预测退伍军人创伤后应激障碍严重程度的自我报告早期干预:一种机器学习方法

Priyanka Annapureddy, Md Fitrat Hossain, Thomas Kissane, Wylie Frydrychowicz, Paromita Nitu, Joseph Coelho, Nadiyah Johnson, P. Madiraju, Zeno Franco, Katinka Hooyer, Niharika Jain, M. Flower, Sheikh Iqbal Ahamed
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引用次数: 3

摘要

对处于危机中的退伍军人进行早期干预是减少这一人群心理和健康负担的一个重要研究领域。与服兵役有关的创伤经历与药物和酒精滥用、自杀、愤怒以及工作和家庭关系中断有关。该项目使用机器学习(ML)模型整合社会人口学数据,自我报告基线症状,每周简短的生态瞬时评估(EMA)调查退伍军人在一个基于社区的12周同伴支持计划,以预测退伍后创伤后应激障碍严重程度。ML预测将参与者置于三个风险水平之一:低、中、高PCL-5评分。这些模型在项目的不同时间点(每周间隔)进行评估,以确定最早的一周,指导早期干预,减少退伍军人参与危险行为。投票分类器在第4周的平均f值为0.69,获得了最好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting PTSD Severity in Veterans from Self-reports for Early Intervention: A Machine Learning Approach
Early intervention for veterans in crisis represents a crucial area of study to reduce the psychological and health burdens for this population. Traumatic experiences associated with military service are associated with drug and alcohol abuse, suicidality, anger, and disrupted work and family relationships. This project used machine learning (ML) models to integrate data from sociodemographic, self-report baseline symptoms, weekly brief Ecological momentary assessment (EMA) survey of veterans in a community-based 12-week peer support program to predict the discharge PTSD severity level. The ML predictions place the participants into one of the three risk levels: low, medium, and high PCL-5 score. The models were evaluated at different timepoints (weekly intervals) of the program for identifying the earliest week to guide early intervention and reduce veterans’ engagement in risky behaviors. The best results were achieved from a voting classifier with an average f-score of 0.69 at week 4.
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