Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer's Disease.

ABSTRACT:

Background

The progression of Alzheimer's disease (AD) varies in different patients at different stages, which makes predicting the time of disease conversions challenging.

Objective

We established an algorithm by leveraging machine learning techniques to predict the probability of the conversion time to next stage for different subjects during a given period.

Methods

Firstly, we used Kaplan-Meier (KM) estimation to get the transition curves of different AD stages, and calculated Log-rank statistics to test whether the progression rate between different stages was identical. This quantitatively confirmed the progression rates known in the literature. Then, we developed an approach based on deep learning model, DeepSurv, to predict the probabilities of time-to-conversi

SUBMITTER: Wu X 

PROVIDER: S-EPMC9840816 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature