Using LLMs for Multilingual Clinical Entity Linking to ICD-10
This addresses the need for consistent and efficient clinical coding in hospitals, benefiting healthcare professionals, but it is incremental as it applies existing LLM methods to a specific domain.
The paper tackled the problem of automatically linking clinical terms to ICD-10 codes in multilingual texts, using a pipeline with dictionaries and GPT-4.1, achieving results such as 0.89 F1 on Spanish and 0.85 F1 on Greek datasets.
The linking of clinical entities is a crucial part of extracting structured information from clinical texts. It is the process of assigning a code from a medical ontology or classification to a phrase in the text. The International Classification of Diseases - 10th revision (ICD-10) is an international standard for classifying diseases for statistical and insurance purposes. Automatically assigning the correct ICD-10 code to terms in discharge summaries will simplify the work of healthcare professionals and ensure consistent coding in hospitals. Our paper proposes an approach for linking clinical terms to ICD-10 codes in different languages using Large Language Models (LLMs). The approach consists of a multistage pipeline that uses clinical dictionaries to match unambiguous terms in the text and then applies in-context learning with GPT-4.1 to predict the ICD-10 code for the terms that do not match the dictionary. Our system shows promising results in predicting ICD-10 codes on different benchmark datasets in Spanish - 0.89 F1 for categories and 0.78 F1 on subcategories on CodiEsp, and Greek - 0.85 F1 on ElCardioCC.