Mimic-IV-ICD: A New Benchmark for Multi-Label Medical Code Classification

Anonymous

Abstract:

Clinical notes are assigned ICD codes -- sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding. However, there is a lack of widely accepted benchmarks for automated ICD coding models based on large-scale public EHR data. This paper proposes a public benchmark suite for ICD-10 coding using a large EHR dataset derived from MIMIC-IV, the most recent public EHR dataset. We standardize data preprocessing and establish a comprehensive ICD coding benchmark dataset.
Some state-of-the-art models for ICD prediction are thoroughly investigated, and we provide benchmark results as useful references for future studies. Our open-source code offers easy access to data processing steps, benchmark creation, and experiment replication for those with MIMIC-IV access, providing insights, guidance, and protocols to efficiently develop ICD coding models.

Paper Type: long

Research Area: NLP Applications

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Consent To Share Submission Details: On behalf of all authors, we agree to the terms above to share our submission details.