Mining standardized neurological signs and symptoms data for concussion identification.
Mining standardized neurological signs and symptoms data for concussion identification.
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挖掘标准化神经体征和症状数据以进行脑震荡识别。
DOI：
10.1109/bhi.2017.7897261
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Wang,MayD
中科院分区：
文献类型：
--
作者：
Venugopalan,Janani;LaPlaca,MichelleC;Wang,MayD
The Centers for Disease Control estimate that 1.6 to 3.8 million concussions occur in sports and recreational activities annually. Studies have shown that concussions increase the risk of future injuries and mild cognitive disorders. Despite extensive research on sports related concussion risk factors, the factors which are most predictive of concussion outcome and recovery time course remain unknown. In order to overcome the issue of physician bias and to identify the factors which can best predict concussion diagnosis, we propose a multi-variate logistic regression based analysis. We demonstrate our results on a dataset with 126 subjects (ages 12-31). Our results indicate that among 322 features, our model selected 27-29 features which included a history of playing sports, history of a previous concussion, drowsiness, nausea, trouble focusing as measured by a common symptom list, and oculomotor function. The features picked using our model were found to be highly predictive of concussions and gave a prediction performance accuracy greater than 90%, Matthews correlation coefficient greater than 0.8 and the area under the curve greater than 0.95.
