A systematic literature review of machine learning in online personal health data | Zendy

Zhijun Yin | Zendy; Lina Sulieman | Zendy; Bradley Malin | Zendy

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A systematic literature review of machine learning in online personal health data

Author(s) -

Zhijun Yin,

Lina Sulieman,

Bradley Malin

Publication year - 2019

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/ocz009

Subject(s) - computer science , social media , naive bayes classifier , systematic review , artificial intelligence , inference , medline , machine learning , coping (psychology) , world wide web , data science , support vector machine , psychology , information retrieval , clinical psychology , political science , law

User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.

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