ArticleJournal of clinical medicine2022
Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Boulevard of broken rhythms: A systematic review and meta-analysis on the relationship between sleep disturbances and suicidal behavior in bipolar disorder.European psychiatry : the journal of the Association of European Psychiatrists · 2025Pooled it
- Clinical impact of obsessive-compulsive disorder comorbidity in bipolar disorder: a systematic review and meta-analysis.European psychiatry : the journal of the Association of European Psychiatrists · 2025Pooled it
- Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables.Npj mental health research · 2026Article
- Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.BMJ open quality · 2025Article
- Cognitive Dysfunction in the Addictions (CDiA): protocol for a neuron-to-neighbourhood collaborative research program.Frontiers in psychiatry · 2025Article
- Can adaptive hyperactivation result in a positive score on the Mood Disorder Questionnaire? Evidence from a case-control study over a community survey.Frontiers in psychiatry · 2025Article
- Research landscape analysis on dual diagnosis of substance use and mental health disorders: key contributors, research hotspots, and emerging research topics.Annals of general psychiatry · 2024Article
- Does Screening for Bipolar Disorders Identify a "Dysregulation of Mood, Energy, and Social Rhythms Syndrome" (DYMERS)? A Heuristic Working Hypothesis.Journal of clinical medicine · 2023Article
- Recent Advances in Dual Disorders (Addiction and Other Mental Disorders).Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42-13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48-6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone.
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