@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}.",
}

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}.
Cui, Yang Han, Lifeng Nenadic, Goran
MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries