Predicting explainable dementia types with LLM-aided feature engineering.

ABSTRACT:

Motivation

The integration of Machine Learning and Artificial Intelligence (AI) into healthcare has immense potential due to the rapidly growing volume of clinical data. However, existing AI models, particularly Large Language Models (LLMs) like GPT-4, face significant challenges in terms of explainability and reliability, particularly in high-stakes domains like healthcare.

Results

This paper proposes a novel LLM-aided feature engineering approach that enhances interpretability by extracting clinically relevant features from the Oxford Textbook of Medicine. By converting clinical notes into concept vector representations and employing a linear classifier, our method achieved an accuracy of 0.72, outperforming a traditional n-gram Logistic Regression baseline (0.64) and the

SUBMITTER: Kashyap AM 

PROVIDER: S-EPMC12021793 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature