ReviewBrain sciences2026
Accuracy of Machine Learning Models in Predicting Clinical Outcomes in Bipolar Disorder: A Systematic Review.
Review in Brain sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesBipolar disorder (BD) is one of the leading causes of disability worldwide, causing significant functional impairments in those affected. The heterogeneous course of BD renders the prediction of clinical progress and outcomes challenging, but it can be potentially enhanced with the use of artificial intelligence methods. In this systematic review, we aimed to examine the extant literature regarding the predictive accuracy of clinical functioning, illness affective state, relapse, and relevant predictors amongst patients with BD, using artificial intelligence methods.
methodsThe study was guided by PRISMA and the
resultsForty articles were included in this review. The area under the curve (AUC) values for clinical functioning, illness affective state, and relapse prediction were 0.59-0.72 (poor to acceptable), 0.57-0.97 (poor to outstanding), and 0.45-0.98 (poor to outstanding), respectively. Supervised, tree-based algorithms performed the best. Predictive factors included sociodemographic, clinical and psychological factors and wearable data, as well as speech and video recordings.
conclusionsExisting studies showed the potential of machine learning methods in the prediction of clinical progress and outcomes of BD (specifically functional status, affective state, and relapse) based on relevant collected variables. Longitudinal studies can further clarify and validate the associated predictive factors for earlier identification of those at risk of poorer prognosis to enhance management of BD.
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Registered trials
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 Socratic graph.