a new approximation method which can significantly improve the computational efficiency of Bayesian networks. He applies this technique to the diagnosis of acute abdominal pain, with good results. This approach is based on using a reduced set of the model parameters for diagnostic reasoning. The tradeoffs in diagnostic accuracy required to obtain increased computational efficiency (due to the smaller models) are carefully specified using a variety of statistical metrics.<>
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