A systematic literature review of machine learning in online personal health data.
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 ef...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 26; no. 6; pp. 561 - 577 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136465795&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136465795 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jun2019 vid: 26 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 136465795 136465795 NLM30908576 136465795 10.1093/jamia/ocz009 NLM30908576 136465795 ppf: 561 ppct: 16 formats: tig: atl: A systematic literature review of machine learning in online personal health data. aug: au: Yin, Zhijun Sulieman, Lina M Malin, Bradley A affil: Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA sug: subj: Internet Social Media Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Coping Strategies Questionnaire Scales ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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