Minimizing Survey Questions for PTSD Prediction Following Acute Trauma.

Ben Kurzion, Chia-Hao Shih, Hong Xie, Xin Wang, Kevin S Xu
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Abstract

Traumatic experiences have the potential to give rise to post-traumatic stress disorder (PTSD), a debilitating psychiatric condition associated with impairments in both social and occupational functioning. There has been great interest in utilizing machine learning approaches to predict the development of PTSD in trauma patients from clinician assessment or survey-based psychological assessments. However, these assessments require a large number of questions, which is time consuming and not easy to administer. In this paper, we aim to predict PTSD development of patients 3 months post-trauma from multiple survey-based assessments taken within 2 weeks post-trauma. Our objective is to minimize the number of survey questions that patients need to answer while maintaining the prediction accuracy from the full surveys. We formulate this as a feature selection problem and consider 4 different feature selection approaches. We demonstrate that it is possible to achieve up to 72% accuracy for predicting the 3-month PTSD diagnosis from 10 survey questions using a mean decrease in impurity-based feature selector followed by a gradient boosting classifier.

尽量减少用于预测急性创伤后创伤后应激障碍的调查问题。
创伤经历有可能导致创伤后应激障碍(PTSD),这是一种使人衰弱的精神疾病,与社会和职业功能受损有关。人们对利用机器学习方法从临床医生的评估或基于调查的心理评估中预测创伤后应激障碍患者的发展非常感兴趣。然而,这些评估需要回答大量问题,既费时又不易操作。在本文中,我们旨在通过创伤后两周内进行的多项基于调查的评估来预测创伤后 3 个月患者的创伤后应激障碍发展情况。我们的目标是尽量减少患者需要回答的调查问题数量,同时保持完整调查的预测准确性。我们将其表述为一个特征选择问题,并考虑了 4 种不同的特征选择方法。我们证明,使用基于不纯度的平均下降特征选择器和梯度提升分类器,从 10 个调查问题中预测 3 个月的创伤后应激障碍诊断的准确率可高达 72%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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