LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Despite the remarkable performance of Large Language Models (LLMs) in
automated discharge summary generation, they still suffer from hallucination
issues, such as generating inaccurate content or fabricating information
without valid sources. In addition, electronic medical records (EMRs) typically
consist of long-form data, making it challenging for LLMs to attribute the
generated content to the sources. To address these challenges, we propose LCDS,
a Logic-Controlled Discharge Summary generation system. LCDS constructs a
source mapping table by calculating textual similarity between EMRs and
discharge summaries to constrain the scope of summarized content. Moreover,
LCDS incorporates a comprehensive set of logical rules, enabling it to generate
more reliable silver discharge summaries tailored to different clinical fields.
Furthermore, LCDS supports source attribution for generated content, allowing
experts to efficiently review, provide feedback, and rectify errors. The
resulting golden discharge summaries are subsequently recorded for incremental
fine-tuning of LLMs. Our project and demo video are in the GitHub repository
https://github.com/ycycyc02/LCDS.
