SynthesisJournal of the American Medical Informatics Association : JAMIA2019
A systematic literature review of machine learning in online personal health data.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Developing a standardized protocol for computational sentiment analysis research using health-related social media data.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.Journal of medical Internet research · 2025Article
- Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality.Npj mental health research · 2024Article
- Examining Online Behaviors of Adult-Child and Spousal Caregivers for People Living With Alzheimer Disease or Related Dementias: Comparative Study in an Open Online Community.Journal of medical Internet research · 2023Article
- Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study.Journal of medical Internet research · 2023Article
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges.World journal of psychiatry · 2022Review
- Artificial intelligence: A strategic opportunity for enhancing primary care in South Africa.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2022Article
- Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach.Journal of medical Internet research · 2022Article
- Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review.BMC medical research methodology · 2022Article
- Housing safety and health academic and public opinion mining from 1945 to 2021: PRISMA, cluster analysis, and natural language processing approaches.Frontiers in public health · 2022Article
- Automatic detection of depression symptoms in twitter using multimodal analysis.The Journal of supercomputing · 2022Article
- Semi-automated Clinical Content Curation of COVID-19 Chatbot Remote Patient Monitoring Solution.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Online Disease Identification and Diagnosis and Treatment Based on Machine Learning Technology.Journal of healthcare engineering · 2022Article
- Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier.Healthcare (Basel, Switzerland) · 2021Article
- Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification.European journal of investigation in health, psychology and education · 2021Article
- Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis.JMIR medical informatics · 2021Article
- Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study.JMIR mHealth and uHealth · 2021Article
- Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.Journal of medical Internet research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveUser-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
methodsWe 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.
resultsWe 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.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.