Department of Psychiatry, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China.
BMC Psychiatry. 2022 Sep 1;22(1):580. doi: 10.1186/s12888-022-04223-4.
Previous 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).
Participants 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).
DSA 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.
A limited sample size and failure to include sufficient suicide risk factors in the predictive model.
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.
先前的研究表明，认知缺陷可能会增加自杀的风险。我们的研究旨在使用重性抑郁障碍（MDD）患者的认知，开发一种基于机器学习（ML）算法的自杀风险预测模型。
参与者包括 52 名抑郁自杀未遂者（DSA）、61 名抑郁非自杀未遂者（DNS）和 98 名健康对照者（HC）。所有参与者均需完成一系列问卷、自杀 Stroop 任务（SST）和爱荷华赌博任务（IGT）。使用重复测量方差分析对 IGT 进行分析。使用极端梯度提升（XGBoost）分类算法和局部解释技术对预测自杀企图的特征的性能和相对重要性进行了评估。使用曲线下面积（AUC）、决策曲线分析（DCA）和净重新分类改善（NRI）比较预测性能。
DSA 和 DNS 在风险情况下更倾向于从不利牌组（牌组“ A”+“ B”）中选择牌（p=0.023），并且在 IGT 中表现出明显较差的学习效果（F=2.331，p=0.019）与 HC 相比。基于人口统计学和临床特征的 XGBoost 模型的性能与添加认知数据后创建的模型的性能进行了比较（AUC，0.779 与 0.819，p>0.05）。模型的净收益得到改善，并且 NRI 为 5.3％，认知导致连续重新分类改善。几个临床维度是 XGBoost 分类算法中的重要预测因子。
样本量有限，并且预测模型中未包含足够的自杀风险因素。
本研究表明，认知缺陷可能是预测 MDD 患者自杀企图的重要危险因素。结合其他人口统计学特征和临床问卷中的属性，认知功能可以提高 ML 模型的预测效果。此外，解释性 ML 模型可以帮助临床医生检测 MDD 患者中每个自杀未遂者的特定风险因素。这些发现可能有助于临床医生快速准确地检测到那些自杀风险较高的人，并帮助他们做出积极的治疗决策。
