Query-Guided Self-Supervised Summarization of Nursing Notes

Ya Gao, Hans Moen, Saila Koivusalo, Miika Koskinen, Pekka Marttinen
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:364-383, 2025.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v259-gao25a, title = {Query-Guided Self-Supervised Summarization of Nursing Notes}, author = {Gao, Ya and Moen, Hans and Koivusalo, Saila and Koskinen, Miika and Marttinen, Pekka}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {364--383}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/gao25a/gao25a.pdf}, url = {https://proceedings.mlr.press/v259/gao25a.html}, 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.} }
Endnote
%0 Conference Paper %T Query-Guided Self-Supervised Summarization of Nursing Notes %A Ya Gao %A Hans Moen %A Saila Koivusalo %A Miika Koskinen %A Pekka Marttinen %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-gao25a %I PMLR %P 364--383 %U https://proceedings.mlr.press/v259/gao25a.html %V 259 %X 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.
APA
Gao, Y., Moen, H., Koivusalo, S., Koskinen, M. & Marttinen, P.. (2025). Query-Guided Self-Supervised Summarization of Nursing Notes. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:364-383 Available from https://proceedings.mlr.press/v259/gao25a.html.

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