用恐高症数据迁移学习，客观评估创伤后应激障碍严重程度
Quantitative Evaluation of the Severity of Posttraumatic Stress Disorder through Transfer Learning from Specific Phobia Data
- 基于多变量核密度估计，从生理信号中提取创伤应激特征
- 分类准确率86%，严重程度预测误差仅5.6（MAE）
- 适合临床筛查与随访，无需复杂设备，降低主观偏差
创伤后应激障碍（PTSD）是一种普遍且严重影响身心健康的疾病。当前临床评估多依赖主观判断，耗时费力且易受人为偏见影响。本研究提出一种基于多变量核密度估计（MKDE）的机器学习方法，用于客观评估PTSD严重程度。研究采集了21名参与者在沉浸式模拟中的心率（HR）和皮肤电反应（GSR）信号，以及其PCL-M量表得分。利用公开的蜘蛛恐惧症数据集训练恐惧反应模型，并从军事场景数据中提取出与PTSD相关的预测特征。模型在区分有无PTSD（PCL-M阈值36）上达到86%准确率，平均绝对误差（MAE）为5.6，对临床严重程度评分的平均绝对百分比误差为17%。该算法展现出在筛查与随访中实现客观、低成本评估的潜力。
原文摘要 · Abstract (English)
Posttraumatic stress disorder (PTSD) is a prevalent and debilitating mental health condition with significant personal and societal impacts. Current clinical assessments of PTSD often rely on subjective evaluations, which can be time-consuming, costly, and prone to human bias. This study proposes a machine learning (ML) approach based on multivariate kernel density estimation (MKDE) technique for the objective evaluation of PTSD severity. We collected heart rate (HR) and galvanic skin response (GSR) signals as well as PTSD Checklist - Military Version (PCL-M) labels from 21 participants during an immersive simulation. A fear-response model was trained on a public arachnophobia dataset, and predictive features of PTSD were extracted from the fear-response curves estimated on the military dataset. The model achieved an accuracy of 86\% in classifying PTSD status, effectively distinguishing participants with and without PTSD (PCL-M threshold of 36). The average mean absolute error (MAE) of the models is 5.6, and it estimated a clinical PTSD severity scale with a mean absolute percentage error of 17\%. Our algorithm demonstrates promising potential for enhancing estimation of PTSD severity and followup by offering an objective and low-effort evaluation approach using physiology. These findings suggest clinical utility in both screening and follow-up settings.
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