Secondary Use of Clinical Problem List Descriptions for Bi-Encoder Based ICD-10 Classification

• Published in: AMIA ... Annual Symposium proceedings Vol. 2024; p. 620
• Main Authors: Kreuzthaler, Markus, Pfeifer, Bastian, Schulz, Stefan
• Format: Journal Article
• Language: English
• Published: United States 2024
• Subjects: Clinical Coding; Humans; International Classification of Diseases; Machine Learning; Natural Language Processing; Unified Medical Language System
• ISSN: 1942-597X; 1559-4076

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.