The Impact of "Possible Patients" on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria
The Impact of "Possible Patients" on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria
复制标题
“可能的患者”对表型算法的影响：电子表型算法只能通过共享详细注释标准来重现
DOI：
10.3233/978-1-61499-830-3-432
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发表时间：
2017
影响因子：
--
通讯作者：
K. Ohe
中科院分区：
文献类型：
--
作者：
Rina Kagawa;Yoshimasa Kawazoe;E. Shinohara;Takeshi Imai;K. Ohe
表型分析是一种基于电子健康记录（EHR）识别诊断患有特定疾病的患者的自动化技术。为了评估表型分型算法，这应该是可重复的，作为金标准的EHR注释是至关重要的。然而，我们发现不同类型的EHR不能被明确地注释到CASE或CONTROLs中。这种“可能的患者”对表型分析算法的影响是未知的。为了评估这些问题，对于四种慢性疾病，我们通过使用不直接涉及疾病的信息来注释EHR，并为每种疾病开发了两种类型的表型分析算法。我们确认每种疾病都包括不同类型的可能患者。表型分型算法的性能取决于可能的患者是否被认为是CASE，这与算法类型无关。我们的结果表明，研究人员必须共享对可能的患者进行分类的注释标准，以重现表型算法。
Phenotyping is an automated technique for identifying patients diagnosed with a particular disease based on electronic health records (EHRs). To evaluate phenotyping algorithms, which should be reproducible, the annotation of EHRs as a gold standard is critical. However, we have found that the different types of EHRs cannot be definitively annotated into CASEs or CONTROLs. The influence of such "possible patients" on phenotyping algorithms is unknown. To assess these issues, for four chronic diseases, we annotated EHRs by using information not directly referring to the diseases and developed two types of phenotyping algorithms for each disease. We confirmed that each disease included different types of possible patients. The performance of phenotyping algorithms differed depending on whether possible patients were considered as CASEs, and this was independent of the type of algorithms. Our results indicate that researchers must share annotation criteria for classifying the possible patients to reproduce phenotyping algorithms.