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

Zhijun Yin · Lina Mahmoud Sulieman · Bradley Malin

70 citations0/0 files storedJournal of the American Medical Informatics Association

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Mental Health via Writing Artificial Intelligence in Healthcare Sentiment Analysis and Opinion Mining

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. RESULTS: We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support. CONCLUSIONS: The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.

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References

2015Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement 2013A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication 2016Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words 2014Are Health Behavior Change Interventions That Use Online Social Networks Effective? A Systematic Review 2016Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media 2014Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment 2014Empowering patients through social media: The benefits and challenges 2017Characterisation of mental health conditions in social media using Informed Deep Learning

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