Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Kreuzthaler, M; Pfeifer, B; Schulz, S.
Secondary Use of Clinical Problem List Descriptions for Bi-Encoder Based ICD-10 Classification.
AMIA Annu Symp Proc. 2024; 2024:620-627
[OPEN ACCESS]
PubMed
- Führende Autor*innen der Med Uni Graz
- Kreuzthaler Markus Eduard
- Co-Autor*innen der Med Uni Graz
- Pfeifer Bastian
- Schulz Stefan
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- Abstract:
- Annotated language resources are essential for supervised machine learning methods. In the clinical domain, such data sets can boost use-case specific natural language processing services. In this work, we have analyzed a clinical problem list table consisting of millions of ICD-10 codes assigned to short problem list descriptions in German. We have investigated whether the given data forms a valuable resource within a secondary use case scenario for coding support. Our proposed methodology exploits an embedding-based k-NN classifier, which was evaluated based on its coding performance, leveraging the multilingual BERT based language model SapBERT-UMLS in comparison with medBERT.de, which is specifically tailored to medical and clinical language resources in German. Our approach reached a weighted F1-measure of 0.87 using SapBERT-UMLS and an F1-measure of 0.86 for medBERT.de. The approach revealed promising coding results when reusing annotated language resources out of clinical routine documentation.
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