@inproceedings{naskar-etal-2024-mlbmikabr,
title = "{MLBMIKABR} at {``}Discharge Me!{''}: Concept Based Clinical Text Description Generation",
author = "Naskar, Abir and
Hocking, Jane and
Chondros, Patty and
Boyle, Douglas and
Conway, Mike",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.66",
pages = "740--747",
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.",
}
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%0 Conference Proceedings
%T MLBMIKABR at “Discharge Me!”: Concept Based Clinical Text Description Generation
%A Naskar, Abir
%A Hocking, Jane
%A Chondros, Patty
%A Boyle, Douglas
%A Conway, Mike
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F naskar-etal-2024-mlbmikabr
%X 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.
%U https://aclanthology.org/2024.bionlp-1.66
%P 740-747
Markdown (Informal)
[MLBMIKABR at “Discharge Me!”: Concept Based Clinical Text Description Generation](https://aclanthology.org/2024.bionlp-1.66) (Naskar et al., BioNLP-WS 2024)
ACL