用机器学习预测脑震荡后综合征的结果
Predicting Post-Concussion Syndrome Outcomes with Machine Learning
摘要：
在本文中，机器学习模型用于预测持续性脑震荡后综合征（PCS）患者的结局。患者在研究前平均两到三个月遭受脑震荡。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会继续患有PCS。随机森林分类器实现了最高的性能，具有85%的准确度和0.94的受试者工作特征曲线下面积（AUC）。PCS结果的预测因素是创伤后应激障碍（PTSD），感知不公正，自我评价的预后和损伤后症状严重程度。本研究的结果表明，机器学习模型可以高准确度地预测PCS结果。随着进一步的研究，机器学习模型可以在医疗环境中实施，以帮助患有持续性PCS的患者。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
