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.- Anthology ID:
- 2023.acl-srw.27
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Vishakh Padmakumar, Gisela Vallejo, Yao Fu
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 160–183
- Language:
- URL:
- https://aclanthology.org/2023.acl-srw.27
- DOI:
- 10.18653/v1/2023.acl-srw.27
- Cite (ACL):
- Yang Cui, Lifeng Han, and Goran Nenadic. 2023. MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 160–183, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries (Cui et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.acl-srw.27.pdf