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

ABSTRACT:

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, p

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12712315 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature