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作 者:冯鑫 郁磊 许斐 李阳[4] 靳祯 FENG Xin;YU Lei;XU Fei;LI Yang;JIN Zhen(Complex Systems Research Center,Shanxi University,Taiyuan 030006,China;Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention,Shanxi University,Taiyuan 030006,China;The First Clinical Medical College,Shanxi Medical University,Taiyuan 030001,China;The First Hospital,Shanxi Medical University,Taiyuan 030001,China)
机构地区:[1]山西大学复杂系统研究所,山西太原030006 [2]山西大学疾病防控的数学技术与大数据分析山西省重点实验室,山西太原030006 [3]山西医科大学第一临床医学院,山西太原030001 [4]山西医科大学第一医院,山西太原030001
出 处:《云南师范大学学报(自然科学版)》2020年第5期21-24,共4页Journal of Yunnan Normal University:Natural Sciences Edition
基 金:国家自然科学基金资助项目(61803242);山西省重点研发计划资助项目(201703D421012,201803D31032);中央引导地方科技发展专项资金资助项目(YDZX20191400002563)。
摘 要:为了克服传统Cookie Theft测验费时费力、评测结果受主观影响等不足,提出了一种数字化测评方法.首先,将Cookie Theft测验转化成总分为28分的数字化量表;接着,分析对比了脑小血管病组和健康组分别在无提示语和有提示语条件下的表现差异情况;最后,综合语音相关特征及信息量相关特征,利用随机森林方法建立了脑小血管病智能诊断模型.实验结果显示,综合使用测试过程中记录的语音相关特征和信息量相关特征,脑小血管病智能诊断模型的识别准确率可达82.4%.In order to overcome the shortcomings of the traditional Cookie Theft test,such as time-consuming and labor-intensive test,and subjective influence on the evaluation results,a new digital assessment method was proposed.Firstly,the Cookie Theft test was convert into a digital scale with a total score of 28.Secondly,the performance differences between the cerebral small vessel disease group and the healthy group under the condition of silent and prompt words were analyzed and compared.Finally,based on the speech-related features and information-related features,an intelligent diagnosis model of cerebral small vessel disease was established by using random forest algorithm.The experimental results show that using the voice-related features and information-related features recorded during the test process,the recognition accuracy of the intelligent diagnosis model for cerebral small vessel disease can reach 82.4%.
关 键 词:脑小血管病 认知沟通能力 数字化测评 Cookie Theft测验
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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