Big data are coming to psychiatry: a general introduction

International Journal of Bipolar Disorders

Volumn 3, Issue 1, 2015, Pages

  Monteith, Scott ^a   Glenn, Tasha ^b   Geddes, John ^c   Bauer, Michael ^d  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b ChronoRecord Association Inc   (United States)

^c WARNEFORD HOSPITAL   (United Kingdom)

^d UNIVERSITY HOSPITAL CARL GUSTAV CARUS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; BIPOLAR DISORDER; COMPUTER PREDICTION; DATA ANALYSIS; DRUG SURVEILLANCE PROGRAM; ELECTRONIC MEDICAL RECORD; FUNCTIONAL GENOMICS; FUNCTIONAL PROTEOMICS; GENERAL PRACTICE; HUMAN; INFORMATION PROCESSING; INTERNET; METABOLOMICS; NATIONAL HEALTH ORGANIZATION; PHARMACY; PRIORITY JOURNAL; PSYCHIATRY; RANDOMIZED CONTROLLED TRIAL (TOPIC); REFERENCE DATABASE; REVIEW; SCHIZOPHRENIA;

EID: 85046284551     PISSN: None     EISSN: 21947511     Source Type: Journal    
DOI: 10.1186/s40345-015-0038-9     Document Type: Review

References (133)

1. Altman RB, Ashley EA. Using “big data” to dissect clinical heterogeneity. Circulation. 2015;131:232–3.
2. ATKearney. Big data and the creative destruction of today’s business models. 2013. http://www.atkearney.com/documents/10192/698536/Big+Data+and+the+Creative+Destruction+of+Todays+Business+Models.pdf/f05aed38-6c26-431d-8500-d75a2c384919. Accessed 12 June 2015.
3. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46:399–424.
4. Bauer M, Glenn T, Alda M, Sagduyu K, Marsh W, Grof P, et al. Drug treatment patterns in bipolar disorder: analysis of long-term self-reported data. Int J Bipolar Disord. 2013a;1:5.
5. Bauer R, Glenn T, Alda M, Sagduyu K, Marsh W, Grof P, et al. Antidepressant dosage taken by patients with bipolar disorder: factors associated with irregularity. Int J Bipolar Disord. 2013b;9(1):26.
6. Ben-Ari A, Hammond K. Text mining the EMR for modeling and predicting suicidal behavior among US veterans of the 1991 Persian Gulf War. In: 2015 48th Hawaii international conference on system sciences (HICSS), IEEE; 2015. p. 3168–75.
7. Bhugra D, Easter A, Mallaris Y, Gupta S. Clinical decision making in psychiatry by psychiatrists. Acta Psychiatr Scand. 2011;124:403–11.
8. Birney E. The making of ENCODE: lessons for big-data projects. Nature. 2012;489:49–51.
9. Bollier D, Firestone CM, Bollier D, Firestone CM. The promise and peril of big data. Washington: Aspen Institute, Communications and Society Program; 2010.
10. Bourgeois FC, Olson KL, Mandl KD. Patients treated at multiple acute health care facilities: quantifying information fragmentation. Arch Intern Med. 2010;170:1989–95.
11. Boyd D, Crawford K. Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc. 2012;15:662–79.
12. Buneman P, Khanna S, Tan, WC. Data provenance: some basic issues. In: FST TCS 2000: foundations of software technology and theoretical computer science. Berlin: Springer; 2000. p. 87–93.
13. Castro VM, Minnier J, Murphy SN, Kohane I, Churchill SE, Gainer V, et al. Validation of electronic health record phenotyping of bipolar disorder cases and controls. Am J Psychiatry. 2015;172:363–72.
14. CDC. CDC/National Center for Health Statistics. 2014. http://www.cdc.gov/nchs/fastats/physician-visits.htm. Accessed 12 June 2015.
15. Celi LA, Mark RG, Stone DJ, Montgomery RA. “Big data” in the intensive care unit. Closing the data loop. Am J Respir Crit Care Med. 2013;187:1157–60.
16. CERN. Animation shows LHC data processing. 2015. http://home.web.cern.ch/about/updates/2013/04/animation-shows-lhc-data-processing. Accessed 12 June 2015.
17. Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev. 2010;67:503–27.
18. Cohen R, Elhadad M, Elhadad N. Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies. BMC Bioinform. 2013;14:10.
19. Cohen SB. AHRQ statistical brief #392: the concentration and persistence in the level of health expenditures over time: estimates for the U.S. population, 2009–2010. 2012. http://meps.ahrq.gov/data_files/publications/st392/stat392.shtml. Accessed 12 June 2015.
20. Cook BL, Zuvekas SH, Carson N, Wayne GF, Vesper A, McGuire TG. Assessing racial/ethnic disparities in treatment across episodes of mental health care. Health Serv Res. 2014;49:206–29.
21. Cooke CR, Iwashyna TJ. Using existing data to address important clinical questions in critical care. Crit Care Med. 2013;41:886–96.
22. Collins FS, Hudson KL, Briggs JP, Lauer MS. PCORnet: turning a dream into reality. J Am Med Inform Assoc. 2014;21:576–7.
23. Curran PJ, Hussong AM. Integrative data analysis: the simultaneous analysis of multiple data sets. Psychol Methods. 2009;14:81–100.
24. De Domenico M, Lima A, Musolesi M. Interdependence and predictability of human mobility and social interactions. Pervasive Mob Comput. 2013;9:798–807.
25. Dormuth CR, Patrick AR, Shrank WH, Wright JM, Glynn RJ, Sutherland J, Brookhart MA. Statin adherence and risk of accidents: a cautionary tale. Circulation. 2009;119:2051–7.
26. Drazen JM, Gelijns AC. Statin strikeout. N Engl J Med. 2014;370:2240–1.
27. Eagle N, Pentland AS, Lazer D. Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci USA. 2009;106:15274–8.
28. Economist. Data, data everywhere. The Economist. 2010. http://www.emc.com/collateral/analyst-reports/ar-the-economist-data-data-everywhere.pdf. Accessed 12 June 2015.
29. Fan J, Han F, Liu H. Challenges of big data analysis. Natl Sci Rev. 2014;1:293–314.
30. FDA. Sentinel initiative. 2014. http://www.fda.gov/Safety/FDAsSentinelInitiative/ucm2007250.htm. Accessed 12 June 2015.
31. FitzHenry F, Murff HJ, Matheny ME, Gentry N, Fielstein EM, Brown SH, et al. Exploring the frontier of electronic health record surveillance: the case of postoperative complications. Med Care. 2013;51:509–16.
32. Gallagher P. Welcome and opening remarks NIST joint cloud and big data workshop. 2013. http://www.nist.gov/director/speeches/cloud-big-data-011513.cfm. Accessed 12 June 2015.
33. Gardner RC, Burke JF, Nettiksimmons J, Kaup A, Barnes DE, Yaffe K. Dementia risk after traumatic brain injury vs nonbrain trauma: the role of age and severity. JAMA Neurol. 2014;71:1490–7.
34. Ghassemi M, Marshall J, Singh N, Stone DJ, Celi LA. Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. Chest. 2014;145:745–52.
35. Giordano SH, Kuo YF, Duan Z, Hortobagyi GN, Freeman J, Goodwin JS. Limits of observational data in determining outcomes from cancer therapy. Cancer. 2008;112:2456–66.
36. Glass TA, Goodman SN, Hernán MA, Samet JM. Causal inference in public health. Annu Rev Public Health. 2013;34:61–75.
37. Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Curr Psychiatry Rep. 2014;16:523.
38. Goldman D, Fastenau J, Dirani R, Helland E, Joyce G, Conrad R, et al. Medicaid prior authorization policies and imprisonment among patients with schizophrenia. Am J Manag Care. 2014;20:577–86.
39. Gonzalez MC, Hidalgo CA, Barabasi AL. Understanding individual human mobility patterns. Nature. 2008;453:779–82.
40. Grether JK, Anderson MC, Croen LA, Smith D, Windham GC. Risk of autism and increasing maternal and paternal age in a large north American population. Am J Epidemiol. 2009;170:1118–26.
41. Groves RM. Three eras of survey research. Public Opin Q. 2011;75:861–71.
42. Halamka JD. Early experiences with big data at an academic medical center. Health Aff (Millwood). 2014;33:1132–8.
43. Halamka J. The cost of storing patient records. 2011. http://geekdoctor.blogspot.com/2011/04/cost-of-storing-patient-records.html. Accessed 12 June 2015.
44. Hargittai E. Is bigger always better? Potential biases of big data derived from social network sites. Ann Am Acad Pol Soc Sci. 2015;659:63–76.
45. Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012;91:1010–21.
46. Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV, et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care. 2013;51(8 Suppl 3):S30–7.
47. HHS. More physicians and hospitals are using EHRs than before. 2014. http://www.hhs.gov/news/press/2014pres/08/20140807a.html. Accessed 12 June 2015.
48. Hill G. Looking at data from a different perspective: an interview with Sean Patrick Murphy. Big Data Innovation Magazine; 2013.
49. Hoertel N, Le Strat Y, Lavaud P, Dubertret C, Limosin F. Generalizability of clinical trial results for bipolar disorder to community samples: findings from the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry. 2013;74:265–70.
50. Hogan WR, Wagner MM. Accuracy of data in computer-based patient records. J Am Med Inform Assoc. 1997;4:342–55.
51. Holowka DW, Marx BP, Gates MA, Litman HJ, Ranganathan G, Rosen RC, et al. PTSD diagnostic validity in Veterans Affairs electronic records of Iraq and Afghanistan veterans. J Consult Clin Psychol. 2014;82:569–79.
52. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013;20:117–21.
53. Hripcsak G, Knirsch C, Zhou L, Wilcox A, Melton G. Bias associated with mining electronic health records. J Biomed Discov Collab. 2011;6:48–52.
54. Huybrechts KF, Schneeweiss S, Gerhard T, Olfson M, Avorn J, Levin R, et al. Comparative safety of antipsychotic medications in nursing home residents. J Am Geriatr Soc. 2012;60:420–9.
55. IBM. Big data at the speed of business. 2015. http://www-01.ibm.com/software/data/bigdata/what-is-big-data.html. Accessed 12 June 2015.
56. IBM. Data-driven healthcare organizations use big data analytics for big gains. 2013. http://www-03.ibm.com/industries/ca/en/healthcare/documents/Data_driven_healthcare_organizations_use_big_data_analytics_for_big_gains.pdf. Accessed 12 June 2015.
57. IDC. The digital universe. Driving data growth in healthcare. 2014. http://www.emc.com/analyst-report/digital-universe-healthcare-vertical-report-ar.pdf. Accessed 12 June 2015.
58. Ince D. The problem of reproducibility. Chance. 2012; 25.3. http://chance.amstat.org/2012/09/prob-reproducibility/. Accessed 12 June 2015.
59. Ioannidis JP. Informed consent, big data, and the oxymoron of research that is not research. Am J Bioeth. 2013;13:40–2.
60. Ioannidis JP, Khoury MJ. Are randomized trials obsolete or more important than ever in the genomic era? Genome Med. 2013;5:32.
61. Ioannidis JP, Allison DB, Ball CA, Coulibaly I, Cui X, Culhane AC, et al. Repeatability of published microarray gene expression analyses. Nat Genet. 2009;41:149–55.
62. Jackson LA, Jackson ML, Nelson JC, Neuzil KM, Weiss NS. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol. 2006;35:337–44.
63. Jacobs A. The pathologies of big data. Commun ACM. 2009;52:36–44.
64. Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, et al. Big data and its technical challenges. Commun ACM. 2014;57:86–94.
65. Jashinsky J, Burton SH, Hanson CL, West J, Giraud-Carrier C, Barnes MD, et al. Tracking suicide risk factors through Twitter in the US. Crisis. 2014;35:51–9.
66. Jasser SA, Garvin JH, Wiedemer N, Roche D, Gallagher RM. Information technology in mental health research: impediments and implications in one chronic pain study population. Pain Med. 2007;8(s3):S176–81.
67. Joseph AJ, Tandon N, Yang LH, Duckworth K, Torous J, Seidman LJ, et al. #Schizophrenia: use and misuse on Twitter. Schizophr Res. 2015;165:111–5.
68. Kaplan RM, Chambers DA, Glasgow RE. Big data and large sample size: a cautionary note on the potential for bias. Clin Transl Sci. 2014;7:342–6.
69. Kelly J. Big data vendor revenue and market forecast 2013–2017. 2014. http://wikibon.org/wiki/v/Big_Data_Vendor_Revenue_and_Market_Forecast_2013-2017. Accessed 12 June 2015.
70. Kessler RC, Warner CH, Ivany C, Petukhova MV, Rose S, Bromet EJ, et al. Predicting suicides after psychiatric hospitalization in US Army soldiers: the army study to assess risk and resilience in service members (Army STARRS). JAMA Psychiatry. 2015;72:49–57.
71. Kho ME, Duffett M, Willison DJ, Cook DJ, Brouwers MC. Written informed consent and selection bias in observational studies using medical records: systematic review. BMJ. 2009;338:b866.
72. Kielman J, Thomas J, May R. Foundations and frontiers in visual analytics. Inf Vis. 2009;8:239–46.
73. Knopman DS, Petersen RC, Rocca WA, Larson EB, Ganguli M. Passive case-finding for Alzheimer’s disease and dementia in two U.S. communities. Alzheimers Dement. 2011;7:53–60.
74. Kosinski M, Stillwell D, Graepel T. Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci USA. 2013;110:5802–5.
75. Kouzes RT, Anderson GA, Elbert ST, Gorton I, Gracio DK. The changing paradigm of data-intensive computing. Computer. 2009;1:26–34.
76. Kraska T. Finding the needle in the big data systems haystack. IEEE Internet Comput. 2013;17:84–6.
77. Lagomasino IT, Stockdale SE, Miranda J. Racial-ethnic composition of provider practices and disparities in treatment of depression and anxiety, 2003–2007. Psychiatr Serv. 2011;62:1019–25.
78. Laney D. 3-D data management: controlling data volume, velocity. Gartner. 2001. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 12 June 2015.
79. Lauer MS, D’Agostino RB Sr. The randomized registry trial—the next disruptive technology in clinical research? N Engl J Med. 2013;369:1579–81.
80. Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science. 2014;343:1203–5.
81. March JS, Silva SG, Compton S, Shapiro M, Califf R, Krishnan R. The case for practical clinical trials in psychiatry. Am J Psychiatry. 2005;162:836–46.
82. Mark TL, Levit KR, Buck JA. Datapoints: psychotropic drug prescriptions by medical specialty. Psychiatr Serv. 2009;60:1167.
83. McIntyre RS, Cha DS, Jerrell JM, Swardfager W, Kim RD, Costa LG, et al. Advancing biomarker research: utilizing ‘Big Data’ approaches for the characterization and prevention of bipolar disorder. Bipolar Disord. 2014;16:531–47.
84. Mislove A, Lehmann S, Ahn YY, Onnela JP, Rosenquist JN. Understanding the demographics of Twitter users, 5th ICWSM; 2011. p. 11.
85. MIT Sloan and IBM. Analytics: the new path to value. 2010. http://sloanreview.mit.edu/reports/analytics-the-new-path-to-value/. Accessed 12 June 2015.
86. Monniaux D. The pitfalls of verifying floating-point computations. ACM Trans Progr Lang Syst (TOPLAS). 2008;30:12.
87. Monroe BL, Pan J, Roberts ME, Sen M, Sinclair B. No! Formal theory, causal inference, and big data are not contradictory trends in political science. PS Polit Sci Polit. 2015;48:71–4.
88. Moses C, Celi LA, Marshall J. Pharmacovigilance: an active surveillance system to proactively identify risks for adverse events. Popul Health Manag. 2013;16:147–9.
89. Munson L. Facebook: at least 67 million accounts are fake. 2014. https://nakedsecurity.sophos.com/2014/02/10/facebook-at-least-67-million-accounts-are-fake/. Accessed 12 June 2015.
90. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309:1351–2.
91. Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, et al. Systems biology of vaccination for seasonal influenza in humans. Nat Immunol. 2011;12:786–95.
92. NCI. An assessment of the impact of the NCI cancer biomedical informatics grid (CaBig). 2011. http://deainfo.nci.nih.gov/advisory/bsa/bsa0311/caBIGfinalReport.pdf. Accessed 12 June 2015.
93. NSA. Searching the future enterprise. Next Wave. 2014;20:3. https://www.nsa.gov/research/tnw/tnw203/article8.shtml. Accessed 12 June 2015.
94. O’Donoghue SI, Gavin AC, Gehlenborg N, Goodsell DS, Hériché JK, Nielsen CB, et al. Visualizing biological data—now and in the future. Nat Methods. 2010;7(3 Suppl):S2–4.
95. O’Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005;40:1620–39.
96. Orlovska S, Pedersen MS, Benros ME, Mortensen PB, Agerbo E, Nordentoft M. Head injury as risk factor for psychiatric disorders: a nationwide register-based follow-up study of 113,906 persons with head injury. Am J Psychiatry. 2014;171:463–9.
97. Overhage JM, Overhage LM. Sensible use of observational clinical data. Stat Methods Med Res. 2013;22:7–13.
98. Patty JW, Penn EM. Analyzing big data: social choice and measurement. PS Polit Sci Polit. 2015;48:95–101.
99. Perlis RH, Iosifescu DV, Castro VM, Murphy SN, Gainer VS, Minnier J. Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol Med. 2012;42:41–50.
100. Pivovarov R, Albers DJ, Sepulveda JL, Elhadad N. Identifying and mitigating biases in EHR laboratory tests. J Biomed Inform. 2014;51:24–34.
101. Polsky D, Eremina D, Hess G, Hill J, Hulnick S, Roumm A, et al. The importance of clinical variables in comparative analyses using propensity-score matching: the case of ESA costs for the treatment of chemotherapy-induced anaemia. Pharmacoeconomics. 2009;27:755–65.
102. Potash JB. Electronic medical records: fast track to big data in bipolar disorder. Am J Psychiatry. 2015;172:310–1.
103. Rohrer R, Paul CL, Nebesh B. Visual analytics for big data. Next Wave. 2014;20:1–17.
104. Rusanov A, Weiskopf NG, Wang S, Weng C. Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research. BMC Med Inform Decis Mak. 2014;14:51.
105. Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58:323–37.
106. Shneiderman B, Plaisant C, Hesse BW. Improving healthcare with interactive visualization. Computer. 2013;5:58–66.
107. Sinha A, Hripcsak G, Markatou M. Large datasets in biomedicine: a discussion of salient analytic issues. J Am Med Inform Assoc. 2009;16:759–67.
108. Smith A. Pew research. 6 new facts about Facebook. 2014. http://www.pewresearch.org/fact-tank/2014/02/03/6-new-facts-about-facebook/. Accessed 12 June 2015.
109. Spiegelhalter DJ. Statistics. The future lies in uncertainty. Science. 2014;18(345):264–5.
110. Srinivasan S, Moser RP, Willis G, Riley W, Alexander M, Berrigan D, et al. Small is essential: importance of subpopulation research in cancer control. Am J Public Health. 2015;105(Suppl 3):S371–3.
111. Starren J, Williams MS, Bottinger EP. Crossing the omic chasm: a time for omic ancillary systems. JAMA. 2013;309:1237–8.
112. Szlezák N, Evers M, Wang J, Pérez L. The role of big data and advanced analytics in drug discovery, development, and commercialization. Clin Pharmacol Ther. 2014;95:492–5.
113. Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci. 2010;25:1–21.
114. Titiunik R. Can big data solve the fundamental problem of causal inference? PS Polit Sci Polit. 2015;48(1):75–9.
115. Toh S, García Rodríguez LA, Hernán MA. Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records. Pharmacoepidemiol Drug Saf. 2011;20:849–57.
116. Tukey JW. The collected works of John W. Tukey: graphics 1965–1985, vol V. In: Cleveland WS, editor. Statistics/probability series. Belmont: Chapman and Hall; 1988. p. 421.
117. VA. Million veteran program. 2015. http://www.research.va.gov/mvp/. Accessed 12 June 2015.
118. Valuck RJ, Anderson HO, Libby AM, Brandt E, Bryan C, Allen RR, et al. Enhancing electronic health record measurement of depression severity and suicide ideation: a distributed ambulatory research in therapeutics network (DARTNet) study. J Am Board Fam Med. 2012;25:582–93.
119. Van Horn JD, Toga AW. Human neuroimaging as a “Big Data” science. Brain Imaging Behav. 2014;8:323–31.
120. Varian HR. Beyond big data. Bus Econ. 2014;49:27–31.
121. Wang W, Krishnan E. Big data and clinicians: a review on the state of the science. JMIR Med Inform. 2014;2:e1.
122. Ware C. Information visualization: perception for design. 3rd ed. Waltham: Elsevier; 2012.
123. Weinberger D. To know, but not understand: David Weinberger on science and big data. The Atlantic. 2012. http://www.theatlantic.com/technology/archive/2012/01/to-know-but-not-understand-david-weinberger-on-science-and-big-data/250820/. Accessed 12 June 2015.
124. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, et al. The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 2012;8(1 Suppl):S1–68.
125. Wells BJ, Chagin KM, Nowacki AS, Kattan MW. Strategies for handling missing data in electronic health record derived data. EGEMS (Wash DC). 2013;1:1035.
126. Wiechers IR, Leslie DL, Rosenheck RA. Prescribing of psychotropic medications to patients without a psychiatric diagnosis. Psychiatr Serv. 2013;64:1243–8.
127. Wilbur DC. Digital pathology: get on board—the train is leaving the station. Cancer Cytopathol. 2014;122:791–5.
128. Wolfe PJ. Making sense of big data. Proc Natl Acad Sci USA. 2013;110:18031–2.
129. Wong PC, Shen HW, Johnson CR, Chen C, Ross RB. The top 10 challenges in extreme-scale visual analytics. IEEE Comput Graph Appl. 2012;32:63.
130. Wu LT, Gersing KR, Swartz MS, Burchett B, Li TK, Blazer DG. Using electronic health records data to assess comorbidities of substance use and psychiatric diagnoses and treatment settings among adults. J Psychiatr Res. 2013;47:555–63.
131. Yancey AK, Ortega AN, Kumanyika SK. Effective recruitment and retention of minority research participants. Annu Rev Public Health. 2006;27:1–28.
132. Youyou W, Kosinski M, Stillwell D. Computer-based personality judgments are more accurate than those made by humans. Proc Natl Acad Sci USA. 2015;112:1036–40.
133. Zarin DA, Young JL, West JC. Challenges to evidence-based medicine: a comparison of patients and treatments in randomized controlled trials with patients and treatments in a practice research network. Soc Psychiatry Psychiatr Epidemiol. 2005;40:27–35.