Leveraging Error-Prone Algorithm-Derived Phenotypes: Enhancing Association Studies for Risk Factors in Ehr Data

Author:

Lu Yiwen,Tong Jiayi,Chubak Jessica,Lumley Thomas,Hubbard Rebecca A.,Xu Hua,Chen Yong

Publisher

Elsevier BV

Reference15 articles.

1. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability;J C Kirby;Journal of the American Medical Informatics Association,2016

2. PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records;N S Zheng;Journal of the American Medical Informatics Association,2020

3. Inflation of type I error rates due to differential misclassification in EHR-derived outcomes: Empirical illustration using breast cancer recurrence;Y Chen;Pharmacoepidemiol Drug Saf,2019

4. An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies;R Duan;AMIA Annual Symposium Proceedings,2016

5. Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records;T Williamson;The Annals of Family Medicine,2014

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