A systematic review of automated International Classification of Diseases coding models using the Medical Information Mart for Intensive Care dataset.

Objective: This study aims to investigate the development of automated International Classification of Diseases (ICD) coding models using the Medical Information Mart for Intensive Care (MIMIC) dataset. This work integrates computer science and clinical perspectives to evaluate progress, identify challenges, and provide insights for future ICD coding automation. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We selected 73 studies between 2014 and 2024 and extracted key information about data preprocessing, knowledge integration, model architectures, evaluation strategies, and explainability. Results: In the reviewed papers, 69.57% (48 papers) focused on utilizing medical knowledge, primarily through knowledge graphs. The methods have evolved from traditional machine learning techniques to more advanced approaches, such as deep learning, knowledge reasoning, information retrieval, and generative models. Since 2019, F1-micro scores have consistently improved: Studies using the MIMIC-III full dataset have shown a 6.4% increase, while those using the MIMIC-III top-50 dataset have experienced a 10.2% improvement. Furthermore, 60.27% (44 papers) implemented strategies to enhance explainability, which included attention visualization and analysis. Conclusion: Automated ICD coding tasks have improved, but ongoing challenges remain. These challenges include a lack of diverse data, inadequate use of medical knowledge, complex algorithms, and insufficient validation by clinical coders. Those issues obstruct the model implementation. Future research should focus on integrating a wider range of multimodal data, enhancing the application of medical knowledge, and improving the explainability of models.