Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.
Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.
复制标题
使用机器学习方法评估自杀危机清单对近期自杀行为的预测能力。
DOI：
10.1002/mpr.1863
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发表时间：
2021-03
影响因子：
3.1
通讯作者：
Galynker I
中科院分区：
文献类型：
--
作者：
Parghi N;Chennapragada L;Barzilay S;Newkirk S;Ahmedani B;Lok B;Galynker I
本研究探讨使用自杀危机量表(SCI)的机器学习(ML)分析来预测近期自杀行为，SCI衡量自杀危机综合症，自杀前的心理状态。SCI资料收集自高危精神疾病住院患者(N=591)，根据他们的短期自杀行为分组，即在服药至1个月随访日之间试图自杀的患者(N=20)和没有自杀的患者(N=571)。数据分析采用三种预测算法(Logistic回归、随机森林和梯度提升)和三种抽样方法(分裂样本、合成少数过抽样技术和增强型Bootstrap)。改进的Bootstrap方法明显优于其他抽样方法，其中随机森林(98.0%的准确率；33.9%的召回率；71.0%的精确度-回忆曲线下的面积[AUPRC]；87.8%的面积在接收者操作特征[AUROC]下)和梯度提升(94.0%的准确率；48.9%的召回率；70.5%的AUPRC；89.4%的AUROC)算法在使用该数据集预测近期自杀行为的阳性病例方面表现最好。ML可用于分析心理测量量表的数据，如SCI，并可用于预测近期自杀行为。然而，在当前分析数据高度不平衡的情况下，必须仔细考虑和选择衡量绩效的最佳方法。
This study explores the prediction of near‐term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state. SCI data were collected from high‐risk psychiatric inpatients (N = 591) grouped based on their short‐term suicidal behavior, that is, those who attempted suicide between intake and 1‐month follow‐up dates (N = 20) and those who did not (N = 571). Data were analyzed using three predictive algorithms (logistic regression, random forest, and gradient boosting) and three sampling approaches (split sample, Synthetic minority oversampling technique, and enhanced bootstrap). The enhanced bootstrap approach considerably outperformed the other sampling approaches, with random forest (98.0% precision; 33.9% recall; 71.0% Area under the precision‐recall curve [AUPRC]; and 87.8% Area under the receiver operating characteristic [AUROC]) and gradient boosting (94.0% precision; 48.9% recall; 70.5% AUPRC; and 89.4% AUROC) algorithms performing best in predicting positive cases of near‐term suicidal behavior using this dataset. ML can be useful in analyzing data from psychometric scales, such as the SCI, and for predicting near‐term suicidal behavior. However, in cases such as the current analysis where the data are highly imbalanced, the optimal method of measuring performance must be carefully considered and selected.
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DOI：
10.1126/science.ns-4.93.453-a
发表时间：
1884-11-14
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Peirce, C S
通讯作者：
Peirce, C S
影响因子：
39.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
17.7
作者：
Simon, Gregory E.;Stewart, Christine;Carrell, David S.
通讯作者：
Carrell, David S.
影响因子：
22.4
作者：
Franklin, Joseph C.;Ribeiro, Jessica D.;Nock, Matthew K.
通讯作者：
Nock, Matthew K.
影响因子：
6.7
作者：
Rudd, M David
通讯作者：
Rudd, M David