@inproceedings{salek-faramarzi-etal-2023-context,
    title = "Context-aware Medication Event Extraction from Unstructured Text",
    author = "Salek Faramarzi, Noushin  and
      Patel, Meet  and
      Bandarupally, Sai Harika  and
      Banerjee, Ritwik",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.clinicalnlp-1.11",
    doi = "10.18653/v1/2023.clinicalnlp-1.11",
    pages = "86--95",
    abstract = "Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.",
}

Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.
Salek Faramarzi, Noushin Patel, Meet B arupally, Sai Harika Banerjee, Ritwik
Context-aware Medication Event Extraction from Unstructured Text