Query-Guided Self-Supervised Summarization of Nursing Notes
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A4 Artikkeli konferenssijulkaisussa
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Date
2024
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Language
en
Pages
20
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Proceedings of Machine Learning Research, Volume 259, pp. 364-383
Abstract
Nursing notes, an important part of Electronic Health Records (EHRs), track a patient's health during a care episode. Summarizing key information in nursing notes can help clinicians quickly understand patients' conditions. However, existing summarization methods in the clinical setting, especially abstractive methods, have overlooked nursing notes and require reference summaries for training. We introduce QGSumm, a novel query-guided self-supervised domain adaptation approach for abstractive nursing note summarization. The method uses patient-related clinical queries for guidance, and hence does not need reference summaries for training. Through automatic experiments and manual evaluation by an expert clinician, we study our approach and other state-of-the-art Large Language Models (LLMs) for nursing note summarization. Our experiments show: 1) GPT-4 is competitive in maintaining information in the original nursing notes, 2) QGSumm can generate high-quality summaries with a good balance between recall of the original content and hallucination rate lower than other top methods. Ultimately, our work offers a new perspective on conditional text summarization, tailored to clinical applications.Description
Publisher Copyright: © 2024 Y. Gao, H. Moen, S. Koivusalo, M. Koskinen & P. Marttinen. | openaire: EC/H2020/101016775/EU//INTERVENE
Keywords
abstractive text summarization, nursing notes, self-supervised learning
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Citation
Gao, Y, Moen, H, Koivusalo, S, Koskinen, M & Marttinen, P 2024, ' Query-Guided Self-Supervised Summarization of Nursing Notes ', Proceedings of Machine Learning Research, vol. 259, pp. 364-383 . < https://proceedings.mlr.press/v259/gao25a.html >