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

An Quang Tang, Xiuzhen Zhang, Minh Ngoc Dinh

Abstract
This paper presents our proposed approach to the Discharge Me! shared task, collocated with the 23th Workshop on Biomedical Natural Language Processing (BioNLP). In this work, we develop an LLM-based framework for solving the Discharge Summary Documentation (DSD) task, i.e., generating the two critical target sections ‘Brief Hospital Course’ and ‘Discharge Instructions’ in the discharge summary. By streamlining the recent instruction-finetuning process on LLMs, we explore several prompting strategies for optimally adapting LLMs to specific generation task of DSD. Experimental results show that providing a clear output structure, complimented by a set of comprehensive Chain-of-Thoughts (CoT) questions, effectively improves the model’s reasoning capability, and thereby, enhancing the structural correctness and faithfulness of clinical information in the generated text. Source code is available at: https://anonymous.4open.science/r/Discharge_LLM-A233
Anthology ID:
2024.bionlp-1.65
Volume:
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
731–739
Language:
URL:
https://aclanthology.org/2024.bionlp-1.65
DOI:
10.18653/v1/2024.bionlp-1.65
Bibkey:
Cite (ACL):
An Quang Tang, Xiuzhen Zhang, and Minh Ngoc Dinh. 2024. IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 731–739, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries (Tang et al., BioNLP-WS 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/2024.bionlp-1.65.pdf
PDF Search