Abstract
Clinical narratives are a rich resource of patient-based information, where the automatic extraction of specific types of entities is still challenging due to various idiosyncrasies of non-standardized routine documentation. Much of the literature in clinical natural language processing (NLP) has been focused on English language. In this work, we focus on de-identified German-language clinical narratives. Predominant supervised approaches in the area of NLP, specifically named entity recognition (NER), need an expressive set of annotations for robust sequence modeling via a machine learning approach. This work investigates to what extent three main model types, conditional random fields (CRFs), bidirectional long short-term memory (BiLSTM) and Transformer models (BERT), perform on a limited set of annotations for medication information extraction in a specific clinical domain. The top performing feature, an optimized CRF model exploiting embedding representations out of a natively pre-trained language model using fastText, performed best with an overall F1 score 0.91. A multilingual BERT model incorporating German language resources reached an overall F1 score of 0.88, followed by the BiLSTM approach with an F1 score of 0.81. BERT based end-to-end systems nearly reach the performance of an optimized CRF approach. The results highlight the importance of pre-trained clinical language models available in languages other than English for fast, high performing, problem domain adaption.
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Acknowledgements
This work was part of the Digital Biomarkers for Precision Medicine (DBM4PM) project within the K1 COMET Competence Center CBmed, funded by the Federal Ministry of Transport, Innovation and Technology (BMVIT); the Federal Ministry of Science, Research and Economy (BMWFW); Land Steiermark (Department 12, Business and Innovation); the Styrian Business Promotion Agency (SFG); and the Vienna Business Agency. The COMET program is executed by the FFG. The work was mandated via the IRB (Ethics Committee) votes 32-194 ex 19/20 and 30-496 ex 17/18 from the Medical University of Graz. We acknowledge the financial support from Roche Diagnostics.
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Sharma, V., Thalhammer, A., Kugic, A., Schulz, S., Kreuzthaler, M. (2024). Sequence-Model-Based Medication Extraction from Clinical Narratives in German. In: Finkelstein, J., Moskovitch, R., Parimbelli, E. (eds) Artificial Intelligence in Medicine. AIME 2024. Lecture Notes in Computer Science(), vol 14844. Springer, Cham. https://doi.org/10.1007/978-3-031-66538-7_33
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