Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review | Zendy

Benjamin A. Goldstein | Zendy; Ann Marie Návar | Zendy; Michael Pencina | Zendy; John P. A. Ioannidis | Zendy

Open Access

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

Author(s) -

Benjamin A. Goldstein,

Ann Marie Návar,

Michael Pencina,

John P. A. Ioannidis

Publication year - 2016

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocw042

Subject(s) - documentation , predictive modelling , leverage (statistics) , electronic health record , health records , medicine , sample size determination , missing data , medline , data science , data mining , computer science , statistics , health care , machine learning , mathematics , political science , law , economics , programming language , economic growth

Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data.

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