@inproceedings{cui-etal-2023-medtem2,
title = "{M}ed{T}em2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries",
author = "Cui, Yang and
Han, Lifeng and
Nenadic, Goran",
editor = "Padmakumar, Vishakh and
Vallejo, Gisela and
Fu, Yao",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-srw.27",
doi = "10.18653/v1/2023.acl-srw.27",
pages = "160--183",
abstract = "Discharge summaries are comprehensive medical records that encompass vital information about a patient{'}s hospital stay. A crucial aspect of discharge summaries is the temporal information of treatments administered throughout the patient{'}s illness. With an extensive volume of clinical documents, manually extracting and compiling a patient{'}s medication list can be laborious, time-consuming, and susceptible to errors. The objective of this paper is to build upon the recent development on clinical NLP by temporally classifying treatments in clinical texts, specifically determining whether a treatment was administered between the time of admission and discharge from the hospital. State-of-the-art NLP methods including prompt-based learning on Generative Pre-trained Transformers (GPTs) models and fine-tuning on pre-trained language models (PLMs) such as BERT were employed to classify temporal relations between treatments and hospitalisation periods in discharge summaries. Fine-tuning with the BERT model achieved an F1 score of 92.45{\%} and a balanced accuracy of 77.56{\%}, while prompt learning using the T5 model and mixed templates resulted in an F1 score of 90.89{\%} and a balanced accuracy of 72.07{\%}.Our codes and data are available at \url{https://github.com/HECTA-UoM/MedTem}.",
}
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<abstract>Discharge summaries are comprehensive medical records that encompass vital information about a patient’s hospital stay. A crucial aspect of discharge summaries is the temporal information of treatments administered throughout the patient’s illness. With an extensive volume of clinical documents, manually extracting and compiling a patient’s medication list can be laborious, time-consuming, and susceptible to errors. The objective of this paper is to build upon the recent development on clinical NLP by temporally classifying treatments in clinical texts, specifically determining whether a treatment was administered between the time of admission and discharge from the hospital. State-of-the-art NLP methods including prompt-based learning on Generative Pre-trained Transformers (GPTs) models and fine-tuning on pre-trained language models (PLMs) such as BERT were employed to classify temporal relations between treatments and hospitalisation periods in discharge summaries. Fine-tuning with the BERT model achieved an F1 score of 92.45% and a balanced accuracy of 77.56%, while prompt learning using the T5 model and mixed templates resulted in an F1 score of 90.89% and a balanced accuracy of 72.07%.Our codes and data are available at https://github.com/HECTA-UoM/MedTem.</abstract>
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%0 Conference Proceedings
%T MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries
%A Cui, Yang
%A Han, Lifeng
%A Nenadic, Goran
%Y Padmakumar, Vishakh
%Y Vallejo, Gisela
%Y Fu, Yao
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F cui-etal-2023-medtem2
%X Discharge summaries are comprehensive medical records that encompass vital information about a patient’s hospital stay. A crucial aspect of discharge summaries is the temporal information of treatments administered throughout the patient’s illness. With an extensive volume of clinical documents, manually extracting and compiling a patient’s medication list can be laborious, time-consuming, and susceptible to errors. The objective of this paper is to build upon the recent development on clinical NLP by temporally classifying treatments in clinical texts, specifically determining whether a treatment was administered between the time of admission and discharge from the hospital. State-of-the-art NLP methods including prompt-based learning on Generative Pre-trained Transformers (GPTs) models and fine-tuning on pre-trained language models (PLMs) such as BERT were employed to classify temporal relations between treatments and hospitalisation periods in discharge summaries. Fine-tuning with the BERT model achieved an F1 score of 92.45% and a balanced accuracy of 77.56%, while prompt learning using the T5 model and mixed templates resulted in an F1 score of 90.89% and a balanced accuracy of 72.07%.Our codes and data are available at https://github.com/HECTA-UoM/MedTem.
%R 10.18653/v1/2023.acl-srw.27
%U https://aclanthology.org/2023.acl-srw.27
%U https://doi.org/10.18653/v1/2023.acl-srw.27
%P 160-183
Markdown (Informal)
[MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries](https://aclanthology.org/2023.acl-srw.27) (Cui et al., ACL 2023)
ACL