Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review | Zendy

Seyedeh Neelufar Payrovnaziri | Zendy; Zhaoyi Chen | Zendy; Pablo RengifoMoreno | Zendy; Tim Miller | Zendy; Jiang Bian | Zendy; Jonathan H. Chen | Zendy; Xiuwen Liu | Zendy; Zhe He | Zendy

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Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

Author(s) -

Seyedeh Neelufar Payrovnaziri,

Zhaoyi Chen,

Pablo RengifoMoreno,

Tim Miller,

Jiang Bian,

Jonathan H. Chen,

Xiuwen Liu,

Zhe He

Publication year - 2020

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/ocaa053

Subject(s) - computer science , electronic health record , health records , data science , artificial intelligence , real world evidence , medicine , health care , economics , economic growth

To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions.

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