The Impact of Possible Patients on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria
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.
Clinical Phenotyping Data Annotation Electronic Health Records
Rina Kagawa Yoshimasa Kawazoe Emiko Shinohara Takeshi Imai Kazuhiko Ohe
Department of Biomedical Informatics,Graduate School of Medicine,The University of Tokyo,Japan Department of Healthcare Information Management,The University of Tokyo Hospital,Japan Center for Disease Biology and Integrative Medicine,The University of Tokyo,Japan Department of Biomedical Informatics,Graduate School of Medicine,The University of Tokyo,Japan;Depar
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
432-436
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）