Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Box G-BH, Providence, RI 02912, United States; Department of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, 370 W. 9th Avenue, Columbus, OH 43210, United States.
Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Box G-BH, Providence, RI 02912, United States; Department of Psychosocial Research, Butler Hospital, 345 Blackstone Blvd., Providence, RI 02906, United States.
J Affect Disord. 2024 Nov 1;364:57-64. doi: 10.1016/j.jad.2024.08.038. Epub 2024 Aug 12.
Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indicate that machine learning can be applied to momentary data to improve classification of SI. This study examined whether the classification accuracy of these models varies as a function of type of training data or characteristics of ideation.
A total of 257 psychiatric inpatients completed a 3-week battery of ecological momentary assessment and measures of suicide risk factors. The accuracy of machine learning models in classifying the presence, duration, or intensity of ideation was compared across models trained on baseline and/or momentary suicide risk data. Relative feature importance metrics were examined to identify the risk factors that were most important for outcome classification.
Models including both baseline and momentary features outperformed models with only one feature type, providing important information in both correctly classifying and differentiating individual characteristics of SI. Models classifying SI presence, duration, and intensity performed similarly.
Results of this study may not generalize beyond a high-risk, psychiatric inpatient sample, and additional work is needed to examine temporal ordering of the relationships identified.
Our results support using machine learning approaches for accurate identification of SI characteristics and underscore the importance of understanding the factors that differentiate and drive different characteristics of SI. Expansion of this work can support use of these models to guide intervention strategies.
迫切需要检测自杀意念（SI）存在或表明明显自杀风险的意念特征的策略，以指导干预措施并改善护理过渡期的护理。一些研究表明，机器学习可以应用于瞬时数据，以提高 SI 的分类准确性。本研究探讨了这些模型的分类准确性是否因训练数据的类型或意念特征的不同而有所不同。
共有 257 名精神病住院患者完成了为期 3 周的生态瞬时评估和自杀风险因素测量。比较了基于基线和/或瞬时自杀风险数据训练的模型在分类意念的存在、持续时间或强度方面的准确性。检查了相对特征重要性指标，以确定对结果分类最重要的风险因素。
同时包含基线和瞬时特征的模型优于仅具有一种特征类型的模型，在正确分类和区分 SI 的个体特征方面提供了重要信息。分类 SI 存在、持续时间和强度的模型表现相似。
本研究的结果可能不适用于高风险的精神病住院患者样本，需要进一步研究以检查所确定的关系的时间顺序。
我们的研究结果支持使用机器学习方法准确识别 SI 特征，并强调理解区分和驱动不同 SI 特征的因素的重要性。这项工作的扩展可以支持使用这些模型来指导干预策略。
