1. Karstoft, K-I, Galatzer-Levy, IR, Galatzer-Levy, IR, et al. Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry 2015; 15(1): 30.
Google Scholar | Crossref | Medline | ISI2. Galatzer-Levy, IR, Karstoft, K-I, Statnikov, A, et al. Quantitative forecasting of PTSD from early trauma responses: a machine learning application. J Psychiatric Research 2014; 59: 68–76.
Google Scholar | Crossref | Medline | ISI3. Russo, J, Katon, W, Zatzick, D. The development of a population-based automated screening procedure for PTSD in acutely injured hospitalized trauma survivors. Gen Hospital Psychiatry 2013; 35(5): 485–491.
Google Scholar | Crossref | Medline4. Harrington, KM, Quaden, R, Stein, MB, et al. Validation of an electronic medical record-based algorithm for identifying posttraumatic stress disorder in U.S. veterans. J Traumatic Stress 2019; 32(2): 226–237.
Google Scholar | Crossref | Medline5. Shiner, B, Levis, M, Dufort, VM, et al. Improvements to PTSD quality metrics with natural language processing. J Eval Clin Pract 2021.
Google Scholar | Crossref | Medline6. Leightley, D, Williamson, V, Darby, J, et al. Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort. J Ment Health 2019; 28(1): 34–41.
Google Scholar | Crossref | Medline7. He, Q, Veldkamp, BP, Glas, CAW, et al. Automated assessment of patients’ self-narratives for posttraumatic stress disorder screening using natural language processing and text mining. Assessment 2017; 24(2): 157–172.
Google Scholar | SAGE Journals | ISI8. Marinić, I, Supek, F, Kovacić, Z, et al. Posttraumatic stress disorder: diagnostic data analysis by data mining methodology. Croat Medical Journal 2007; 48(2): 185–197.
Google Scholar | Medline9. Rosellini, AJ, Dussaillant, F, Zubizarreta, JR, et al. Predicting posttraumatic stress disorder following a natural disaster. J Psychiatric Research 2018; 96: 15–22.
Google Scholar | Crossref | Medline10. Kessler, RC, Rose, S, Koenen, KC, et al. How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO world mental health surveys. World Psychiatry 2014; 13(3): 265–274.
Google Scholar | Crossref | Medline11. Papini, S, Pisner, D, Shumake, J, et al. Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization. J Anxiety Disorders 2018; 60: 35–42.
Google Scholar | Crossref | Medline12. Saxe, GN, Ma, S, Ren, J, et al. Machine learning methods to predict child posttraumatic stress: a proof of concept study. BMC Psychiatry 2017; 17(1): 223.
Google Scholar | Crossref | Medline13. Karstoft, K-I, Statnikov, A, Andersen, SB, et al. Early identification of posttraumatic stress following military deployment: application of machine learning methods to a prospective study of Danish soldiers. J Affective Disorders 2015; 184: 170–175.
Google Scholar | Crossref | Medline | ISI14. Breen, MS, Thomas, KGF, Baldwin, DS, et al. Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: an exploratory machine‐learning study. Hum Psychopharmacol Clin Exp 2019; 34(2): e2691.
Google Scholar | Crossref | Medline15. McDonald, AD, Sasangohar, F, Jatav, A, et al. Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: a supervised machine learning approach. IISE Trans Healthc Syst Eng 2019; 9(3): 201–211.
Google Scholar | Crossref16. Ma, S, Galatzer-Levy, IR, Wang, X, et al. A first step towards a clinical decision support system for post-traumatic stress disorders. In: AMIA Annual Symposium Proceedings, Chicago, IL, . Bethesda, MD: American Medical Informatics Association, 2016, pp. 837–843.
Google Scholar17. Peker, M . A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM. J Medical Systems 2016; 40(5): 116.
Google Scholar | Crossref | Medline18. Williamson, T, Green, ME, Birtwhistle, R, et al. Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records. Ann Fam Med 2014; 12(4): 367–372.
Google Scholar | Crossref | Medline | ISI19. Sareen, J, Cox, BJ, Stein, MB, et al. Physical and mental comorbidity, disability, and suicidal behavior associated with posttraumatic stress disorder in a large community sample. Psychosomatic Medicine 2007; 69(3): 242–248.
Google Scholar | Crossref | Medline | ISI20. Pedregosa, F, Varoquaux, G, Gramfort, A, et al. Scikit-learn: machine learning in Python. The J Machine Learn Research 2011; 12: 2825–2830.
Google Scholar | ISI21. Sklearn . “sklearn.ensemble.RandomForestClassifier”. scikit-learn developers. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html (accessed on 15 June 2021)
Google Scholar22. Zhang, Y, Jin, R, Zhou, ZH. Understanding bag-of-words model: a statistical framework. Int J Machine Learn Cybernetics 2010; 1(1–4): 43–52.
Google Scholar | Crossref23. Goldberg, Y . A primer on neural network models for natural language processing. J Artif Intelligence Res 2016; 57: 345–420.
Google Scholar | Crossref24. Y, Kim . Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746–1751, Doha, Qatar, October 2014. Association for Computational Linguistics.
Google Scholar25. Brownlee, J . Deep learning for natural language processing: develop deep learning models for your natural language problems. Machine Learn Mastery 2017.
Google Scholar26. Keras . The functional API. San Francisco, CA: Keras. https://keras.io/guides/functional_api/(accessed Jun. 22, 2020).
Google Scholar27. Hur, JH, Ihm, SY, Park, YH. A variable impacts measurement in random forest for mobile cloud computing. Wireless Commun Mobile Comput 2017; 2017: 1–13.
Google Scholar | Crossref28. Breiman, L, Friedman, J, Stone, CJ, et al. Classification and regression trees. Boca Raton, FL: CRC Press, 1984.
Google Scholar29. Strobl, C, Boulesteix, A-L, Zeileis, A, et al. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 2007; 8(1): 25.
Google Scholar | Crossref | Medline | ISI30. Scipy . Stats.pearsonr. The SciPy community. https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html (accessed Mar. 15, 2021).
Google Scholar31. Holowka, DW, Marx, BP, Gates, MA, et al. PTSD diagnostic validity in Veterans affairs electronic records of Iraq and Afghanistan veterans. J Consulting Clinical Psychology 2014; 82(4): 569–579.
Google Scholar | Crossref | Medline32. Shickel, B, Tighe, PJ, Bihorac, A, et al. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal Biomedical Health Informatics 2017; 22(5): 1589–1604.
Google Scholar | Crossref | Medline33. Diao, X, Huo, Y, Yan, Z, et al. An application of machine learning to etiological diagnosis of secondary hypertension: retrospective study using electronic medical records. JMIR Med Inform 2021; 9(1): e19739.
Google Scholar | Crossref | Medline34. Liu, J, Zhang, Z, Razavian, N. Deep EHR: Chronic disease prediction using medical notes, 2018 (arXiv46 preprint arXiv:1808.04928.
Google Scholar35. Baxter, SL, Marks, C, Kuo, T-T, et al. Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records. Am Journal Ophthalmol 2019; 208: 30–40.
Google Scholar | Crossref | Medline36. Judd, M, Zulkernine, F, Wolfrom, B, et al. Detecting low back pain from clinical narratives using machine learning approaches. In: International Conference on Database and Expert Systems Applications, Regensburg, Germany, . Berlin, Germany: Springer; 2018, pp. 126–137.
Google Scholar | Crossref37. LaFreniere, D, Zulkernine, F, Barber, D, et al. Using machine learning to predict hypertension from a clinical dataset. In: IEEE symposium series on computational intelligence (SSCI), Athens, Greece, . IEEE; 2016, pp. 1–7.
Google Scholar | Crossref38. Cheng, Y, Wang, F, Zhang, P, et al. Risk prediction with electronic health records: a deep learning approach. In: Venkatasubramanian, SC, Meira, W (eds) Proceedings of the 2016 SIAM international conference on data mining. Philadelphia, PA: Society for Industrial and Applied Mathematics; 2016, pp. 432–440.
Google Scholar | Crossref39. Fleming, G, Bruce, P. Responsible data science. Mountain View, CA: Google Play Books, 2021, p. 74.
Google Scholar40. Andre, Ye . “When and Why Tree-Based Models (Often) Outperform Neural Networks”. Medium. https://towardsdatascience.com/when-and-why-tree-based-models-often-outperform-neural-networks-ceba9ecd0fd8” (accessed on 15 June 2021)
Google Scholar41. Chung, MC, Allen, RD, Dennis, I. The impact of self-efficacy, alexithymia and multiple traumas on posttraumatic stress disorder and psychiatric co-morbidity following epileptic seizures: a moderated mediation analysis. Psychiatry Research 2013; 210(3): 1033–1041.
Google Scholar | Crossref | Medline42. Zijlmans, M, van Campen, JS, de Weerd, A. Post traumatic stress-sensitive epilepsy. Seizure 2017; 52: 20–21.
Google Scholar | Crossref | Medline43. Asmundson, GJG, Stapleton, JA. Associations between dimensions of anxiety sensitivity and PTSD symptom clusters in active‐duty police officers. Cogn Behaviour Therapy 2008; 37(2): 66–75.
Google Scholar | Crossref44. Debell, F, Fear, NT, Head, M, et al. A systematic review of the comorbidity between PTSD and alcohol misuse. Soc Psychiat Psych Epidemiol 2014; 49(9): 1401–1425.
Google Scholar | Crossref | Medline45. Wilkinson, ST, Stefanovics, E, Rosenheck, RA. Marijuana use is associated with worse outcomes in symptom severity and violent behavior in patients with posttraumatic stress disorder. J Clinical Psychiatry 2015; 76(9): 1174–1180.
Google Scholar | Crossref | Medline46. Warner, CH, Warner, CM, Appenzeller, GN, et al. Identifying and managing posttraumatic stress disorder. Am Family Physician 2013; 88(12): 827–834.
Google Scholar | Medline47. Thai-Nghe, N, Gantner, Z, Schmidt-Thieme, L. Cost-sensitive learning methods for imbalanced data. In: The 2010 International joint conference on neural networks (IJCNN), Barcelona, Spain, . Piscataway, NJ: IEEE; 2010, pp. 1–8.
Google Scholar | Crossref48. Zhong, Q-Y, Karlson, EW, Gelaye, B, et al. Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing. BMC Medical Informatics Decision Making 2018; 18(1): 30.
Google Scholar | Crossref | Medline49. Jay, B . Many heads are better than one: making the case for ensemble learning. Zest AI. https://www.zest.ai/insights/many-heads-are-better-than-one-making-the-case-for-ensemble-learning (accessed on 15 June 2021.
Google Scholar