Leveraging Geolocation in Clinical Records to Improve Alzheimer's Disease Diagnosis Using DMV Framework

TL;DR

This paper introduces a novel framework that integrates geolocation data with clinical notes using advanced embedding models to improve early Alzheimer's Disease risk prediction accuracy.

Contribution

It presents the DMV framework utilizing Llama3-70B and GPT-4o models to incorporate geolocation data into AD risk assessment, significantly reducing prediction error.

Findings

Geolocation data integration reduces prediction error by over 28%.

The framework improves early AD risk prediction accuracy.

Environmental context enhances clinical decision-making.

Abstract

Alzheimer's Disease (AD) early detection is critical for enabling timely intervention and improving patient outcomes. This paper presents a DMV framework using Llama3-70B and GPT-4o as embedding models to analyze clinical notes and predict a continuous risk score associated with early AD onset. Framing the task as a regression problem, we model the relationship between linguistic features in clinical notes (inputs) and a target variable (data value) that answers specific questions related to AD risk within certain topic categories. By leveraging a multi-faceted feature set that includes geolocation data, we capture additional environmental context potentially linked to AD. Our results demonstrate that the integration of the geolocation information significantly decreases the error of predicting early AD risk scores over prior models by 28.57% (Llama3-70B) and 33.47% (GPT4-o). Our findings suggest that this combined approach can enhance the predictive accuracy of AD risk assessment, supporting early diagnosis and intervention in clinical settings. Additionally, the framework's ability to incorporate geolocation data provides a more comprehensive risk assessment model that could help healthcare providers better understand and address environmental factors contributing to AD development.