Abstract Meaning Representation for Hospital Discharge Summarization

TL;DR

This paper proposes a novel method combining language-based graphs and deep learning to improve the reliability and trustworthiness of automatic hospital discharge summaries, addressing hallucination issues in LLMs.

Contribution

It introduces a new approach that integrates graph-based reasoning with deep learning to enhance clinical summarization accuracy and trustworthiness.

Findings

Achieves high reliability on MIMIC-III dataset

Produces trustworthy discharge summaries with reduced hallucination

Provides source code and models for reproducibility

Abstract

The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a lengthy medical document that summarizes a hospital in-patient visit). Automatically generating these summaries would free physicians to care for patients and reduce documentation burden. The goal of this work is to discover new methods that combine language-based graphs and deep learning models to address provenance of content and trustworthiness in automatic summarization. Our method shows impressive reliability results on the publicly available Medical Information Mart for Intensive III (MIMIC-III) corpus and clinical notes written by physicians at Anonymous Hospital. rovide our method, generated discharge ary output examples, source code and trained models.