摘要
意识障碍(DOC)患者的预后预测对临床治疗及患者家属都有重要意义。但目前的预后预测模型的准确度不高,敏感性和特异性均较低。本研究在基于病因、年龄和病程作为重要预测指标的基础上,融合使用基于脑功能磁共振影像的患者脑功能网络特征,利用人工智能和机器学习算法,研发出了一个预测意识障碍患者能否苏醒的计算模型,准确率达到了88%。
Although some indicators and models have been proposed to prognosticate disorders of consciousness(DOC),any single method when used alone carries a high risk of false prediction.This study aimd to develop a multidomain prognostic model that combined functional brain networks with clinical characteristics to predict one year outcomes at the single-patient level.The model discriminate patients who would later recover consciousness from those who would not with an accuracy of around 88%.
作者
宋明
SONG Ming(Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)
出处
《临床神经外科杂志》
CAS
2020年第1期8-10,共3页
Journal of Clinical Neurosurgery
基金
国家自然科学基金(31870984)
关键词
脑网络
人工智能
意识障碍
预后预测
功能磁共振
brain network
artificial intelligence
disorders of consciousness
prognostications
fMRI