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

Yunzhen He, Hiroaki Yamagiwa, Hidetoshi Shimodaira
6/26/2024
cs.CL

No official code found. Best implementation: githubhyz/DischargeMe_BioNLP2024 (5 stars, last commit 2024-08-08).

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.

View on arXivView PDF

Code Implementations(1)

githubhyz/DischargeMe_BioNLP2024Community100%
50PythonMay 18, 20242 years ago

Similar Papers

UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Models

89% match2024

e-Health CSIRO at "Discharge Me!" 2024: Generating Discharge Summary Sections with Fine-tuned Language Models

89% match2024

WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV

85% match2024

IgnitionInnovators at "Discharge Me!": Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries

84% match1 repo32024

Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"

83% match2024

Enhanced Electronic Health Records Text Summarization Using Large Language Models

82% match2024

Cite this paper

@article{he2024shimo,
  title  = {Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections},
  author = {Yunzhen He and Hiroaki Yamagiwa and Hidetoshi Shimodaira},
  year   = {2024},
  eprint = {2406.18094},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.18094v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2406.18094.svg)](https://codeofpaper.com/paper/2406.18094)

Discussion