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Mamba-enhanced disease semantic knowledge graph for interpretable automatic ICD coding.

Pengli Lu1, Chao Dong1, Jingjin Xue1

  • 1School of Computer and Artificial Intelligence, Lanzhou University of Technology, Lanzhou 730050, Gansu, China.

Journal of Biomedical Informatics
|December 24, 2025
PubMed
Summary

A new AI framework, MKHCNet, enhances automatic International Classification of Diseases (ICD) coding by integrating knowledge graphs and advanced deep learning. This improves accuracy and interpretability in electronic health records.

Keywords:
Automatic ICD codingDisease semantic knowledge graphKolmogorov–Arnold NetworksMambaMulti–label text classification

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Computational Linguistics

Background:

  • Automatic ICD coding leverages AI to streamline disease classification from electronic health records (EHRs).
  • Current deep learning models face challenges with semantic inconsistency, label ambiguity, and limited interpretability in practical EHR data.
  • Existing methods struggle to capture complex relationships and provide transparent decision-making processes.

Purpose of the Study:

  • To introduce MKHCNet, a novel framework designed to overcome limitations in automatic ICD coding.
  • To enhance coding performance through integrated knowledge representation, long-range dependency modeling, and contrastive normalization.
  • To improve the interpretability and clinical applicability of AI-driven ICD coding systems.

Main Methods:

  • Developed MKHCNet, incorporating a disease semantic knowledge graph for enriched label representation.
  • Utilized the Mamba network for cross-domain dependency modeling and ContraNorm for improved label separability.
  • Implemented Hierarchical Position Label Attention (HPLA) for fine-grained interpretability and FastKAN with RBF for classification.

Main Results:

  • MKHCNet demonstrated superior performance on benchmark datasets (MIMIC-FULL, MIMIC-50), improving MaAUC by 2.1% and P@8 by 0.3% on MIMIC-FULL.
  • The model effectively captured complex nonlinear relationships within EHR data.
  • Case studies confirmed the model's ability to identify complex semantic cues and offer strong clinical interpretability.

Conclusions:

  • MKHCNet represents a significant advancement in automatic ICD coding, addressing key challenges in accuracy and interpretability.
  • The integration of knowledge graphs and novel deep learning modules enhances the model's ability to process complex clinical data.
  • The proposed framework offers a promising direction for developing more reliable and transparent AI tools in healthcare.