Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-17T06:15:43.213Z Has data issue: false hasContentIssue false
  • English
  • Français

The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders

Published online by Cambridge University Press:  09 August 2021

Lasse Hansen Open the ORCID record for Lasse Hansen [Opens in a new window] ,
Kenneth C. Enevoldsen Open the ORCID record for Kenneth C. Enevoldsen [Opens in a new window] ,
Martin Bernstorff Open the ORCID record for Martin Bernstorff [Opens in a new window] ,
Kristoffer L. Nielbo Open the ORCID record for Kristoffer L. Nielbo [Opens in a new window] ,
Andreas A. Danielsen Open the ORCID record for Andreas A. Danielsen [Opens in a new window]  and
Søren D. Østergaard Open the ORCID record for Søren D. Østergaard [Opens in a new window]
Lasse Hansen*
Affiliation:
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Center for Humanities Computing Aarhus, Aarhus University, Aarhus, Denmark
Kenneth C. Enevoldsen
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Center for Humanities Computing Aarhus, Aarhus University, Aarhus, Denmark
Martin Bernstorff
Affiliation:
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark
Kristoffer L. Nielbo
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Center for Humanities Computing Aarhus, Aarhus University, Aarhus, Denmark
Andreas A. Danielsen
Affiliation:
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark
Søren D. Østergaard
Affiliation:
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark
*
Author for correspondence: Lasse Hansen, Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background:

The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years.

Methods:

PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation.

Discussions:

We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.

Keywords

mental disordersprecision medicinemachine learningdeep learningnatural language processing
Type
Protocol
Information
Acta Neuropsychiatrica , Volume 33 , Issue 6 , December 2021 , pp. 323 - 330
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abadi, M, Barham, P, Chen, J, Chen, Z, Davis, A, Dean, J, Devin, M, Ghemawat, S, Irving, G and Isard, M (2016) Tensorflow: a system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). pp. 265–283.Google Scholar
Adamson, AS and Smith, A (2018) Machine learning and health care disparities in dermatology. JAMA Dermatology 154, 1247–1248. doi: 10.1001/jamadermatol.2018.2348 CrossRefGoogle ScholarPubMed
Beeler, PE, Bates, DW and Hug, BL (2014) Clinical decision support systems. Swiss Medical Weekly 144, w14073.Google ScholarPubMed
Boag, W, Kovaleva, O, McCoy, TH, Rumshisky, A, Szolovits, P and Perlis, RH (2021) Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes. Transl. Psychiatry 11, 1–6.CrossRefGoogle ScholarPubMed
Brown, TB, Mann, B, Ryder, N, Subbiah, M, Kaplan, J, Dhariwal, P, Neelakantan, A, Shyam, P, Sastry, G, Askell, A, Agarwal, S, Herbert-Voss, A, Krueger, G, Henighan, T, Child, R, Ramesh, A, Ziegler, DM, Wu, J, Winter, C, Hesse, C, Chen, M, Sigler, E, Litwin, M, Gray, S, Chess, B, Clark, J, Berner, C, McCandlish, S, Radford, A, Sutskever, I and Amodei, D (2020) Language models are few-shot learners. ArXiv:200514165 Cs.Google Scholar
Bybjerg-Grauholm, J, Pedersen, CB, Bækvad-Hansen, M, Pedersen, MG, Adamsen, D, Hansen, CS, Agerbo, E, Grove, J, Als, TD, Schork, AJ, Buil, A, Mors, O, Nordentoft, M, Werge, T, Børglum, AD, Hougaard, DM and Mortensen, PB (2020) The iPSYCH2015 Case-Cohort sample: updated directions for unravelling genetic and environmental architectures of severe mental disorders. medRxiv. doi: 10.1101/2020.11.30.20237768 CrossRefGoogle Scholar
Chekroud, AM and Koutsouleris, N (2018) The perilous path from publication to practice. Molecular Psychiatry 23, 24–25. doi: 10.1038/mp.2017.227 CrossRefGoogle Scholar
Chen, IY, Joshi, S and Ghassemi, M (2020) Treating health disparities with artificial intelligence. Nat. Med. 26, 16–17. doi: 10.1038/s41591-019-0649-2 CrossRefGoogle ScholarPubMed
Chen, T and Guestrin, C (2016) Xgboost: a scalable tree boosting system. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. pp. 785–794.CrossRefGoogle Scholar
Ching, T, Himmelstein, DS, Beaulieu-Jones, BK, Kalinin, AA, Do, BT, Way, GP, Ferrero, E, Agapow, P-M, Zietz, M, Hoffman, MM, Xie, W, Rosen, GL, Lengerich, BJ, Israeli, J, Lanchantin, J, Woloszynek, S, Carpenter, AE, Shrikumar, A, Xu, J, Cofer, EM, Lavender, CA, Turaga, SC, Alexandari, AM, Lu, Z, Harris, DJ, DeCaprio, D, Qi, Y, Kundaje, A, Peng, Y, Wiley, LK, Segler, MHS, Boca, SM, Swamidass, SJ, Huang, A, Gitter, A and Greene, CS (2018) Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface 15. doi: 10.1098/rsif.2017.0387 CrossRefGoogle Scholar
Collins, FS and Varmus, H (2015) A new initiative on precision medicine. The New England Journal of Medicine 372, 793–795. doi: 10.1056/NEJMp1500523 CrossRefGoogle ScholarPubMed
Conneau, A, Khandelwal, K, Goyal, N, Chaudhary, V, Wenzek, G, Guzmán, F, Grave, E, Ott, M, Zettlemoyer, L and Stoyanov, V (2020) Unsupervised cross-lingual representation learning at scale. ArXiv:191102116 Cs.CrossRefGoogle Scholar
Dalsgaard, S, Thorsteinsson, E, Trabjerg, BB, Schullehner, J, Plana-Ripoll, O, Brikell, I, Wimberley, T, Thygesen, M, Madsen, KB, Timmerman, A, Schendel, D, McGrath, JJ, Mortensen, PB and Pedersen, CB (2020) Incidence rates and cumulative incidences of the full spectrum of diagnosed mental disorders in childhood and adolescence. JAMA Psychiatry 77, 155–164. doi: 10.1001/jamapsychiatry.2019.3523 CrossRefGoogle ScholarPubMed
Danielsen, AA, Fenger, MHJ, Østergaard, SD, Nielbo, KL and Mors, O (2019) Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data. Acta Psychiatrica Scandinavica 140, 147–157. doi: 10.1111/acps.13061 CrossRefGoogle ScholarPubMed
Desalegn, D, Girma, S and Abdeta, T (2020) Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: a hospital-based cross-sectional study. PLOS ONE 15, e0229514. doi: 10.1371/journal.pone.0229514 CrossRefGoogle ScholarPubMed
Devlin, J, Chang, M-W, Lee, K and Toutanova, K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. ArXiv181004805 Cs.Google Scholar
Elkis, H and Buckley, PF (2016) Treatment-resistant schizophrenia. Psychiatric Clinics of North America 39, 239–265. doi: 10.1016/j.psc.2016.01.006 CrossRefGoogle ScholarPubMed
Esteva, A, Kuprel, B, Novoa, RA, Ko, J, Swetter, SM, Blau, HM and Thrun, S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118.CrossRefGoogle ScholarPubMed
Goldberg, JF and Ernst, CL (2016) Core concepts involving adverse psychotropic drug effects: assessment, implications, and management. Psychiatric Clinics of North America 39, 375–389. doi: 10.1016/j.psc.2016.04.001 CrossRefGoogle ScholarPubMed
Goldstein, BA (2020) Five analytic challenges in working with electronic health records data to support clinical trials with some solutions. Clin. Trials 17, 370–376. doi: 10.1177/1740774520931211 CrossRefGoogle ScholarPubMed
Goldstein, BA, Navar, AM, Pencina, MJ and Ioannidis, JPA (2017) Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. Journal of the American Medical Informatics Association 24, 198–208. doi: 10.1093/jamia/ocw042 CrossRefGoogle ScholarPubMed
Gulshan, V, Peng, L, Coram, M, Stumpe, MC, Wu, D, Narayanaswamy, A, Venugopalan, S, Widner, K, Madams, T, Cuadros, J, Kim, R, Raman, R, Nelson, PC, Mega, JL and Webster, DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402. doi: 10.1001/jama.2016.17216 CrossRefGoogle ScholarPubMed
Hamilton, M (1960) A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry 23, 56–62. doi: 10.1136/jnnp.23.1.56 CrossRefGoogle ScholarPubMed
Katon, WJ (2008) The comorbidity of diabetes mellitus and depression. American Journal of Medicine, A Primary Care Approach: Managing Depression in the Face of Chronic Medical Conditions 121, S8–S15. doi: 10.1016/j.amjmed.2008.09.008 Google ScholarPubMed
Lauritsen, SM, Thiesson, B, Jørgensen, MJ, Riis, AH, Espelund, US, Weile, JB and Lange, J (2021) The consequences of the framing of machine learning risk prediction models: evaluation of Sepsis in general wards. ArXiv210110790 Cs Stat. Google Scholar
Laursen, TM, Musliner, KL, Benros, ME, Vestergaard, M and Munk-Olsen, T (2016) Mortality and life expectancy in persons with severe unipolar depression. Journal of Affective Disorders 193, 203–207. doi: 10.1016/j.jad.2015.12.067 CrossRefGoogle ScholarPubMed
Lawrence, D, Hancock, KJ and Kisely, S (2013) The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ 346, f2539. doi: 10.1136/bmj.f2539 CrossRefGoogle ScholarPubMed
Leadholm, AKK, Rothschild, AJ, Nielsen, J, Bech, P and Ostergaard, SD (2014) Risk factors for suicide among 34,671 patients with psychotic and non-psychotic severe depression. Journal of Affective Disorders 156, 119–125. doi: 10.1016/j.jad.2013.12.003 CrossRefGoogle ScholarPubMed
LeCun, Y (2019) 1.1 deep learning hardware: Past, present, and future. In 2019 IEEE International Solid-State Circuits Conference-(ISSCC). IEEE, pp. 12–19.CrossRefGoogle Scholar
LeCun, Y, Bengio, Y and Hinton, G (2015) Deep learning. Nature 521, 436–444. doi: 10.1038/nature14539 CrossRefGoogle ScholarPubMed
Li, Y, Rao, S, Solares, JRA, Hassaine, A, Ramakrishnan, R, Canoy, D, Zhu, Y, Rahimi, K and Salimi-Khorshidi, G (2020) BEHRT: transformer for Electronic Health Records. Scientific Reports 10, 7155. doi: 10.1038/s41598-020-62922-y CrossRefGoogle ScholarPubMed
Manchia, M, Pisanu, C, Squassina, A and Carpiniello, B (2020) Challenges and future prospects of precision medicine in psychiatry. Pharmacogenomics and Personalized Medicine 13, 127–140. doi: 10.2147/PGPM.S198225 CrossRefGoogle Scholar
Mandelli, L and Serretti, A (2013) Gene environment interaction studies in depression and suicidal behavior: an update. Neuroscience & Biobehavioral Reviews, Discovery Research in Neuropsychiatry – Anxiety, Depression and Schizophrenia in Focus 37, 2375–2397. doi: 10.1016/j.neubiorev.2013.07.011 Google ScholarPubMed
Messer, T, Lammers, G, Müller-Siecheneder, F, Schmidt, R-F and Latifi, S (2017) Substance abuse in patients with bipolar disorder: a systematic review and meta-analysis. Psychiatry Research 253, 338–350. doi: 10.1016/j.psychres.2017.02.067 CrossRefGoogle ScholarPubMed
Miotto, R, Li, L, Kidd, BA and Dudley, JT (2016) Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific reports 6, 26094. doi: 10.1038/srep26094 CrossRefGoogle ScholarPubMed
Momen, NC, Plana-Ripoll, O, Agerbo, E, Benros, ME, Børglum, AD, Christensen, MK, Dalsgaard, S, Degenhardt, L, de Jonge, P, Debost, J-CPG, Fenger-Grøn, M, Gunn, JM, Iburg, KM, Kessing, LV, Kessler, RC, Laursen, TM, Lim, CCW, Mors, O, Mortensen, PB, Musliner, KL, Nordentoft, M, Pedersen, CB, Petersen, LV, Ribe, AR, Roest, AM, Saha, S, Schork, AJ, Scott, KM, Sievert, C, Sørensen, HJ, Stedman, TJ, Vestergaard, M, Vilhjalmsson, B, Werge, T, Weye, N, Whiteford, HA, Prior, A and McGrath, JJ (2020) Association between mental disorders and subsequent medical conditions. New England Journal of Medicine 382, 1721–1731. doi: 10.1056/NEJMoa1915784 CrossRefGoogle ScholarPubMed
Musliner, KL, Krebs, MD, Albiñana, C, Vilhjalmsson, B, Agerbo, E, Zandi, PP, Hougaard, DM, Nordentoft, M, Børglum, AD, Werge, T, Mortensen, PB and Østergaard, SD (2020) Polygenic risk and progression to bipolar or psychotic disorders among individuals diagnosed with unipolar depression in early life. American Journal of Psychiatry 177, 936–943. doi: 10.1176/appi.ajp.2020.19111195 CrossRefGoogle ScholarPubMed
Musliner, KL, Munk-Olsen, T, Mors, O and Østergaard, SD (2017) Progression from unipolar depression to schizophrenia. Acta Psychiatrica Scandinavica 135, 42–50. doi: 10.1111/acps.12663 CrossRefGoogle Scholar
Musliner, KL and Østergaard, SD (2018) Patterns and predictors of conversion to bipolar disorder in 91 587 individuals diagnosed with unipolar depression. Acta Psychiatrica Scandinavica 137, 422–432. doi: 10.1111/acps.12869 CrossRefGoogle ScholarPubMed
Offord, DR (2000) Selection of levels of prevention. Addictive Behaviors 25, 833–842. doi: 10.1016/s0306-4603(00)00132-5 CrossRefGoogle ScholarPubMed
Organization W.H. (1992) The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. World Health Organization.Google Scholar
Paszke, A, Gross, S, Massa, F, Lerer, A, Bradbury, J, Chanan, G, Killeen, T, Lin, Z, Gimelshein, N and Antiga, L (2019) Pytorch: an imperative style, high-performance deep learning library. ArXiv Prepr. ArXiv191201703. Google Scholar
Patton, GC, Coffey, C, Romaniuk, H, Mackinnon, A, Carlin, JB, Degenhardt, L, Olsson, CA and Moran, P (2014) The prognosis of common mental disorders in adolescents: a 14-year prospective cohort study. The Lancet 383, 1404–1411. doi: 10.1016/S0140-6736(13)62116-9 CrossRefGoogle ScholarPubMed
Pedersen, CB (2011) The Danish civil registration system. Scandinavian journal of public health 39, 22–25. doi: 10.1177/1403494810387965 CrossRefGoogle ScholarPubMed
Pedersen, CB, Bybjerg-Grauholm, J, Pedersen, MG, Grove, J, Agerbo, E, Bækvad-Hansen, M, Poulsen, JB, Hansen, CS, McGrath, JJ, Als, TD, Goldstein, JI, Neale, BM, Daly, MJ, Hougaard, DM, Mors, O, Nordentoft, M, Børglum, AD, Werge, T and Mortensen, PB (2018) The iPSYCH2012 case-cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Molecular Psychiatry 23, 6–14. doi: 10.1038/mp.2017.196 CrossRefGoogle ScholarPubMed
Plana-Ripoll, O, Pedersen, CB, Agerbo, E, Holtz, Y, Erlangsen, A, Canudas-Romo, V, Andersen, PK, Charlson, FJ, Christensen, MK, Erskine, HE, Ferrari, AJ, Iburg, KM, Momen, N, Mortensen, PB, Nordentoft, M, Santomauro, DF, Scott, JG, Whiteford, HA, Weye, N, McGrath, JJ and Laursen, TM (2019a) A comprehensive analysis of mortality-related health metrics associated with mental disorders: a nationwide, register-based cohort study. The Lancet 394, 1827–1835. doi: 10.1016/S0140-6736(19)32316-5 CrossRefGoogle ScholarPubMed
Plana-Ripoll, O, Pedersen, CB, Holtz, Y, Benros, ME, Dalsgaard, S, de Jonge, P, Fan, CC, Degenhardt, L, Ganna, A, Greve, AN, Gunn, J, Iburg, KM, Kessing, LV, Lee, BK, Lim, CCW, Mors, O, Nordentoft, M, Prior, A, Roest, AM, Saha, S, Schork, A, Scott, JG, Scott, KM, Stedman, T, Sørensen, HJ, Werge, T, Whiteford, HA, Laursen, TM, Agerbo, E, Kessler, RC, Mortensen, PB and McGrath, JJ (2019b) Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 76, 259. doi: 10.1001/jamapsychiatry.2018.3658 CrossRefGoogle ScholarPubMed
Prochaska, JJ, Gill, P, Hall, SE and Hall, SM (2005) Identification and treatment of substance misuse on an inpatient psychiatry unit. Psychiatric Services 56, 347–349. doi: 10.1176/appi.ps.56.3.347 CrossRefGoogle Scholar
Qin, P, Nordentoft, M (2005) Suicide risk in relation to psychiatric hospitalization: evidence based on longitudinal registers. Archives of General Psychiatry 62, 6.CrossRefGoogle ScholarPubMed
Quinn, PO and Madhoo, M (2014) A review of attention-deficit/hyperactivity disorder in women and girls: uncovering this hidden diagnosis. Primary Care Companion for CNS Disorders 16.CrossRefGoogle Scholar
Raffel, C, Shazeer, N, Roberts, A, Lee, K, Narang, S, Matena, M, Zhou, Y, Li, W and Liu, PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. ArXiv191010683 Cs Stat.Google Scholar
Rains, LS, Zenina, T, Dias, MC, Jones, R, Jeffreys, S, Branthonne-Foster, S, Lloyd-Evans, B and Johnson, S (2019) Variations in patterns of involuntary hospitalisation and in legal frameworks: an international comparative study. Lancet Psychiatry 6, 403–417. doi: 10.1016/S2215-0366(19)30090-2 CrossRefGoogle Scholar
Rajkomar, A, Hardt, M, Howell, MD, Corrado, G and Chin, MH (2018a) Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine 169, 866–872. doi: 10.7326/M18-1990 CrossRefGoogle ScholarPubMed
Rajkomar, A, Oren, E, Chen, K, Dai, AM, Hajaj, N, Hardt, M, Liu, PJ, Liu, X, Marcus, J, Sun, M, Sundberg, P, Yee, H, Zhang, K, Zhang, Y, Flores, G, Duggan, GE, Irvine, J, Le, Q, Litsch, K, Mossin, A, Tansuwan, J, Wang, D, Wexler, J, Wilson, J, Ludwig, D, Volchenboum, SL, Chou, K, Pearson, M, Madabushi, S, Shah, NH, Butte, AJ, Howell, MD, Cui, C, Corrado, GS and Dean, J (2018b) Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine 1, 1–10. doi: 10.1038/s41746-018-0029-1 CrossRefGoogle ScholarPubMed
Riis, AH, Kristensen, PK, Petersen, MG, Ebdrup, NH, Lauritsen, SM and Jørgensen, MJ (2020) Cohort profile: CROSS-TRACKS: a population-based open cohort across healthcare sectors in Denmark. BMJ Open 10, e039996. doi: 10.1136/bmjopen-2020-039996 CrossRefGoogle ScholarPubMed
Roberts, AL, Gilman, SE, Breslau, J, Breslau, N and Koenen, KC (2011) Race/ethnic differences in exposure to traumatic events, development of post-traumatic stress disorder, and treatment-seeking for post-traumatic stress disorder in the United States. Psychological Medicine 41, 71–83. doi: 10.1017/S0033291710000401 CrossRefGoogle ScholarPubMed
Rothschild, JM, Mann, K, Keohane, CA, Williams, DH, Foskett, C, Rosen, SL, Flaherty, L, Chu, JA and Bates, DW (2007) Medication safety in a psychiatric hospital. General Hospital Psychiatry 29, 156–162. doi: 10.1016/j.genhosppsych.2006.12.002 CrossRefGoogle ScholarPubMed
Rudin, C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, 206–215. doi: 10.1038/s42256-019-0048-x CrossRefGoogle Scholar
Rumshisky, A, Ghassemi, M, Naumann, T, Szolovits, P, Castro, VM, McCoy, TH and Perlis, RH (2016) Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Translational Psychiatry 6, e921–e921. doi: 10.1038/tp.2015.182 CrossRefGoogle ScholarPubMed
Rush, AJ, Trivedi, MH, Wisniewski, SR, Nierenberg, AA, Stewart, JW, Warden, D, Niederehe, G, Thase, ME, Lavori, PW, Lebowitz, BD, McGrath, PJ, Rosenbaum, JF, Sackeim, HA (2006) Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. American Journal of Psychiatry 13.CrossRefGoogle Scholar
Salagre, E, Rohde, C, Ishtiak-Ahmed, K, Gasse, C and Østergaard, SD (2020) Survival rate following involuntary electroconvulsive therapy: a population-based study. Journal of Electroconvulsive Therapy. Publish Ahead of Print. doi: 10.1097/YCT.0000000000000736 CrossRefGoogle Scholar
Salazar de Pablo, G, Studerus, E, Vaquerizo-Serrano, J, Irving, J, Catalan, A, Oliver, D, Baldwin, H, Danese, A, Fazel, S, Steyerberg, EW, Stahl, D and Fusar-Poli, P (2020) Implementing precision psychiatry: a systematic review of individualized prediction models for clinical practice. Schizophrenia Bulletin. doi: 10.1093/schbul/sbaa120 CrossRefGoogle Scholar
Saxena, S (2018) Excess mortality among people with mental disorders: a public health priority. Lancet Public Health 3, e264–e265. doi: 10.1016/S2468-2667(18)30099-9 CrossRefGoogle ScholarPubMed
Schoon, EW, Melamed, D, Breiger, RL, Yoon, E and Kleps, C (2019) Precluding rare outcomes by predicting their absence. PLOS ONE 14, e0223239. doi: 10.1371/journal.pone.0223239 CrossRefGoogle ScholarPubMed
Silver, D, Huang, A, Maddison, CJ, Guez, A, Sifre, L, van den Driessche, G, Schrittwieser, J, Antonoglou, I, Panneershelvam, V, Lanctot, M, Dieleman, S, Grewe, D, Nham, J, Kalchbrenner, N, Sutskever, I, Lillicrap, T, Leach, M, Kavukcuoglu, K, Graepel, T and Hassabis, D (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489. doi: 10.1038/nature16961 CrossRefGoogle ScholarPubMed
Starke, G, Clercq, ED, Borgwardt, S and Elger, BS (2020) Computing schizophrenia: ethical challenges for machine learning in psychiatry. Psychological Medicine 1–7. doi: 10.1017/S0033291720001683 CrossRefGoogle Scholar
Straszek, SP and Licht, RW (2019) The development of the Bech-Rafaelsen Mania Scale (MAS) and the clinical applications of a 10-item modified subscale - the MAS-M. Psychotherapy and Psychosomatics 88, 122–123. doi: 10.1159/000502467 Google Scholar
Subbaswamy, A, Adams, R and Saria, S (2021) Evaluating model robustness and stability to dataset shift. ArXiv201015100 Cs Stat.Google Scholar
Tanskanen, A, Tiihonen, J and Taipale, H (2018) Mortality in schizophrenia: 30-year nationwide follow-up study. Acta Psychiatrica Scandinavica 138, 492–499. doi: 10.1111/acps.12913 CrossRefGoogle ScholarPubMed
Thornicroft, G (2013) Premature death among people with mental illness. BMJ 346, f2969. doi: 10.1136/bmj.f2969 CrossRefGoogle Scholar
Tiihonen, J, Lönnqvist, J, Wahlbeck, K, Klaukka, T, Niskanen, L, Tanskanen, A and Haukka, J (2009) 11-year follow-up of mortality in patients with schizophrenia: a population-based cohort study (FIN11 study). Lancet (London, England) 374, 620–627. doi: 10.1016/S0140-6736(09)60742-X CrossRefGoogle Scholar
Tomlin, J, Lega, I, Braun, P, Kennedy, HG, Herrando, VT, Barroso, R, Castelletti, L, Mirabella, F, Scarpa, F, Völlm, B, Pham, T, Müller-Isberner, R, Taube, M, Rivellini, G, Calevro, V, Liardo, R, Pennino, M, Markiewicz, I, Barbosa, F, Bulten, E, Thomson, L, Pustoslemšek, M, Arroyo, JM, Seppänen, A, Thibaut, F, Kozaric-Kovacic, D, Palijan, TZ, Markovska-Simoska, S, Raleva, M, Šileikaitė, A, Germanavicius, A, Čėsnienė, I, The Experts of Cost Action IS1302 (2021) Forensic mental health in Europe: some key figures. Social Psychiatry and Psychiatric Epidemiology 56, 109–117. doi: 10.1007/s00127-020-01909-6 CrossRefGoogle ScholarPubMed
Van, RG and Drake, F (2009) Python 3 Reference Manual. Scotts Vallry, CA: CreateSpace.Google Scholar
Vaswani, A, Shazeer, N, Parmar, N, Uszkoreit, J, Jones, L, Gomez, AN, Kaiser, L and Polosukhin, I (2017) Attention is all you need. ArXiv170603762 Cs. Google Scholar
Wang, F (2021) Machine learning for predicting rare clinical outcomes—finding needles in a haystack. JAMA Network Open 4, e2110738. doi: 10.1001/jamanetworkopen.2021.10738 CrossRefGoogle ScholarPubMed
World Health Organization (1976) Anatomical Therapeutic Chemical Classification System [WWW Document]. URL https://www.whocc.no/atc_ddd_index/ Google Scholar