Abstract Meaning Representation for Hospital Discharge Summarization
This addresses the critical problem of trustworthiness in automated clinical documentation for physicians, though it appears incremental as it builds on existing methods.
The paper tackles hallucination in LLMs for generating hospital discharge summaries by combining language-based graphs and deep learning, achieving impressive reliability results on the MIMIC-III corpus and clinical notes from Anonymous Hospital.
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.