Extraction of Medication and Temporal Relation from Clinical Text using Neural Language Models

Research output: Contribution to conferencePaperpeer-review


Clinical texts, represented in electronic medical records (EMRs), contain rich medical information and are essential for disease prediction, personalised information recommendation, clinical decision support, and medication pattern mining and measurement. Relation extractions between medication mentions and temporal information can further help clinicians better understand the patients' treatment history. To evaluate the performances of deep learning (DL) and large language models (LLMs) in medication extraction and temporal relations classification, we carry out an empirical investigation of MEDTEM project using several advanced learning structures including BiLSTM-CRF and CNN-BiLSTM for a clinical domain named entity recognition (NER), and BERT-CNN for temporal relation extraction (RE), in addition to the exploration of different word embedding techniques. Furthermore, we also designed a set of post-processing roles to generate structured output on medications and the temporal relation. Our experiments show that CNN-BiLSTM slightly wins the BiLSTM-CRF model on the i2b2-2009 clinical NER task yielding 75.67, 77.83, and 78.17 for precision, recall, and F1 scores using Macro Average. BERT-CNN model also produced reasonable evaluation scores 64.48, 67.17, and 65.03 for P/R/F1 using Macro Avg on the temporal relation extraction test set from i2b2-2012 challenges. Code and Tools from MEDTEM will be hosted at https://github.com/HECTA-UoM/MedTem

Original languageEnglish
Number of pages10
Publication statusPublished - 15 Dec 2023
Event2023 IEEE International Conference on Big Data (BigData) - Sorrento, Italy
Duration: 15 Dec 202318 Dec 2023


Conference2023 IEEE International Conference on Big Data (BigData)


  • AI for Healthcare
  • Deep Learning Models
  • Medication and Temporal Relation
  • Natural Language Processing
  • Neural Language Models


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