Evidence map›Paper›PMID 39687745›Full record

ArticleBiomarkers in neuropsychiatry2024

Biomarker discovery using machine learning in the psychosis spectrum.

Walid Yassin, Kendra M Loedige, Cassandra M J Wannan, Kristina M Holton, Jonathan Chevinsky, John Torous, Mei-Hua Hall, Rochelle Ruby Ye, Poornima Kumar, Sidhant Chopra and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomarkers in neuropsychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Deep learning for psychiatric genomics: from tools to applications.Current opinion in genetics & development · 2026
    Review
  3. Review
  4. Article
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

14 authors.

Walid YassinHarvard Medical School, Boston, MA, USA.
Kendra M LoedigeWestern University, London, ON, Canada.
Cassandra M J WannanThe University of Melbourne, Parkville, Victoria, Australia.
Kristina M HoltonHarvard Medical School, Boston, MA, USA.
Jonathan ChevinskyHarvard Medical School, Boston, MA, USA.
John TorousHarvard Medical School, Boston, MA, USA.
Mei-Hua HallHarvard Medical School, Boston, MA, USA.
Rochelle Ruby YeThe University of Melbourne, Parkville, Victoria, Australia.
Poornima KumarHarvard Medical School, Boston, MA, USA.
Sidhant ChopraYale University, New Haven, CT, USA.
Kshitij KumarMcLean Hospital, Belmont, MA, USA.
Jibran Y KhokharWestern University, London, ON, Canada.
Eric MargolisHarvard University, Boston, MA, USA.
Alessandro S De NadaiHarvard Medical School, Boston, MA, USA.

Funding

Neurobiological Markers as Predictors of Later Functional Outcome in First Episode PsychosisR01MH109687 · NIMH · MCLEAN HOSPITAL · PI HALL, MEI-HUA · 2016 to 2021
$2.5M
Identification of Distinct Biotypes in Clinical High Risk for Psychosis State Using Objective Brain-Based BiomarkersR21MH133001 · NIMH · BETH ISRAEL DEACONESS MEDICAL CENTER · PI YASSINE, WALID · 2023 to 2024
$346k
NIMH NIH HHS R01 MH109687NIMH NIH HHS R21 MH133001
6 · The paper itself

Abstract

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

Indexed as

Bipolar disorderClinical high riskFirst episodeMachine learningPsychosisSchizophrenia

Identifiers

PMID39687745
PMCPMC11649307

What OpenQuestion holds

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Registered trials

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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.