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

ABSTRACT:

Objective

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.

Materials and methods

We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review.

R

SUBMITTER: Yin Z 

PROVIDER: S-EPMC7647332 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature