Evaluating the predictability of medical conditions from social media posts
PLoS ONE, ISSN: 1932-6203, Vol: 14, Issue: 6, Page: e0215476
2019
- 57Citations
- 185Captures
- 37Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations57
- Citation Indexes57
- CrossRef57
- 53
- Captures185
- Readers185
- 185
- Mentions37
- News Mentions37
- News37
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Article Description
We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients’ consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients’ Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85067288709&origin=inward; http://dx.doi.org/10.1371/journal.pone.0215476; http://www.ncbi.nlm.nih.gov/pubmed/31206534; https://dx.plos.org/10.1371/journal.pone.0215476; https://dx.doi.org/10.1371/journal.pone.0215476; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215476
Public Library of Science (PLoS)
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