标题： 使用机器学习预测轻度创伤性脑损伤后综合征的结果 显示英文标题

标题： Predicting Post-Concussion Syndrome Outcomes with Machine Learning

摘要： 在本文中，机器学习模型被用于预测持续性脑震荡后综合征（PCS）患者的预后。 患者在研究开始前平均已经遭受了脑震荡两到三个月。 通过利用评估数据，机器学习模型旨在预测患者在四到五个月后是否还会持续患有PCS。 随机森林分类器表现最佳，准确率为85%，受试者工作特征曲线下的面积（AUC）为0.94。 发现对PCS预后具有预测性的因素包括创伤后应激障碍（PTSD）、感知不公、自我评估的预后和受伤后的症状严重程度。 这项研究的结果表明，机器学习模型可以高精度地预测PCS的预后。 通过进一步的研究，机器学习模型可能被应用于医疗环境中，以帮助持续性PCS患者。 显示英文摘要

摘要： In this paper, machine learning models are used to predict outcomes for patients with persistent post-concussion syndrome (PCS). Patients had sustained a concussion at an average of two to three months before the study. By utilizing assessed data, the machine learning models aimed to predict whether or not a patient would continue to have PCS after four to five months. The random forest classifier achieved the highest performance with an 85% accuracy and an area under the receiver operating characteristic curve (AUC) of 0.94. Factors found to be predictive of PCS outcome were Post-Traumatic Stress Disorder (PTSD), perceived injustice, self-rated prognosis, and symptom severity post-injury. The results of this study demonstrate that machine learning models can predict PCS outcomes with high accuracy. With further research, machine learning models may be implemented in healthcare settings to help patients with persistent PCS.