A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

作者：

Highlights：

• Integrated machine-learning methods can predict AD severity with high accuracy.

• Model validation procedure appropriate for processing individual participant data.

• Highly accessible cognitive and functional markers more accurate than biomarkers.

• Automated decision-support tool predicts individual AD severity on continuous scale.

• System assesses undiagnosed patient data against an existing dataset of patients.

摘要

•Integrated machine-learning methods can predict AD severity with high accuracy.•Model validation procedure appropriate for processing individual participant data.•Highly accessible cognitive and functional markers more accurate than biomarkers.•Automated decision-support tool predicts individual AD severity on continuous scale.•System assesses undiagnosed patient data against an existing dataset of patients.

论文关键词：Dementia,Alzheimer's disease,Decision support system,Machine learning,Diagnosis support,Cognitive impairment

论文评审过程：Received 9 November 2018, Revised 13 March 2019, Accepted 9 April 2019, Available online 10 April 2019, Version of Record 22 April 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.022