Content area
Full Text
Molecular Psychiatry (2017) 22, 3743 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved 1359-4184/17
http://www.nature.com/mp
Web End =www.nature.com/mp
EXPERT REVIEW
Predictive analytics in mental health: applications, guidelines, challenges and perspectives
T Hahn1, AA Nierenberg2,3 and S Whiteld-Gabrieli4
The emerging eld of 'predictive analytics in mental health' has recently generated tremendous interest with the bold promise to revolutionize clinical practice in psychiatry paralleling similar developments in personalized and precision medicine. Here, we provide an overview of the key questions and challenges in the eld, aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad and informed discussion involving all stakeholders including researchers, clinicians, patients, funding bodies and
policymakers.
Molecular Psychiatry (2017) 22, 3743; doi:http://dx.doi.org/10.1038/mp.2016.201
Web End =10.1038/mp.2016.201 ; published online 15 November 2016
Mental disorders are among the most debilitating diseases in industrialized nations today.1,2 The immense economic loss35 mirrors the enormous suffering of patients and their friends and relatives.612 In addition, health-care costs as well as the number of individuals diagnosed with psychiatric disorders are projected to disproportionately rise within the next 20 years.13 With an ever-growing number of patients, the future quality of health care in psychiatry will crucially depend on the timely translation of research ndings into more effective and efcient patient care. Despite the certainly impressive contributions of psychiatric research to our understanding of the etiology and pathogenesis of mental disorders, the ways in which we diagnose and treat psychiatric patients have largely remained unchanged for decades.14
Recognizing this translational roadblock, we currently witness an explosion of interest in the emerging eld of predictive analytics in mental health, paralleling similar developments in personalized or precision medicine.1519 In contrast to the vast majority of investigations employing group-level statistics, predictive analytics aims to build models which allow for individual (that is, single subject) predictions, thereby moving from the description of patients (hindsight) and the investigation of statistical group differences or associations (insight) toward models capable of predicting current or future characteristics for individual patients (foresight), thus allowing for a direct assessment of a models clinical utility (Figure 1).
Within this framework, we can differentiate three main areas of clinical application of predictive...