摘要
传统基于语音的帕金森症(PD)病情预测方法则是分别预测运动症状评分(motor-UPDRS)和总体症状评分(total-UPDRS)。为解决在单任务预测过程中,传统方法无法利用任务之间的共享信息和预测性能不佳的问题,提出了一种基于模型过滤的多任务回归方法来预测帕金森症患者的motor-UPDRS和total-UPDRS。首先,考虑到子任务语音特征对预测motor-UPDRS和total-UPDRS不同的影响,添加L1正则化项进行特征选择;其次,在构建模型的同时,根据不同帕金森患者对象分布在不同的域,添加了过滤机制,来提高预测精度。在远程帕金森数据集仿真实验中,基于模型过滤的多任务回归方法在预测UPDRS时,较单任务条件下最小二乘法(LS)模型预测motor值准确度提高了67.2%,预测total值则提高了83.3%;相比单任务条件下决策回归树(CART)模型预测motor值提高了64%,预测total值则提高了78.4%。实验结果表明,基于模型过滤的多任务回归算法对UPDRS预测要优于单任务回归算法。
The traditional speech-based Parkinson’s Disease(PD)prediction method is to predict the motor Unified Parkinson’s Disease Rating Scale(motor-UPDRS)and the total Unified Parkinson’s Disease Rating Scale(total-UPDRS)respectively.In order to solve the problem that the traditional method could not use the shared information between tasks and the poor prediction performance in the process of single task prediction,a multi-task regression method based on model filtering was proposed to predict the motor-UPDRS and total-UPDRS of Parkinson’s disease patients.Firstly,considering the different effects of the subtask speech features on the predicted motor-UPDRS and total-UPDRS,an L1 regularization term was added for feature selection.Secondly,according to different Parkinson’s patient objects distributed in different domains,a filtering mechanism was added to improve the prediction accuracy.In the simulation experiments of remote Parkinson data set,the Mean Absolute Error(MAE)of motor-UPDRS is 67.2%higher than that of the Least Squares(LS)method.Compared with the Classification And Regression Tree(CART)in the single task,the motor value increased by 64%and the total value increased by 78.4%.The results of experiment show that multi-task regression based on model filtering is superior to the single task regression algorithm for UPDRS prediction.
作者
刘峰
季薇
李云
LIU Feng;JI Wei;LI Yun(College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu 210003,China;College of Computer Science and Technology,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu 210023,China)
出处
《计算机应用》
CSCD
北大核心
2018年第11期3221-3224,3230,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61603197
61772284
41571389)
南京邮电大学科研基金资助项目(NY215104)~~
关键词
帕金森症
语音
多任务回归
模型过滤
特征选择
统一帕金森评定量表
Parkinson’s Disease(PD)
speech
multi-task regression
model filtering
feature selection
unified Parkinson’s disease rating scale
