@inproceedings{zhou-etal-2022-automatic,
title = "Automatic Patient Note Assessment without Strong Supervision",
author = "Zhou, Jianing and
Thakkar, Vyom Nayan and
Yudkowsky, Rachel and
Bhat, Suma and
Bond, William F.",
editor = "Lavelli, Alberto and
Holderness, Eben and
Jimeno Yepes, Antonio and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.14",
doi = "10.18653/v1/2022.louhi-1.14",
pages = "116--126",
abstract = "Training of physicians requires significant practice writing patient notes that document the patient{'}s medical and health information and physician diagnostic reasoning. Assessment and feedback of the patient note requires experienced faculty, consumes significant amounts of time and delays feedback to learners. Grading patient notes is thus a tedious and expensive process for humans that could be improved with the addition of natural language processing. However, the large manual effort required to create labeled datasets increases the challenge, particularly when test cases change. Therefore, traditional supervised NLP methods relying on labelled datasets are impractical in such a low-resource scenario. In our work, we proposed an unsupervised framework as a simple baseline and a weakly supervised method utilizing transfer learning for automatic assessment of patient notes under a low-resource scenario. Experiments on our self-collected datasets show that our weakly-supervised methods could provide reliable assessment for patient notes with accuracy of 0.92.",
}
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<abstract>Training of physicians requires significant practice writing patient notes that document the patient’s medical and health information and physician diagnostic reasoning. Assessment and feedback of the patient note requires experienced faculty, consumes significant amounts of time and delays feedback to learners. Grading patient notes is thus a tedious and expensive process for humans that could be improved with the addition of natural language processing. However, the large manual effort required to create labeled datasets increases the challenge, particularly when test cases change. Therefore, traditional supervised NLP methods relying on labelled datasets are impractical in such a low-resource scenario. In our work, we proposed an unsupervised framework as a simple baseline and a weakly supervised method utilizing transfer learning for automatic assessment of patient notes under a low-resource scenario. Experiments on our self-collected datasets show that our weakly-supervised methods could provide reliable assessment for patient notes with accuracy of 0.92.</abstract>
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%0 Conference Proceedings
%T Automatic Patient Note Assessment without Strong Supervision
%A Zhou, Jianing
%A Thakkar, Vyom Nayan
%A Yudkowsky, Rachel
%A Bhat, Suma
%A Bond, William F.
%Y Lavelli, Alberto
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F zhou-etal-2022-automatic
%X Training of physicians requires significant practice writing patient notes that document the patient’s medical and health information and physician diagnostic reasoning. Assessment and feedback of the patient note requires experienced faculty, consumes significant amounts of time and delays feedback to learners. Grading patient notes is thus a tedious and expensive process for humans that could be improved with the addition of natural language processing. However, the large manual effort required to create labeled datasets increases the challenge, particularly when test cases change. Therefore, traditional supervised NLP methods relying on labelled datasets are impractical in such a low-resource scenario. In our work, we proposed an unsupervised framework as a simple baseline and a weakly supervised method utilizing transfer learning for automatic assessment of patient notes under a low-resource scenario. Experiments on our self-collected datasets show that our weakly-supervised methods could provide reliable assessment for patient notes with accuracy of 0.92.
%R 10.18653/v1/2022.louhi-1.14
%U https://aclanthology.org/2022.louhi-1.14
%U https://doi.org/10.18653/v1/2022.louhi-1.14
%P 116-126
Markdown (Informal)
[Automatic Patient Note Assessment without Strong Supervision](https://aclanthology.org/2022.louhi-1.14) (Zhou et al., Louhi 2022)
ACL
- Jianing Zhou, Vyom Nayan Thakkar, Rachel Yudkowsky, Suma Bhat, and William F. Bond. 2022. Automatic Patient Note Assessment without Strong Supervision. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 116–126, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.