MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries
Yang Cui, Lifeng Han, Goran Nenadić · 2023
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 Pretrained Transformers (GPTs) models and finetuning on pre-trained language models (PLMs) such as BERT were used 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%.