MedNuggetizer: Confidence-Based Information Nugget Extraction from Medical Documents

TL;DR

MedNuggetizer is a tool that uses large language models to extract and cluster relevant medical evidence from documents, aiding clinicians in evidence-based decision-making.

Contribution

It introduces a confidence-based, query-driven method for extracting and grouping medical information nuggets from multiple documents, enhancing evidence exploration.

Findings

Effective extraction of medical evidence demonstrated on prostate biopsy guidelines.

Domain expert evaluation confirms utility for clinicians and researchers.

Supports exploration of long medical documents with improved reliability.

Abstract

We present MedNuggetizer, https://mednugget-ai.de/; access is available upon request.}, a tool for query-driven extraction and clustering of information nuggets from medical documents to support clinicians in exploring underlying medical evidence. Backed by a large language model (LLM), \textit{MedNuggetizer} performs repeated extractions of information nuggets that are then grouped to generate reliable evidence within and across multiple documents. We demonstrate its utility on the clinical use case of \textit{antibiotic prophylaxis before prostate biopsy} by using major urological guidelines and recent PubMed studies as sources of information. Evaluation by domain experts shows that \textit{MedNuggetizer} provides clinicians and researchers with an efficient way to explore long documents and easily extract reliable, query-focused medical evidence.