How data science can advance mental health research


Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: What can data science do for mental health research?
Fig. 2: Data science applications in understanding mental health and mental illness.
Fig. 3: The cycle of data science applications in the context of mental health treatments.


  1. 1.

    Walesby, K. E., Harrison, J. K. & Russ, T. C. What big data could achieve in Scotland. J. R. Coll. Physicians Edinb. 47, 114–119 (2017).

    CAS  PubMed  Google Scholar 

  2. 2.

    Soni, J., Ansari, U., Sharma, D. & Soni, S. Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17, 43–48 (2011).

    Google Scholar 

  3. 3.

    Hamada, T., Keum, N., Nishihara, R. & Ogino, S. Molecular pathological epidemiology: new developing frontiers of big data science to study etiologies and pathogenesis. J. Gastroenterol. 52, 265–275 (2017).

    PubMed  Google Scholar 

  4. 4.

    Hafferty, J. D., Smith, D. J. & McIntosh, A. M. Invited commentary on Stewart and Davis “‘Big data’in mental health research — current status and emerging possibilities”. Soc. Psychiatry Psychiatr. Epidemiol. 52, 127–129 (2017).

    PubMed  Google Scholar 

  5. 5.

    Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Lencz, T. et al. Molecular genetic evidence for overlap between general cognitive ability and risk for schizophrenia: a report from the Cognitive Genomics consorTium (COGENT). Mol. Psychiatry 19, 168–174 (2014).

    CAS  PubMed  Google Scholar 

  8. 8.

    Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    PubMed  Google Scholar 

  9. 9.

    Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Herper, M. Illumina promises to sequence human genome for $100 — but not quite yet. Forbes (2017).

  11. 11.

    Schatz, M. C. Biological data sciences in genome research. Genome Res. 25, 1417–1422 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Cheng, J., Tegge, A. N. & Baldi, P. Machine learning methods for protein structure prediction. IEEE Rev. Biomed. Eng. 1, 41–49 (2008).

    PubMed  Google Scholar 

  13. 13.

    Montes, J., Gomez, E., Merchán-Pérez, A., DeFelipe, J. & Peña, J.-M. A machine learning method for the prediction of receptor activation in the simulation of synapses. PLoS ONE 8, e68888 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Ou-Yang, S.-s et al. Computational drug discovery. Acta Pharmacol. Sin. 33, 1131–1140 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Immadisetty, K., Geffert, L. M., Surratt, C. K. & Madura, J. D. New design strategies for antidepressant drugs. Expert Opin. Drug Discov. 8, 1399–1414 (2013).

    CAS  PubMed  Google Scholar 

  16. 16.

    Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Thompson, P. M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Shi, Y. & Toga, A. Connectome imaging for mapping human brain pathways. Mol. Psychiatry 22, 1230–1240 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Gu, S. et al. Emergence of system roles in normative neurodevelopment. Proc. Natl Acad. Sci. USA 112, 13681–13686 (2015).

    CAS  PubMed  Google Scholar 

  20. 20.

    Christakou, A. et al. Disorder-specific functional abnormalities during sustained attention in youth with attention deficit hyperactivity disorder (ADHD) and with autism. Mol. Psychiatry 18, 236–244 (2013).

    CAS  PubMed  Google Scholar 

  21. 21.

    Chang, X. et al. Altered default mode and fronto-parietal network subsystems in patients with schizophrenia and their unaffected siblings. Brain Res. 1562, 87–99 (2014).

    CAS  PubMed  Google Scholar 

  22. 22.

    Schwindt, G. C. et al. Modulation of the default-mode network between rest and task in Alzheimer’s disease. Cereb. Cortex 23, 1685–1694 (2013).

    PubMed  Google Scholar 

  23. 23.

    Broyd, S. J. et al. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci. Biobehav. Rev. 33, 279–296 (2009).

    PubMed  Google Scholar 

  24. 24.

    Xia, M. & He, Y. Functional connectomics from a “big data” perspective. NeuroImage 160, 152–167 (2017).

    PubMed  Google Scholar 

  25. 25.

    Van Horn, J. D. & Toga, A. W. Human neuroimaging as a “big data” science. Brain Imaging Behav. 8, 323–331 (2014).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Bidargaddi, N. et al. Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Mol. Psychiatry 22, 164–169 (2017).

    CAS  PubMed  Google Scholar 

  27. 27.

    Khan, Y., Ostfeld, A. E., Lochner, C. M., Pierre, A. & Arias, A. C. Monitoring of vital signs with flexible and wearable medical devices. Adv. Mater. 28, 4373–4395 (2016).

    CAS  PubMed  Google Scholar 

  28. 28.

    Selvam, A. P., Muthukumar, S., Kamakoti, V. & Prasad, S. A wearable biochemical sensor for monitoring alcohol consumption lifestyle through ethyl glucuronide (EtG) detection in human sweat. Sci. Rep. 6, 23111 (2016).

    PubMed  Google Scholar 

  29. 29.

    Bradley, A. J. et al. Sleep and circadian rhythm disturbance in bipolar disorder. Psychol. Med. 47, 1678–1689 (2017).

    CAS  PubMed  Google Scholar 

  30. 30.

    Knight, A. & Bidargaddi, N. Commonly available activity tracker apps and wearables as a mental health outcome indicator: a prospective observational cohort study among young adults with psychological distress. J. Affect. Disord. 236, 31–36 (2018).

    PubMed  Google Scholar 

  31. 31.

    Zafarani, R., Abbasi, M. A. & Liu, H. Social Media Mining: An Introduction (Cambridge Univ. Press, 2014).

  32. 32.

    Jackson, R. G. et al. Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project. BMJ Open 7, e012012 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ford, D. V. et al. The SAIL Databank: building a national architecture for e-health research and evaluation. BMC Health Serv. Res. 9, 157 (2009).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    McIntosh, A. M. et al. Data science for mental health: a UK perspective on a global challenge. Lancet Psychiatry 3, 993–998 (2016).

    PubMed  Google Scholar 

  35. 35.

    Engel, G. The need for a new medical model: a challenge for biomedicine. Science 196, 129–136 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Astell-Burt, T., Mitchell, R. & Hartig, T. The association between green space and mental health varies across the lifecourse. A longitudinal study. J. Epidemiol. Community Health 68, 578–583 (2014).

    PubMed  Google Scholar 

  37. 37.

    Gascon, M. et al. Mental health benefits of long-term exposure to residential green and blue spaces: a systematic review. Int. J. Environ. Res. Public Health 12, 4354–4379 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Ruijsbroek, A. et al. Neighbourhood green space, social environment and mental health: an examination in four European cities. Int. J. Public Health 62, 1–11 (2017).

    Google Scholar 

  39. 39.

    Chen, J. C. et al. Ambient air pollution and neurotoxicity on brain structure: evidence from women’s health initiative memory study. Ann. Neurol. 78, 466–476 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Miller, H. J. & Tolle, K. Big data for healthy cities: using location-aware technologies, open data and 3D urban models to design healthier built environments. Built Environ. 42, 441–456 (2016).

    Google Scholar 

  41. 41.

    Inoue, T. et al. Does temperature or sunshine mediate the effect of latitude on affective temperaments? A study of 5 regions in Japan. J. Affect. Disord. 172, 141–145 (2015).

    PubMed  Google Scholar 

  42. 42.

    Roffman, J. L. et al. Randomized multicenter investigation of folate plus vitamin B12 supplementation in schizophrenia. JAMA Psychiatry 70, 481–489 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Milaneschi, Y. et al. The association between low vitamin D and depressive disorders. Mol. Psychiatry 19, 444–451 (2014).

    CAS  PubMed  Google Scholar 

  44. 44.

    Mokry, L. E. et al. Genetically decreased vitamin D and risk of Alzheimer disease. Neurology 87, 2567–2574 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Chen, H. et al. Living near major roads and the incidence of dementia, Parkinson’s disease, and multiple sclerosis: a population-based cohort study. Lancet 389, 718–726 (2017).

    PubMed  Google Scholar 

  46. 46.

    White, J. et al. Improving mental health through the regeneration of deprived neighborhoods: a natural experiment. Am. J. Epidemiol. 186, 473–480 (2017).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Pettit, S. et al. Variation in referral and access to new psychological therapy services by age: an empirical quantitative study. Br. J. Gen. Pract. 67, e453–e459 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Asthana, S. et al. Equity of utilisation of cardiovascular care and mental health services in England: a cohort-based cross-sectional study using small-area estimation. Health Serv. Deliv. Res. 14, 4 (2016).

    Google Scholar 

  49. 49.

    Wu, C.-Y. et al. Evaluation of smoking status identification using electronic health records and open-text information in a large mental health case register. PLoS ONE 8, e74262 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Vuorilehto, M. S., Melartin, T. K., Rytsala, H. J. & Isometsa, E. T. Do characteristics of patients with major depressive disorder differ between primary and psychiatric care? Psychol. Med. 37, 893–904 (2007).

    PubMed  Google Scholar 

  51. 51.

    Demyttenaere, K. et al. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization world mental health surveys. JAMA 291, 2581–2590 (2004).

    PubMed  Google Scholar 

  52. 52.

    John, A. et al. Case-finding for common mental disorders of anxiety and depression in primary care: an external validation of routinely collected data. BMC Med. Inform. Decis. Mak. 16, 35 (2016).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Davis, K. A. S. et al. Mental health in UK Biobank — implementation and results of an online questionnaire in 157,366 participants. Br. J. Psychiatry Open 4, 83–90 (2018).

    Google Scholar 

  54. 54.

    Simon, G. E. et al. First presentation with psychotic symptoms in a population-based sample. Psychiatr. Serv. 68, 456–461 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    John, A. et al. Recent trends in primary-care antidepressant prescribing to children and young people: an e-cohort study. Psychol. Med. 46, 3315–3327 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Aragona, M. in Alternative Perspectives on Psychiatric Validation: DSM, ICD, RDoC, and Beyond (eds Zachar, P., St. Stoyanov, D., Aragona, M. & Jablensky, A.) 27–46 (Oxford Univ. Press, Oxford, 2014).

  57. 57.

    Reed, G. M. et al. The ICD-11 developmental field study of reliability of diagnoses of high-burden mental disorders: results among adult patients in mental health settings of 13 countries. World Psychiatry 17, 174–186 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Blumenthal-Barby, J. Psychiatry’s new manual (DSM-5): ethical and conceptual dimensions. J. Med. Ethics 40, 531–536 (2013).

    PubMed  Google Scholar 

  59. 59.

    Ghaemi, S. N. Nosologomania: DSM & Karl Jaspers’ critique of Kraepelin. Philos. Ethics Humanit. Med. 4, 10 (2009).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Davis, K. A., Sudlow, C. L. & Hotopf, M. Can mental health diagnoses in administrative data be used for research? A systematic review of the accuracy of routinely collected diagnoses. BMC Psychiatry 16, 263 (2016).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    National Information Board and Department of Health Personalised Health and Care 2020: Using Data and Technology to Transform Outcomes for Patients and Citizens (GOV.UK and NHS, HM Government, 2014).

  62. 62.

    Spiranovic, C., Matthews, A., Scanlan, J. & Kirkby, K. C. Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges. Adv. Ment. Health 14, 14–25 (2016).

    Google Scholar 

  63. 63.

    Frances, A. J. & Widiger, T. Psychiatric diagnosis: lessons from the DSM-IV past and cautions for the DSM-5 future. Annu. Rev. Clin. Psychol. 8, 109–130 (2012).

    PubMed  Google Scholar 

  64. 64.

    Hickie, I. B. et al. Clinical classification in mental health at the cross-roads: which direction next? BMC Med. 11, 125 (2013).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Lin, Y., Huang, S., Simon, G. E. & Liu, S. Analysis of depression trajectory patterns using collaborative learning. Math. Biosci. 282, 191–203 (2016).

    PubMed  Google Scholar 

  66. 66.

    Lin, Y., Huang, S., Simon, G. E. & Liu, S. Data-based decision rules to personalize depression follow-up. Sci. Rep. 8, 5064 (2018).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Insel, T. R. The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. Am. J. Psychiatry 171, 395–397 (2014).

    PubMed  Google Scholar 

  68. 68.

    Deisseroth, K. Circuit dynamics of adaptive and maladaptive behaviour. Nature 505, 309–317 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Psychometrics and Measurement Database (KCL Institute of Psychiatry Psychology and Neuroscience, 2017);

  70. 70.

    Carcone, D. & Ruocco, A. C. Six years of research on the National Institute of Mental Health’s Research domain criteria (RDoC) initiative: a systematic review. Front. Cell. Neurosci. 11, 46 (2017).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Karalunas, S. L. et al. Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. JAMA Psychiatry 71, 1015–1024 (2014).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Casey, J. A., Schwartz, B. S., Stewart, W. F. & Adler, N. E. Using electronic health records for population health research: a review of methods and applications. Annu. Rev. Public Health 37, 61–81 (2016).

    PubMed  Google Scholar 

  73. 73.

    Torous, J., Onnela, J. & Keshavan, M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Trans. Psychiatry 7, e1053 (2017).

    CAS  Google Scholar 

  74. 74.

    Huang, S. H. et al. Toward personalizing treatment for depression: predicting diagnosis and severity. J. Am. Med. Inform. Assoc. 21, 1069–1075 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Whiteford, H. A. et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382, 1575–1586 (2013).

    PubMed  Google Scholar 

  76. 76.

    Friedman, R. A. Uncovering an epidemic — screening for mental illness in teens. N. Engl. J. Med. 355, 2717–2719 (2006).

    CAS  PubMed  Google Scholar 

  77. 77.

    Hetrick, S. et al. Early identification and intervention in depressive disorders: towards a clinical staging model. Psychother. Psychosom. 77, 263–270 (2008).

    CAS  PubMed  Google Scholar 

  78. 78.

    Knapp, M., McDaid, D. & Parsonage, M. Mental Health Promotion and Mental Illness Prevention: The Economic Case (London School of Economics and Political Science, Centre for Mental Health, Centre for the Economics of Mental Health, Institute of Psychiatry, King’s College London, 2011).

  79. 79.

    Parker, G. Head to head: Is depression overdiagnosed? Yes. Br. Med. J. 335, 328 (2007).

    Google Scholar 

  80. 80.

    Najman, J. M. et al. Screening in early childhood for risk of later mental health problems: a longitudinal study. J. Psychiatr. Res. 42, 694–700 (2008).

    PubMed  Google Scholar 

  81. 81.

    Henderson, S. W., Horwitz, A. V. & Wakefield, J. C. Should screening for depression among children and adolescents be demedicalized? J. Am. Acad. Child Adolesc. Psychiatry 48, 683–687 (2009).

    Google Scholar 

  82. 82.

    McGorry, P. D. Staging in neuropsychiatry: a heuristic model for understanding, prevention and treatment. Neurotox. Res. 18, 244–255 (2010).

    PubMed  Google Scholar 

  83. 83.

    Schoevers, R. A. et al. Prevention of late-life depression in primary care: do we know where to begin? Am. J. Psychiatry 163, 1611–1621 (2006).

    PubMed  Google Scholar 

  84. 84.

    Nock, M. K. et al. Cross-national analysis of the associations among mental disorders and suicidal behavior: findings from the WHO World Mental Health Surveys. PLoS Med. 6, e1000123 (2009).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Poulin, C. et al. Predicting the risk of suicide by analyzing the text of clinical notes. PLoS ONE 9, e85733 (2014).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Hippisley-Cox, J. et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 336, 1475–1482 (2008).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Olfson, M., Marcus, S. C. & Bridge, J. A. Emergency department recognition of mental disorders and short-term outcome of deliberate self-harm. Am. J. Psychiatry 170, 1442–1450 (2013).

    PubMed  Google Scholar 

  88. 88.

    College of Emergency Medicine Mental Health in Emergency Departments: A Toolkit for Improving Care (The College of Emergency Medicine, 2013).

  89. 89.

    National Institute for Health and Clinical Excellence Antenatal and Postnatal Mental Health: Clinical Management and Service Guidance (update) CG192 (National Institute for Health and Clinical Excellence, 2014).

  90. 90.

    Kaye, J. et al. Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimers Dement. 10, 10–17 (2014).

    PubMed  Google Scholar 

  91. 91.

    Jashinsky, J. et al. Tracking suicide risk factors through Twitter in the US. Crisis 35, 51–59 (2014).

    PubMed  Google Scholar 

  92. 92.

    Inkster, B., Stillwell, D., Kosinski, M. & Jones, P. A decade into Facebook: where is psychiatry in the digital age? Lancet Psychiatry 3, 1087–1090 (2016).

    PubMed  Google Scholar 

  93. 93.

    Conway, M. & O’Connor, D. Social media, big data, and mental health: current advances and ethical implications. Curr. Opin. Psychol. 9, 77–82 (2016).

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Mikal, J., Hurst, S. & Conway, M. Ethical issues in using Twitter for population-level depression monitoring: a qualitative study. BMC Med. Ethics 17, 22 (2016).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Institute of Medicine Public Engagement and Clinical Trials: New Models and Disruptive Technologies: Workshop Summary Ch. 3 (National Academies Press, 2012).

  96. 96.

    McDonald, A. M. et al. What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies. Trials 7, 9 (2006).

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    McGregor, J. et al. The Health Informatics Trial Enhancement Project (HITE): using routinely collected primary care data to identify potential participants for a depression trial. Trials 11, 39 (2010).

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Callard, F. et al. Developing a new model for patient recruitment in mental health services: a cohort study using Electronic Health Records. BMJ Open 4, e005654 (2014).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Papoulias, C., Robotham, D., Drake, G., Rose, D. & Wykes, T. Staff and service users’ views on a ‘consent for contact’ research register within psychosis services: a qualitative study. BMC Psychiatry 14, 377 (2014).

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Robotham, D. et al. Facilitating mental health research for patients, clinicians and researchers: a mixed-method study. BMJ Open 6, e011127 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Relton, C. L. & Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Eepidemiol. 41, 161–176 (2012).

    Google Scholar 

  102. 102.

    Burgess, S., Butterworth, A., Malarstig, A. & Thompson, S. G. Use of Mendelian randomisation to assess potential benefit of clinical intervention. Br. Med. J. 345, e7325 (2012).

    Google Scholar 

  103. 103.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018).

    CAS  PubMed  Google Scholar 

  105. 105.

    Fabbri, C. et al. New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation. Pharmacogenomics J. 18, 413–421 (2018).

    CAS  PubMed  Google Scholar 

  106. 106.

    Perera, G., Khondoker, M., Broadbent, M., Breen, G. & Stewart, R. Factors associated with response to acetylcholinesterase inhibition in dementia: a cohort study from a secondary mental health care case register in London. PLoS ONE 9, e109484 (2014).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Taggart, H. The Five Year Forward View for Mental Health (Department of Health, London, 2016).

  108. 108.

    Maddox, T. M. & Ferguson, T. B. The potential of learning health care systems: The SWEDEHEART example. J. Am. Coll. Cardiol. 66, 544–546 (2015).

    PubMed  Google Scholar 

  109. 109.

    Fleming, I., Jones, M., Bradley, J. & Wolpert, M. Learning from a learning collaboration: The CORC approach to combining research, evaluation and practice in child mental health. Adm. Policy Ment. Health Ment. Health Serv. Res. 43, 297–301 (2016).

    Google Scholar 

  110. 110.

    Clark, D. M. Implementing NICE guidelines for the psychological treatment of depression and anxiety disorders: the IAPT experience. Int. Rev. Psychiatry 23, 318–327 (2011).

    PubMed  PubMed Central  Google Scholar 

  111. 111.

    Lucock, M. et al. The role of practice research networks (PRN) in the development and implementation of evidence: the northern improving access to psychological therapies PRN case study. Adm. Policy Ment. Health Ment. Health Serv. Res. 44, 919–931 (2017).

    Google Scholar 

  112. 112.

    Delgadillo, J. et al. Improving the efficiency of psychological treatment using outcome feedback technology. Behav. Res. Ther. 99, 89–97 (2017).

    PubMed  Google Scholar 

  113. 113.

    DeRubeis, R. J. et al. The trapersonalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstion. PLoS ONE 9, e83875 (2014).

    PubMed  PubMed Central  Google Scholar 

  114. 114.

    Saunders, R., Cape, J., Fearon, P. & Pilling, S. Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients. J. Affect. Disord. 197, 107–115 (2016).

    PubMed  Google Scholar 

  115. 115.

    Dimidjian, S. et al. A pragmatic randomized clinical trial of behavioral activation for depressed pregnant women. J. Consult. Clin. Psychol. 85, 26–36 (2017).

    PubMed  PubMed Central  Google Scholar 

  116. 116.

    Rossom, R. C. et al. antidepressant adherence across diverse populations and healthcare settings. Depress. Anxiety 33, 765–774 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    National Institute for Health and Clinical Excellence Data Science for Health and Care Excellence: Harnessing the UK Opportunities for New Research and Decision-making Paradigms (National Institute for Health and Clinical Excellence, 2016).

  118. 118.

    Gillan, C. M. & Whelan, R. What big data can do for treatment in psychiatry. Curr. Opin. Behav. Sci. 18, 34–42 (2017).

    Google Scholar 

  119. 119.

    Gravenhorst, F. et al. Mobile phones as medical devices in mental disorder treatment: an overview. Pers. Ubiquitous Comput. 19, 335–353 (2015).

    Google Scholar 

  120. 120.

    Ibrahim, Z. M. et al. A multi-agent platform for automating the collection of patient-provided clinical feedback. In Proc. 2015 International Conference on Autonomous Agents and Multiagent Systems 831–839 (International Foundation for Autonomous Agents and Multiagent Systems, 2015).

  121. 121.

    Donker, T. et al. Smartphones for smarter delivery of mental health programs: a systematic review. J. Med. Internet Res. 15, e247 (2013).

    PubMed  PubMed Central  Google Scholar 

  122. 122.

    Marley, J. & Farooq, S. Mobile telephone apps in mental health practice: uses, opportunities and challenges. BJPsych Bull. 39, 288–290 (2015).

    PubMed  PubMed Central  Google Scholar 

  123. 123.

    Muaremi, A., Arnrich, B. & Tröster, G. Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience 3, 172–183 (2013).

    PubMed  PubMed Central  Google Scholar 

  124. 124.

    Muaremi, A., Bexheti, A., Gravenhorst, F., Arnrich, B. & Tröster, G. Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors. In 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 185–188 (IEEE, 2014).

  125. 125.

    Mazilu, S. et al. GaitAssist: a daily-life support and training system for Parkinson’s disease patients with freezing of gait. In Proc. 32nd Annual ACM Conference on Human Factors in Computing Systems 2531–2540 (ACM, 2014).

  126. 126.

    Meyer, N. et al. Detecting early signs of relapse in psychosis using remote monitoring technology: acceptability and feasibility of a passive sensing approach. Early Interv. Psychiatry 10, 112–112 (2016).

    Google Scholar 

  127. 127.

    Kerz, M. et al. SleepSight: a wearables-based relapse prevention system for schizophrenia. In Proc. 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct 113–116 (ACM, 2016).

  128. 128.

    Keyes, J. Banking Technology Handbook (CRC Press, 1998).

  129. 129.

    Laurie, G. et al. On moving targets and magic bullets: Can the UK lead the way with responsible data linkage for health research? Int. J. Med. Inform. 84, 933–940 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Nuffield Council on Bioethics The Collection, Linking and Use of Data in Biomedical Research and Health Care: Ethical Issues (Nuffield Council on Bioethics, 2015).

  131. 131.

    Hemingway, H. et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur. Heart J. 39, 1481–1495 (2017).

    PubMed Central  Google Scholar 

  132. 132.

    Carter, P., Laurie, G. T. & Dixon-Woods, M. The social licence for research: why ran into trouble. J. Med. Ethics 41, 404–409 (2015).

    PubMed  PubMed Central  Google Scholar 

  133. 133.

    Sethi, N. & Laurie, G. T. Delivering proportionate governance in the era of eHealth: making linkage and privacy work together. Med. Law Int. 13, 168–204 (2013).

    PubMed  PubMed Central  Google Scholar 

  134. 134.

    Jones, K. H., McNerney, C. L. & Ford, D. V. Involving consumers in the work of a data linkage research unit. Int. J. Consum. Stud. 38, 45–51 (2014).

    Google Scholar 

  135. 135.

    Ennis, L. & Wykes, T. Impact of patient involvement in mental health research: longitudinal study. Br. J. Psychiatry 203, 381–386 (2018).

    Google Scholar 

Download references


The MQ Data Science group was set up by the UK mental health research charity MQ in 2015 and includes UK-based researchers from a range of disciplines working the field of mental health data science. The authors of this article are all members of the MQ Data Science group and the article stemmed from discussions at a previous meeting of the wider group. T.C.R. is a member of the Alzheimer Scotland Dementia Research Centre funded by Alzheimer Scotland. T.C.R. and A.M.M. are both members of the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (G0700704/ 84698). Funding from the Biotechnology and Biological Sciences Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, and Medical Research Council is gratefully acknowledged. K.A.S.D., Z.I. and R.S. are part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. W.L. is supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula (NIHR CLAHRC South West Peninsula). A.M.M. has received funding from the Sackler Trust, the Wellcome Trust, and an MRC Mental Health Data Pathfinder award (MC_PC_17209). R.S. has received research funding in the last three years from Janssen, Roche and GSK. The views expressed are those of the authors and not necessarily those of the NHS, the National Institute of Health Research, the Department of Health and Social Care, or any other funder. MQ sponsored the meetings from which this paper emerged. Other than one of the authors (E.W.) being employed by MQ, the charity had no role in the preparation of the manuscript and the final decision to publish was made by the corresponding author.

The MQ Data Science group:

Margaret Anderson16, Kate Aylett17, Suzy Bourke18, Anna Burhouse19, Felicity Callard20, Kathy Chapman21, Matt Cowley22, James Cusack23, Katrina A. S. Davis24, Jaime Delgadillo25, Sophie Dix6, Richard Dobson26, Gary Donohoe27, Nadine Dougall28, Johnny Downs26, Helen Fisher26,29, Amos Folarin8,26, Thomas Foley30, John Geddes31, Joardana Globerman22, Jonathan D. Hafferty2, Lamiece Hassan32, Joseph Hayes33, Helen Hodges34, Zina Ibrahim9, Becky Inkster11, Eddie Jacob22, Rowena Jacobs35, Ann John36, Cynthia Joyce6, Suky Kaur37, Maximilian Kerz26, James Kirkbride33, Gerard Leavey38, Glyn Lewis33, Keith Lloyd36, Wendy Matcham39, Margaret Maxwell40, Erin McCloskey6, Andrew M. McIntosh1,2, Andrew McQuillin33, Tamsin Newlove Delgado41, Catherine Newsome42, Kristin Nicodemus43, David Porteous43, Daniel Ray44, Tom C. Russ1,2,3,4,5, Simran Sanhu45, Daniel Smith46, Robert Stewart7, Laura Tutu6, Ayath Ullah47, Bill Vance32, Eva Woelbert6, Miranda Wolpert48, Cathy Wyse46 and Stanley Zammit49

Author information





All authors drafted individual sections of the manuscript and revised it in its entirety for final content.

Corresponding author

Correspondence to Tom C. Russ.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Russ, T.C., Woelbert, E., Davis, K.A.S. et al. How data science can advance mental health research. Nat Hum Behav 3, 24–32 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing