Evidence map›Paper›PMID 34002503›Full record

ArticleWorld psychiatry : official journal of the World Psychiatric Association (WPA)2021

The promise of machine learning in predicting treatment outcomes in psychiatry.

Adam M Chekroud, Julia Bondar, Jaime Delgadillo, Gavin Doherty, Akash Wasil, Marjolein Fokkema, Zachary Cohen, Danielle Belgrave, Robert DeRubeis, Raquel Iniesta and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World psychiatry : official journal of the World Psychiatric Association (WPA), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 214 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
214citing papers in PubMed, 9 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

214 citing papers in PubMed, 9 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Trial
  11. Trial
  12. Trial
  13. Trial
  14. Trial
  15. Trial
  16. Trial
  17. Trial
  18. Trial
  19. Trial
  20. Trial

154 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Adam M ChekroudDepartment of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
Julia BondarSpring Health, New York City, NY, USA.
Jaime DelgadilloClinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK.
Gavin DohertySchool of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.
Akash WasilDepartment of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
Marjolein FokkemaDepartment of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands.
Zachary CohenDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
Danielle BelgraveMicrosoft Research, Cambridge, UK.
Robert DeRubeisDepartment of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
Raquel IniestaDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK.
Dominic DwyerDepartment of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany.
Karmel ChoiHarvard T.H. Chan School of Public Health, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Indexed as

Computational psychiatryelectronic health recordsexternal validationmachine learningpharmacotherapiespredictionpsy­chotherapiessmartphone datatreatment outcomes

Identifiers

PMID34002503
PMCPMC8129866

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.