Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's.

ABSTRACT:

Introduction

To reduce demands on expert time and improve clinical efficiency, we developed a framework to evaluate whether inexpensive, accessible data could accurately classify Alzheimer's disease (AD) clinical diagnosis and predict the likelihood of progression.

Methods

We stratified relevant data into three tiers: obtainable at primary care (low-cost), mostly available at specialty visits (medium-cost), and research-only (high-cost). We trained several machine learning models, including a hierarchical model, an ensemble model, and a clustering model, to distinguish between diagnoses of cognitively unimpaired, mild cognitive impairment, and dementia due to AD.

Results

All models showed viable classification, but the hierarchical and ensemble models outperformed the

SUBMITTER: Ren Y 

PROVIDER: S-EPMC10613605 | biostudies-literature | 2023 Oct-Dec

REPOSITORIES: biostudies-literature