Predicting Progression to Clinical Alzheimer's Disease Dementia Using the Random Survival Forest.

ABSTRACT:

Background

Assessing the risk of developing clinical Alzheimer's disease (AD) dementia, by machine learning survival analysis approaches, among participants registered in Alzheimer's Disease Centers is important for AD dementia management.

Objective

To construct a prediction model for the onset time of clinical AD dementia using the National Alzheimer Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) registered cohorts.

Methods

A model was constructed using the Random Survival Forest (RSF) approach and internally and externally validated on the NACC cohort and the ADNI cohort. An R package and a Shiny app were provided for accessing the model.

Results

We built a predictive model having the six predictors: delayed logical memo

SUBMITTER: Song S 

PROVIDER: S-EPMC10529100 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature