Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections

TL;DR

This paper introduces a prompt-driven concatenation method combined with LoRA fine-tuning of ClinicalT5-large to generate discharge summaries, aiming to reduce clinicians' documentation effort in EHRs.

Contribution

It presents a novel pipeline that extracts, prompts, and concatenates EHR sections for improved discharge summary generation using fine-tuned language models.

Findings

Achieved a ROUGE-1 score of 0.394 on test data.

Comparable performance to top solutions in the shared task.

Demonstrated effectiveness of prompt-driven concatenation for clinical text generation.

Abstract

In this paper, we present our approach to the shared task "Discharge Me!" at the BioNLP Workshop 2024. The primary goal of this task is to reduce the time and effort clinicians spend on writing detailed notes in the electronic health record (EHR). Participants develop a pipeline to generate the "Brief Hospital Course" and "Discharge Instructions" sections from the EHR. Our approach involves a first step of extracting the relevant sections from the EHR. We then add explanatory prompts to these sections and concatenate them with separate tokens to create the input text. To train a text generation model, we perform LoRA fine-tuning on the ClinicalT5-large model. On the final test data, our approach achieved a ROUGE-1 score of $0.394$, which is comparable to the top solutions.