Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.

ABSTRACT:

Introduction

Accurately predicting Alzheimer's Disease (AD) progression is useful for clinical care. The 2019 TADPOLE (The Alzheimer's Disease Prediction Of Longitudinal Evolution) challenge evaluated 92 algorithms from 33 teams worldwide. Unlike typical clinical prediction studies, TADPOLE accommodates (1) variable number of observed timepoints across patients, (2) missing data across modalities and visits, and (3) prediction over an open-ended time horizon, which better reflects real-world data. However, TADPOLE only used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, so how well top algorithms generalize to other cohorts remains unclear.

Methods

We tested five algorithms in three external datasets covering 2,312 participants and 13,200 timepoints. The alg

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11601715 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature