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The Impact of “Possible Patients” on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria...16th World Congress of Medical and Health Informatics: Precision Healthcare Through Informatics (MedInfo2017 Hangzhou China August 21-25 2017

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 diffe...

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Published in:Studies in Health Technology & Informatics Vol. 245; pp. 432 - 437
Main Authors: Rina Kagawa, Yoshimasa Kawazoe, Emiko Shinohara, Takeshi Imai, Kazuhiko Ohe
Format: proceedings research tables/charts Journal Article
Published: Sage Publications Inc. 2017
Online Access:View this record in EBSCOhost
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      dt: 2017
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        10.3233/978-1-61499-830-3-432
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        atl: The Impact of “Possible Patients” on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria...16th World Congress of Medical and Health Informatics: Precision Healthcare Through Informatics (MedInfo2017 Hangzhou China August 21-25 2017
      aug:
        au:
          Rina Kagawa
          Yoshimasa Kawazoe
          Emiko Shinohara
          Takeshi Imai
          Kazuhiko Ohe
        affil: Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Japan.
      sug:
        subj:
          Phenotype
          Algorithms Evaluation
          Electronic Health Records
          Data Curation
          Health Information Management
          Data Analysis
          Human
          Male
          Female
          Funding Source
          Male
          Female
      ab: 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.
      pubtype: Academic Journal
      doctype:
        proceedings
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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