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. Conclusion This 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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Fig. 1 | BMC Psychiatry

Fig. 1

From: Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

Fig. 1

Performance of IGT. a Comparison of IGT scores by blocks among three groups: healthy controls (HC), depressed non-attempts (DNA) and depressed suicide attempters (DSA). b Comparison of IGT scores by blocks among two groups divided by the lethality of suicide attempts: low and high lethality. *DSA: depressed suicide attempter, DNS: depressed non-suicide attempter, HC: healthy control, IGT: IOWA gambling task

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