Wshah Safwan, Skalka Christian, Price Matthew
University of Vermont, Burlington, VT, United States.
JMIR Ment Health. 2019 Jul 22;6(7):e13946. doi: 10.2196/13946.
A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD).
Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions.
We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones.
We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma.
These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention.
美国大多数成年人都经历过潜在的创伤性事件，但只有少数人会发展为创伤后应激障碍（PTSD）等损害心理健康的状况。
在创伤暴露后不久识别出风险较高的人群是一项临床挑战。本研究的目的是开发计算方法，以更有效地识别高危患者，从而支持更好的早期干预。
我们提出通过机器学习（ML）归纳模型，利用智能手机收集的数据中的自我报告症状，自动预测创伤后1个月患者的PTSD症状是否会加重。
我们表明，使用支持向量机、朴素贝叶斯、逻辑回归和随机森林算法的集成模型能够准确预测PTSD症状加重情况，曲线下面积（AUC）为0.85。此外，我们还表明，获得该AUC仅需要7个自我报告项目（特征）。最重要的是，我们表明在创伤后10至20天就能做出准确预测。
这些结果表明，基于智能手机的简单患者调查，结合使用经过ML训练的模型进行自动分析，能够识别出有发展为PTSD症状加重风险的人群，并以此为目标进行早期干预。
