Identifying dementia neuropathology using low-burden clinical data.

ABSTRACT:

Introduction

Identifying dementia neuropathology is critical for guiding effective therapies and clinical trials. To tackle this, we developed semi-supervised models for identifying neuropathology using low-burden data to improve generalizability.

Methods

We defined low-burden data as being reasonably obtainable at a primary care setting. By using a semi-supervised learning paradigm, we can amplify the utility of low-burden data. We trained a clustering and a semi-supervised prediction model to yield clustering and prediction results for different neuropathology lesion types.

Results

Our clustering model identified two clinically meaningful outlier groups that were either neuropathology-enriched or -scarce. We predicted neuropathology burden across different pathology

SUBMITTER: Ren Y 

PROVIDER: S-EPMC12322317 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature