ArticleBMC psychiatry2022
Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study.
Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Integrating thyroid function and psychometric profiles for lifetime suicide-attempt risk stratification in bipolar disorder: A multi-algorithm machine-learning study.Frontiers in psychiatry · 2026Article
- Risk, reward, and suicide: how impulsivity and loss aversion influence decision-making in individuals who have attempted suicide.Frontiers in psychiatry · 2026Article
- Multimodal Machine Learning Prediction of 12-Month Suicide Attempts in Bipolar Disorder.Bipolar disorders · 2025Article
- Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.Depression and anxiety · 2025Article
- A Naturalistic Prospective Study of the Prognostic Impact of EPHX2 in Major Depressive Disorder: Impulsivity may be an Important Factor.Depression and anxiety · 2025Observational
- Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution ofDepression and anxiety · 2024Observational
- Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.Frontiers in human neuroscience · 2024Article
- Article
- Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling.Frontiers in psychology · 2023Article
- Risky decision-making in major depressive disorder: A three-level meta-analysis.International journal of clinical and health psychology : IJCHPArticle
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Authors and funding
10 authors.
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Abstract
backgroundPrevious studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD).
methodsParticipants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls (HC). All participants were required to complete a series of questionnaires, the Suicide Stroop Task (SST) and the Iowa Gambling Task (IGT). The performance in IGT was analyzed using repeated measures ANOVA. ML with extreme gradient boosting (XGBoost) classification algorithm and locally explanatory techniques assessed performance and relative importance of characteristics for predicting suicide attempts. Prediction performances were compared with the area under the curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI).
resultsDSA and DNS preferred to select the card from disadvantageous decks (decks "A" + "B") under risky situation (p = 0.023) and showed a significantly poorer learning effect during the IGT (F = 2.331, p = 0.019) compared with HC. Performance of XGBoost model based on demographic and clinical characteristics was compared with that of the model created after adding cognition data (AUC, 0.779 vs. 0.819, p > 0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm. LIMITATIONS: A limited sample size and failure to include sufficient suicide risk factors in the predictive model.
conclusionThis study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions.
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