{"title":"Combining multi-level evidence for medical record retrieval","authors":"Dongqing Zhu, Ben Carterette","doi":"10.1145/2389707.2389717","DOIUrl":null,"url":null,"abstract":"The increasing prevalence of electronic health records containing rich information about a patient's health and physical condition has the potential to transform research in health and medicine. In this work, we present a health record search system for finding patients matching certain inclusion criteria (specified as keyword queries) for clinical studies. In particular, our system aggregates multi-level evidence and combines proven statistical IR models, both in an innovative way, and achieves a 20% MAP (mean average precision) improvement over a strong baseline. Moreover, our cross-validation results show that the overall performance of our system is comparable to other top-performing systems on the same task.","PeriodicalId":92138,"journal":{"name":"SHB'12 : proceedings of the 2012 ACM International Workshop on Smart Health and Wellbeing : October 29, 2012, Maui, Hawaii, USA. International Workshop on Smart Health and Wellbeing (2012 : Maui, Hawaii)","volume":"15 1","pages":"49-56"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SHB'12 : proceedings of the 2012 ACM International Workshop on Smart Health and Wellbeing : October 29, 2012, Maui, Hawaii, USA. International Workshop on Smart Health and Wellbeing (2012 : Maui, Hawaii)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2389707.2389717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22

Abstract

The increasing prevalence of electronic health records containing rich information about a patient's health and physical condition has the potential to transform research in health and medicine. In this work, we present a health record search system for finding patients matching certain inclusion criteria (specified as keyword queries) for clinical studies. In particular, our system aggregates multi-level evidence and combines proven statistical IR models, both in an innovative way, and achieves a 20% MAP (mean average precision) improvement over a strong baseline. Moreover, our cross-validation results show that the overall performance of our system is comparable to other top-performing systems on the same task.
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结合多层次证据进行病历检索
包含有关病人健康和身体状况的丰富信息的电子健康记录日益普及,有可能改变健康和医学研究。在这项工作中,我们提出了一个健康记录搜索系统,用于查找符合临床研究的某些纳入标准(指定为关键字查询)的患者。特别是,我们的系统以创新的方式汇集了多层次的证据,并结合了经过验证的统计IR模型,并在强基线的基础上实现了20%的MAP(平均精度)提高。此外,我们的交叉验证结果表明,在相同的任务上,我们的系统的整体性能与其他性能最好的系统相当。
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