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

• URL: http://arxiv.org/abs/2603.13673v1
• Date: Sat, 14 Mar 2026 00:45:23 GMT
• Title: LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes
• Abstract summary: We propose a Large Language Model-based phenotype mining framework for automatic extraction of Alzheimer's Disease and Related Dementias phenotypes from clinical notes.<n>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.<n>Our results demonstrate that LLM-based phenotype extraction is a promising tool for discovering clinically meaningful ADRD signals from unstructured notes.
• Score: 13.189180222247337
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports [49.5225801722164]
  Existing metrics obscure this requirement by reducing report quality to a medically ungrounded scalar.<n>We study this boundary using ReEvalMed benchmark as testbed and evaluate metric-level clinical significance.
  arXiv  Detail & Related papers  (2026-06-17T08:10:30Z)
• Multimodal Graph-based Classification of Esophageal Motility Disorders [73.90451172929117]
  Diagnosing esophageal motility disorders pose significant challenges due to the complexity of high-resolution impedance manometry data and variability in clinical interpretation.<n>This work explores the feasibility of a multimodal Machine Learning-based classification approach that combines HRIM recordings with patient-specific information and incorporates a graph-based modeling of esophageal physiology.
  arXiv  Detail & Related papers  (2026-05-13T14:52:12Z)
• Learning Diagnostic Reasoning for Decision Support in Toxicology [34.623049546258294]
  We present DeToxR, the first adaptation of Reinforcement Learning (RL) to emergency toxicology.<n>We design a robust data-fusion engine for multi-label prediction across 14 substance classes.<n>We optimize the model's reasoning directly using a clinical performance reward.
  arXiv  Detail & Related papers  (2026-03-31T11:26:45Z)
• Enhancing Phenotype Discovery in Electronic Health Records through Prior Knowledge-Guided Unsupervised Learning [1.0414011156637284]
  We operationalize a Bayesian latent class framework for phenotyping.<n>We identify an asthma sub-phenotype informed by features of Type 2 (T2) inflammation.
  arXiv  Detail & Related papers  (2025-11-03T22:25:07Z)
• A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies [28.253741893497136]
  We propose WEST (WEakly Supervised Transformer for rare disease phenotyping and subphenotyping from EHRs) to enable large-scale phenotyping.<n>We evaluate WEST on two rare pulmonary diseases using EHR data from Boston Children's Hospital and show that it outperforms existing methods in phenotype classification, identification of clinically meaningful subphenotypes, and prediction of disease progression.
  arXiv  Detail & Related papers  (2025-07-01T23:11:20Z)
• Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
  Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape. RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
  arXiv  Detail & Related papers  (2024-07-17T07:44:18Z)
• SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy [45.2233252981348]
  Large Language Models (LLMs) have been shown to encode clinical knowledge.<n>We present SemioLLM, an evaluation framework that benchmarks 6 state-of-the-art models.<n>We show that most LLMs are able to accurately and confidently generate probabilistic predictions of seizure onset zones in the brain.
  arXiv  Detail & Related papers  (2024-07-03T11:02:12Z)
• A Hybrid Framework with Large Language Models for Rare Disease Phenotyping [4.550497164299771]
  Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary.
  arXiv  Detail & Related papers  (2024-05-16T20:59:28Z)
• Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models [59.89454513692417]
  Tabular data is often hidden in text, particularly in medical diagnostic reports. We propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics.
  arXiv  Detail & Related papers  (2023-06-08T09:12:28Z)
• PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data [42.56953523499849]
  We present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction. We leverage ensemble learning to combine outputs from single-modal models and multi-modal models to improve phenotype predictions.
  arXiv  Detail & Related papers  (2023-03-19T23:41:04Z)
• Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions [0.6875312133832078]
  We provide a comprehensive review of low-rank approximation-based approaches for computational phenotyping. Recent developments have adapted low-rank data approximation methods by incorporating different constraints and regularizations that facilitate interpretability further.
  arXiv  Detail & Related papers  (2022-09-01T09:47:27Z)
• Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes [13.096008602034086]
  Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. We propose a method using brain and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT) The weakly supervised approach is proposed to learn a confirmation phenotype model to improve Text-to-UMLS linking, without annotated data from domain experts.
  arXiv  Detail & Related papers  (2022-05-11T17:38:24Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.