Federated learning with multi-cohort real-world data for predicting the progression from mild cognitive impairment to Alzheimer's disease.

ABSTRACT:

Introduction

Leveraging routinely collected electronic health records (EHRs) from multiple health-care institutions, this approach aims to assess the feasibility of using federated learning (FL) to predict the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

Methods

We analyzed EHR data from the OneFlorida+ consortium, simulating six sites, and used a long short-term memory (LSTM) model with a federated averaging (FedAvg) algorithm. A personalized FL approach was used to address between-site heterogeneity. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and feature importance techniques.

Results

Of 44,899 MCI patients, 6391 progressed to AD. FL models achieved a 6% improvement in AUC co

SUBMITTER: Pan J 

PROVIDER: S-EPMC11992589 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature