LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

2026-03-13

AI Summary

Accurately extracting Alzheimer’s disease and related dementias (ADRD) phenotypes from unstructured electronic health records is crucial for early detection and staging, yet remains highly challenging. This work proposes LLM-MINE, a novel framework that, for the first time, integrates large language models with few-shot prompting guided by an expert-defined phenotype list to automatically mine highly discriminative ADRD phenotypes. The method substantially outperforms conventional named entity recognition and dictionary-matching approaches. Validated across multiple cohorts, it demonstrates statistically significant phenotype associations, with memory impairment emerging as the strongest discriminative feature. Furthermore, the framework achieves state-of-the-art unsupervised clustering performance (ARI = 0.290, NMI = 0.232), effectively supporting ADRD staging.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Information Extraction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging. However, this information is usually embedded in unstructured textual data rather than tabular data, making it difficult to be extracted accurately. We therefore propose LLM-MINE, a Large Language Model-based phenotype mining framework for automatic extraction of ADRD phenotypes from clinical notes. Using two expert-defined phenotype lists, we evaluate the extracted phenotypes by examining their statistical significance across cohorts and their utility for unsupervised disease staging. Chi-square analyses confirm statistically significant phenotype differences across cohorts, with memory impairment being the strongest discriminator. Few-shot prompting with the combined phenotype lists achieves the best clustering performance (ARI=0.290, NMI=0.232), substantially outperforming biomedical NER and dictionary-based baselines. Our results demonstrate that LLM-based phenotype extraction is a promising tool for discovering clinically meaningful ADRD signals from unstructured notes.

Problem

• Alzheimer's Disease
• Related Dementias
• phenotype extraction
• clinical notes
• electronic health records

Innovation

• Large Language Model
• Phenotype Mining
• Few-shot Prompting
• Alzheimer's Disease
• Clinical Notes