Evaluating the predictability of medical conditions from social media posts
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 diseas...
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| Veröffentlicht in: | PloS one 2019-06, Vol.14 (6), p.e0215476 |
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| creator | Merchant, Raina M Asch, David A Crutchley, Patrick Ungar, Lyle H Guntuku, Sharath C Eichstaedt, Johannes C Hill, Shawndra Padrez, Kevin Smith, Robert J Schwartz, H Andrew |
| 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. |
| doi_str_mv | 10.1371/journal.pone.0215476 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0215476</identifier><identifier>PMID: 31206534</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Alzheimer's disease ; Analysis ; Anxiety ; Artificial intelligence ; Big Data ; Biology and Life Sciences ; Comorbidity ; Computer and Information Sciences ; Demographic variables ; Demographics ; Depression ; Depression (Mood disorder) ; Depressive Disorder ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus - diagnosis ; Diagnosis ; Digital media ; Disease ; Electronic Health Records ; Electronic medical records ; Electronic records ; Emergency medical care ; Environmental assessment ; Environmental diseases ; Environmental illness ; Epidemiology ; Female ; Gene expression ; Genomes ; Genomics ; Health and safety screening ; Health care ; Health risk assessment ; Health risks ; Humans ; Information science ; Language ; Machine learning ; Male ; Medical records ; Medical research ; Medicine ; Medicine and Health Sciences ; Mental depression ; Mental Disorders ; Mental Health ; Models, Biological ; Natural language processing ; Patients ; Physical Sciences ; Predictions ; Psychology ; Psychosis ; Psychotic disorders ; Research and Analysis Methods ; Risk analysis ; Risk factors ; Social Media ; Social networks ; Social Sciences</subject><ispartof>PloS one, 2019-06, Vol.14 (6), p.e0215476</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. 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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.</description><subject>Alzheimer's disease</subject><subject>Analysis</subject><subject>Anxiety</subject><subject>Artificial intelligence</subject><subject>Big Data</subject><subject>Biology and Life Sciences</subject><subject>Comorbidity</subject><subject>Computer and Information Sciences</subject><subject>Demographic variables</subject><subject>Demographics</subject><subject>Depression</subject><subject>Depression (Mood disorder)</subject><subject>Depressive Disorder</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus - diagnosis</subject><subject>Diagnosis</subject><subject>Digital media</subject><subject>Disease</subject><subject>Electronic Health Records</subject><subject>Electronic medical records</subject><subject>Electronic records</subject><subject>Emergency medical care</subject><subject>Environmental assessment</subject><subject>Environmental diseases</subject><subject>Environmental illness</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Gene expression</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Health and safety screening</subject><subject>Health care</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Humans</subject><subject>Information science</subject><subject>Language</subject><subject>Machine learning</subject><subject>Male</subject><subject>Medical records</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Mental depression</subject><subject>Mental Disorders</subject><subject>Mental Health</subject><subject>Models, Biological</subject><subject>Natural language processing</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Psychology</subject><subject>Psychosis</subject><subject>Psychotic disorders</subject><subject>Research and Analysis Methods</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Social Media</subject><subject>Social networks</subject><subject>Social 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the predictability of medical conditions from social media posts</title><author>Merchant, Raina M ; Asch, David A ; Crutchley, Patrick ; Ungar, Lyle H ; Guntuku, Sharath C ; Eichstaedt, Johannes C ; Hill, Shawndra ; Padrez, Kevin ; Smith, Robert J ; Schwartz, H Andrew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-f4f7ed3518c48566ed208c8571d0311ccf5b737a38225c7de19379c25081935c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Alzheimer's disease</topic><topic>Analysis</topic><topic>Anxiety</topic><topic>Artificial intelligence</topic><topic>Big Data</topic><topic>Biology and Life Sciences</topic><topic>Comorbidity</topic><topic>Computer and Information Sciences</topic><topic>Demographic variables</topic><topic>Demographics</topic><topic>Depression</topic><topic>Depression (Mood disorder)</topic><topic>Depressive 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| subjects | Alzheimer's disease Analysis Anxiety Artificial intelligence Big Data Biology and Life Sciences Comorbidity Computer and Information Sciences Demographic variables Demographics Depression Depression (Mood disorder) Depressive Disorder Diabetes Diabetes mellitus Diabetes Mellitus - diagnosis Diagnosis Digital media Disease Electronic Health Records Electronic medical records Electronic records Emergency medical care Environmental assessment Environmental diseases Environmental illness Epidemiology Female Gene expression Genomes Genomics Health and safety screening Health care Health risk assessment Health risks Humans Information science Language Machine learning Male Medical records Medical research Medicine Medicine and Health Sciences Mental depression Mental Disorders Mental Health Models, Biological Natural language processing Patients Physical Sciences Predictions Psychology Psychosis Psychotic disorders Research and Analysis Methods Risk analysis Risk factors Social Media Social networks Social Sciences |
| title | Evaluating the predictability of medical conditions from social media posts |
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