Toward personalizing treatment for depression: Predicting diagnosis and severity

Journal of the American Medical Informatics Association

Volumn 21, Issue 6, 2014, Pages 1069-1075

  Huang, Sandy H ^a   LePendu, Paea ^a   Iyer, Srinivasan V ^a   Tai Seale, Ming ^b   Carrell, David ^c   Shah, Nigam H ^a  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b PALO ALTO MEDICAL FOUNDATION RESEARCH INSTITUTE   (United States)

^c GROUP HEALTH RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIDEPRESSANT AGENT;

ADULT; ARTICLE; COMPUTER MODEL; DEPRESSION; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIAGNOSTIC VALUE; DIFFERENTIAL DIAGNOSIS; DISEASE SEVERITY; ELECTRONIC MEDICAL RECORD; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; MENTAL PATIENT; PERSONALIZED MEDICINE; PSYCHIATRIC DIAGNOSIS; PSYCHOTHERAPY; RECEIVER OPERATING CHARACTERISTIC; TREATMENT RESPONSE; CLASSIFICATION; DEPRESSIVE DISORDER; PSYCHOLOGICAL MODEL; SEVERITY OF ILLNESS INDEX;

DEPRESSIVE DISORDER; DIAGNOSIS, DIFFERENTIAL; ELECTRONIC HEALTH RECORDS; FEMALE; HUMANS; INDIVIDUALIZED MEDICINE; MALE; MODELS, PSYCHOLOGICAL; ROC CURVE; SEVERITY OF ILLNESS INDEX;

EID: 84929045024     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2014-002733     Document Type: Article

References (36)

1. Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: A meta-analysis. Lancet 2009;374:609-19.
2. Sharp LK, Lipsky MS. Screening for depression across the lifespan: a review of measures for use in primary care settings. Am Fam Physician 2002;66:1001-8.
3. Cain RA. Navigating the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study: practical outcomes and implications for depression treatment in primary care. Prim Care 2007;34:505-19.
4. Kamphuis MH, Stegenga BT, Zuithoff NP, et al. Does recognition of depression in primary care affect outcome? The PREDICT-NL study. Fam Pract 2012;29:16-23.
5. Scott J, Eccleston D, Boys R. Can we predict the persistence of depression? Br J Psychiatry 1992;161:633-7.
6. Simon GE, Perlis RH. Personalized medicine for depression: can we match patients with treatments? Am J Psychiatry 2010;167:1445-55.
7. Rush AJ, Trivedi MH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 2006;163:1905-17.
8. Nutting PA, Rost K, Dickinson M, et al. Barriers to initiating depression treatment in primary care practice. J Gen Intern Med 2002;17:103-11.
9. Wei WQ, Leibson CL, Ransom JE, et al. Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus. J Am Med Inform Assoc 2012;19:219-24.
10. Kho AN, Hayes MG, Rasmussen-Torvik L, et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Inform Assoc 2012;19:212-18.
11. Peissig PL, Rasmussen LV, Berg RL, et al. Importance of multi-modal approaches to effectively identify cataract cases from electronic health records. J Am Med Inform Assoc 2012;19:225-34.
12. Conway M, Berg RL, Carrell D, et al. Analyzing the heterogeneity and complexity of electronic health record oriented phenotyping algorithms. AMIA Annu Symp Proc 2011;2011:274-83.
13. Trivedi MH, Rush AJ, Wisniewski SR, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry 2006;163:28-40.
14. Khan A, Leventhal RM, Khan SR, et al. Severity of depression and response to antidepressants and placebo: an analysis of the Food and Drug Administration database. J Clin Psychopharmacol 2002;22:40-5.
15. Fournier JC, DeRubeis RJ, Hollon SD, et al. Antidepressant drug effects and depression severity: a patient-level meta-analysis. JAMA 2010;303:47-53.
16. Valuck RJ, Anderson HO, Libby AM, 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.
17. LePendu P, Iyer SV, Bauer-Mehren A, et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther 2013;93:547-55.
18. Bodenreider O, McCray AT. Exploring semantic groups through visual approaches. J Biomed Inform 2003;36:414-32.
19. Xu R, Musen MA, Shah NH. A comprehensive analysis of five million UMLS Metathesaurus terms using eighteen million MEDLINE citations. AMIA Annu Symp Proc 2010;2010:907-11.
20. Wu ST, Liu H, Li D, et al. Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis. J Am Med Inform Assoc 2012;19:e149-56.
21. Harkema H, Dowling JN, Thornblade T, et al. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform 2009;42:839-51.
22. United States National Library of Medicine. RxNorm website. http://www.nlm.nih. gov/research/umls/rxnorm/ (updated 5 December 2011) (accessed 6 Dec 2013).
23. Townsend L, Walkup JT, Crystal S, et al. Mini-sentinel systematic evaluation of health outcome of interest definitions for studies using administrative data. http:// www.mini-sentinel.org/work_products/HealthOutcomes/MS_HOI_DepressionReport. pdf (updated 8 December 2010) (accessed 6 Dec 2013).
24. Loftus EV Jr, Guerin A, Yu AP, et al. Increased risks of developing anxiety and depression in young patients with Crohn's disease. Am J Gastroenterol 2011;106:1670-7.
25. Valuck RJ, Orton HD, Libby AM. Antidepressant discontinuation and risk of suicide attempt: a retrospective, nested case-control study. J Clin Psychiatry 2009;70:1069-77.
26. Hasin DS, Goodwin RD, Stinson FS, et al. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry 2005;62:1097-106.
27. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Stat Methodol 1996;58:267-88.
28. Dougherty J, Kohavi R, Sahami M. Supervised and unsupervised discretization of continuous features. Proceedings of the Twelfth International Conference on Machine Learning; 1995;1995:194-202.
29. Hsu C-N, Huang H-J, Wong T-T. Implications of the Dirichlet assumption for discretization of continuous variables in naive Bayesian classifiers. Machine Learning 2003;53:235-63.
30. Rost K, Smith R, Matthews DB, et al. The deliberate misdiagnosis of major depression in primary care. Arch Fam Med 1994;3:333-7.
31. Townsend L, Walkup JT, Crystal S, et al. A systematic review of validated methods for identifying depression using administrative data. Pharmacoepidemiol Drug Saf 2012;21(Suppl 1):163-73.
32. Hebbring SJ. The challenges, advantages and future of phenome-wide association studies. Immunology 2014;141:157-65.
33. Pendergrass SA, Brown-Gentry K, Dudek SM, et al. The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet Epidemiol 2011;35:410-22.
34. Carroll RJ, Eyler AE, Denny JC. Naive electronic health record phenotype identification for rheumatoid arthritis. AMIA Annu Symp Proc 2011;2011:189-96.
35. Vanderbilt University. PheKB website. http://www.phekb.org/ (accessed 6 Dec 2013). http://www.phekb.org/ (accessed 6 Dec 2013).
36. Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken) 2010;62:1120-7.