MLBMIKABR at “Discharge Me!”: Concept Based Clinical Text Description Generation

Abir Naskar, Jane Hocking, Patty Chondros, Douglas Boyle, Mike Conway

Abstract
This paper presents a method called Concept Based Description Generation, aimed at creating summaries (Brief Hospital Course and Discharge Instructions) using source (Discharge and Radiology) texts. We propose a rule-based approach for segmenting both the source and target texts. In the target text, we not only segment the content but also identify the concept of each segment based on text patterns. Our methodology involves creating a combined summarized version of each text segment, extracting important information, and then fine-tuning a Large Language Model (LLM) to generate aspects. Subsequently, we fine-tune a new LLM using a specific aspect, the combined summary, and a list of all aspects to generate detailed descriptions for each task. This approach integrates segmentation, concept identification, summarization, and language modeling to achieve accurate and informative descriptions for medical documentation tasks. Due to lack to time, We could only train on 10000 training data.
Anthology ID:
2024.bionlp-1.66
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:
740–747
Language:
URL:
https://aclanthology.org/2024.bionlp-1.66
DOI:
Bibkey:
Cite (ACL):
Abir Naskar, Jane Hocking, Patty Chondros, Douglas Boyle, and Mike Conway. 2024. MLBMIKABR at “Discharge Me!”: Concept Based Clinical Text Description Generation. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 740–747, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
MLBMIKABR at “Discharge Me!”: Concept Based Clinical Text Description Generation (Naskar et al., BioNLP-WS 2024)
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