Journal of Computer Technology and Software

Vol. 4 No. 9 (2025)
Articles

Evidence-Grounded Large Language Models for Reliable Clinical Information Extraction from Longitudinal Medical Records

  • Felix Moreau

Published 2025-09-30

How to Cite

Moreau, F. (2025). Evidence-Grounded Large Language Models for Reliable Clinical Information Extraction from Longitudinal Medical Records. Journal of Computer Technology and Software, 4(9). Retrieved from https://www.ashpress.org/index.php/jcts/article/view/386
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Large language models (LLMs) have demonstrated strong capabilities in processing clinical text, but hallucination and insufficient evidence attribution remain major obstacles to their reliable use in medical applications. This paper presents an evidence-grounded LLM framework for extracting clinically relevant information from longitudinal medical records while explicitly linking generated outputs to supporting evidence. The proposed approach combines retrieval-augmented generation with hierarchical clinical document encoding to identify relevant evidence across temporally distributed patient records. An evidence verification module evaluates the consistency between extracted clinical concepts and their supporting source passages, while an uncertainty-aware decoding strategy suppresses predictions with insufficient evidence. The framework is evaluated on clinical information extraction tasks involving diagnoses, medications, symptoms, and treatment histories. Experimental results show improvements in extraction accuracy, evidence consistency, and hallucination reduction compared with standard prompting and retrieval-augmented baselines. The proposed framework provides a practical approach toward more reliable and auditable LLM-based clinical data processing.

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