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. 4 | BMC Psychiatry

Fig. 4

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

Fig. 4

The force plot for decision process of XGBoost-2 evaluating whether two MDD patients in test set had suicide attempts. Each feature provides a SHAP value for the base value of model. The final prediction value, f (x), are obtained according to the weight of features and the processing of the machine learning algorithm. When f (x) > 0, the model considers the case as DSA, otherwise it is considered as DNS. *IGT: IOWA gambling task, SST: suicide stroop task, HAMD-24:Hamilton Depression Scale-24 items, CTQ: Childhood Trauma Questionnaire

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