Evaluating the predictability of medical conditions from social media posts

PLoS ONE

Volumn 14, Issue 6, 2019, Pages

  Merchant, Raina M ^a,b   Asch, David A ^a,c,d   Crutchley, Patrick ^a,e   Ungar, Lyle H ^a,d,e,f   Guntuku, Sharath C ^a,b,f   Eichstaedt, Johannes C ^e   Hill, Shawndra ^a,g   Padrez, Kevin ^a   Smith, Robert J ^a   Andrew Schwartz, H ^a,e,h  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c VETERANS AFFAIRS MEDICAL CENTER   (United States)

^d UNIVERSITY OF PENNSYLVANIA   (United States)

^e UNIVERSITY OF PENNSYLVANIA   (United States)

^f UNIVERSITY OF PENNSYLVANIA   (United States)

^g MICROSOFT RESEARCH   (United States)

^h Stony Brook University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ANXIETY; ARTICLE; BEHAVIOR; DEPRESSION; DIABETES MELLITUS; DISEASE EXACERBATION; EVALUATION STUDY; FEMALE; GENOME; HUMAN; INFORMED CONSENT; MAJOR CLINICAL STUDY; MALE; MENTAL HEALTH; PHYSICAL DISEASE; PREDICTION; PSYCHOSIS; RISK FACTOR; SOCIAL MEDIA; BIOLOGICAL MODEL; DISEASES; ELECTRONIC HEALTH RECORD; LANGUAGE; MENTAL DISEASE;

DEPRESSION; DEPRESSIVE DISORDER; DIABETES MELLITUS; DIAGNOSIS; DISEASE; ELECTRONIC HEALTH RECORDS; FEMALE; HUMANS; LANGUAGE; MALE; MENTAL DISORDERS; MENTAL HEALTH; MODELS, BIOLOGICAL; SOCIAL MEDIA;

EID: 85067288709     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0215476     Document Type: Article

References (32)

1. Pentland A, Lazer D, Brewer D, Heibeck T. Using reality mining to improve public health and medicine. Studies in health technology and informatics. 2009; 149:93–102. PMID: 19745474
2. Lazer D, Pentland A, Adamic L, Aral S, Barabasi AL, Brewer D, et al. Social science. Computational social science. Science. 2009; 323(5915):721–3. https://doi.org/10.1126/science.1167742 PMID: 19197046
3. Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, Agrawal M, et al. Personality, gender, and age in the language of social media: the open-vocabulary approach. PloS one. 2013; 8(9): e73791. https://doi.org/10.1371/journal.pone.0073791 PMID: 24086296
4. Eichstaedt JC, Smith RJ, Merchant RM, Ungar LH, Crutchley P, Preotiuc-Pietro D, et al. Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences of the United States of America. 2018; 115(44):11203–8. https://doi.org/10.1073/pnas.1802331115 PMID: 30322910
5. Chunara R, Andrews JR, Brownstein JS. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. The American journal of tropical medicine and hygiene. 2012; 86(1):39–45. https://doi.org/10.4269/ajtmh.2012.11-0597 PMID: 22232449
6. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PloS one. 2010; 5(11):e14118. https://doi.org/10.1371/journal.pone.0014118 PMID: 21124761
7. Guntuku SC, Ramsay JR, Merchant RM, Ungar LH. Language of ADHD in Adults on Social Media. J Atten Disord. 2017:1087054717738083.
8. Eichstaedt JC, Schwartz HA, Kern ML, Park G, Labarthe DR, Merchant RM, et al. Psychological Language on Twitter Predicts County-Level Heart Disease Mortality. Psychol Sci. 2015; 26(2):159–69. https://doi.org/10.1177/0956797614557867 PMID: 25605707
9. Wong CA, Sap M, Schwartz A, Town R, Baker T, Ungar L, et al. Twitter sentiment predicts Affordable Care Act marketplace enrollment. Journal of medical Internet research. 2015; 17(2):e51. https://doi.org/10.2196/jmir.3812 PMID: 25707038
10. Asch DA, Rader DJ, Merchant RM. Mining the social mediome. Trends Mol Med. 2015; 21(9):528–9. https://doi.org/10.1016/j.molmed.2015.06.004 PMID: 26341614
11. Guntuku SC, Yaden DB, Kern ML, Ungar LH, Eichstaedt JC. Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences. 2017; 18:43–9.
12. Coppersmith G, Dredze M, Harman C. Quantifying mental health signals in Twitter. Workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality. Association for Computational Linguistics Baltimore, Maryland USA. 2014:51–60.
13. Padrez KA, Ungar L, Schwartz HA, Smith RJ, Hill S, Antanavicius T, et al. Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department. BMJ quality & safety. 2016; 25(6):414–23.
14. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003; 3(4–5):993–1022.
15. Elixhauser A, Steiner C, Harris DR, Coffey RN. Comorbidity measures for use with administrative data. Medical care. 1998; 36(1):8–27. PMID: 9431328
16. Jaidka K, Guntuku S, A. SH, Buffone A, Ungar L. Facebook versus. Twitter: Differences in self-disclosure and trait prediction. Proceedings of the International AAAI Conference on Web and Social Media 2018.
17. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction: Springer New York; 2009 2001.
18. Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006; 63(3):3–42.
19. Dwass M. Modified Randomization Tests for Nonparametric Hypotheses. Annals of Mathematical Statistics. 1957; 28(1):181–7.
20. Benjamini Y, Hocberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 1995; 57(1):289–300.
21. How CB, Ming KE, Chin CY. Does religious affiliation influence glycaemic control in primary care patients with type 2 diabetes mellitus? Ment Health Fam Med. 2011; 8(1):21–8. PMID: 22479289
22. Newlin K, Dyess SM, Allard E, Chase S, Melkus GD. A methodological review of faith-based health promotion literature: advancing the science to expand delivery of diabetes education to Black Americans. Journal of religion and health. 2012; 51(4):1075–97. https://doi.org/10.1007/s10943-011-9481-9 PMID: 21487842
23. Baig AA, Benitez A, Locklin CA, Gao Y, Lee SM, Quinn MT, et al. Picture Good Health: A Church-Based Self-Management Intervention Among Latino Adults with Diabetes. Journal of general internal medicine. 2015; 30(10):1481–90. https://doi.org/10.1007/s11606-015-3339-x PMID: 25920468
24. Snowdon DA, Kemper SJ, Mortimer JA, Greiner LH, Wekstein DR, Markesbery WR. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life. Findings from the Nun Study. JAMA: the journal of the American Medical Association. 1996; 275(7):528–32. PMID: 8606473
25. Libert T, Grande D, Asch DA. What web browsing reveals about your health. Bmj. 2015; 351:h5974. https://doi.org/10.1136/bmj.h5974 PMID: 26572794
26. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1):139–40. https://doi.org/10.1093/bioinformatics/btp616 PMID: 19910308
27. Ruder TD, Hatch GM, Ampanozi G, Thali MJ, Fischer N. Suicide announcement on Facebook. Crisis. 2011; 32(5):280–2. https://doi.org/10.1027/0227-5910/a000086 PMID: 21940257
28. Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. Journal of Big Data. 2016; 3(1):9.
29. Guntuku SC, Buffone A, Jaidka K, Eichstaedt J, Ungar L. Understanding and Measuring Psychological Stress using Social Media. In Thirteenth International AAAI Conference on Web and Social Media. 2019.
30. Obermeyer Z, Emanuel EJ. Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016; 375(13):1216–9. https://doi.org/10.1056/NEJMp1606181 PMID: 27682033
31. Morgan DJ, Bame B, Zimand P, Dooley P, Thom KA, Harris AD, et al. Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions. JAMA Netw Open. 2019; 2(3): e190348. https://doi.org/10.1001/jamanetworkopen.2019.0348 PMID: 30848808
32. Marafino BJ, Park M, Davies JM, Thombley R, Luft HS, Sing DC, et al. Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data. JAMA Netw Open. 2018; 1(8):e185097. https://doi.org/10.1001/jamanetworkopen.2018.5097 PMID: 30646310