概率诊断推理:迈向提高诊断效率

Proceedings of the Tenth Conference on Artificial Intelligence for Applications Pub Date : 1994-03-01 DOI:10.1109/CAIA.1994.323642

G. Provan

{"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}

引用次数: 0

摘要

作者描述了一种新的近似方法，可以显著提高贝叶斯网络的计算效率。他将这项技术应用于急性腹痛的诊断，取得了良好的效果。该方法基于使用简化的模型参数集进行诊断推理。为了获得更高的计算效率(由于较小的模型)，需要在诊断准确性方面进行权衡，并使用各种统计指标仔细指定

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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.<>

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the Tenth Conference on Artificial Intelligence for Applications

自引率

0.00%

发文量