Deep and joint learning of longitudinal data for Alzheimer's disease prediction
作者：
Highlights：
• We propose to build a model using multiple time points to predict longitudinal scores.
• This model incorporates the feature selection, encoding, and ensemble learning.
• The selected features are encoded by the ensemble DPN to predict scores using SVR.
• The missing scores are filled by combing all the previous data.
• The experiments validate our method's effectiveness in predicting longitudinal scores.
摘要
•We propose to build a model using multiple time points to predict longitudinal scores.•This model incorporates the feature selection, encoding, and ensemble learning.•The selected features are encoded by the ensemble DPN to predict scores using SVR.•The missing scores are filled by combing all the previous data.•The experiments validate our method's effectiveness in predicting longitudinal scores.
论文关键词：Alzheimer's disease,Longitudinal scores prediction,Joint learning,Correntropy,Deep polynomial network
论文评审过程：Received 15 February 2019, Revised 14 January 2020, Accepted 26 January 2020, Available online 28 January 2020, Version of Record 13 February 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107247
