Abstract
In this paper, we introduce a method for extracting detailed information from raw medical notes that could help medical providers more easily understand a patient’s medication history and make more informed medical decisions. Our system uses NLP techniques for finding the names of medications and details about the changes to their disposition in unstructured clinical notes.
The system was created to extract data from the Contextualized Medication Event Dataset in three subtasks. Our system utilizes a solution based on a large language model enriched with adversarial examples for the medication extraction and event classification tasks. To extract more detailed contextual information about the medication changes, we were motivated by aspect-based sentiment analysis and used the local context focus mechanism to highlight the relevant parts of the context and extended it with information from dependency syntax.
Both adversarial learning and the syntax-enhanced local focus mechanism improved the results of our system.
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Acknowledgements
This research has been supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory.
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Szántó, Z., Bánáti, B., Zombori, T. (2023). Enhancing Medication Event Classification with Syntax Parsing and Adversarial Learning. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_11
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