Czech medical coding assistant based on transformer networks

• Published in: Computers in biology and medicine Vol. 178; p. 108672
• Main Authors: Lenc, Ladislav, Martínek, Jiří, Baloun, Josef, Přibáň, Pavel, Prantl, Martin, Taylor, Stephen Eugene, Král, Pavel, Kyliš, Jiří
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2024; Elsevier Limited
• Subjects: Classification; Clinical Coding; Coders; Coding; Czech Republic; Diagnosis; Diagnosis coding; Electronic Health Records; Error correction; Human factors; Humans; ICD; International Classification of Diseases; Medical; Medical coding; Taxonomy; Text classification; Transformers; Unstructured data; Czech Republic; Medical; ICD; Coding; Diagnosis coding; Text classification
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108672

The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes. The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis; and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability. The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based “Four-headed” model incorporating four distinct classification heads. This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller. •Design and implementation of an Automatic Medical Coding Assistant for prediction of diagnosis codes (ICD) in the Czech language.•Proposal and evaluation of several transformer-based models for ICD coding with a relatively few training parameters.•Obtaining new state-of-the-art results in ICD coding task on Czech data.