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ICD code mapping model based on clinical text tree structure.

Jingjin Xue1, Pengli Lu1

  • 1School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.

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The TRIC model enhances automatic International Classification of Diseases (ICD) coding for clinical records using deep learning. This Transformer and Tree-lstm for ICD Coding (TRIC) model improves accuracy and efficiency in medical record classification.

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Constituency treeICD auto-codingMulti-label text classificationSemantic similarityTree-lstm

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Natural Language Processing

Background:

  • Deep learning methods have improved electronic medical record (EMR) coding efficiency, replacing manual processes.
  • Challenges remain in representing clinical text semantics and incorporating structural record features.
  • Existing models struggle with the complexity of unstructured clinical data for accurate ICD coding.

Purpose of the Study:

  • To propose the TRansformer and TRee-lstm for ICD Coding (TRIC) model for enhanced automatic ICD encoding.
  • To address limitations in semantic representation and structural feature extraction in clinical records.
  • To improve the accuracy and efficiency of mapping unstructured clinical text to ICD codes.

Main Methods:

  • The TRIC model integrates constituency tree and transformer-based models for feature extraction.
  • Tree-lstm is employed to enrich clinical record features.
  • BioBERT is utilized for highlighting key ICD coding elements and improving matching.
  • A fully connected neural network classifier performs the many-to-many mapping.

Main Results:

  • The TRIC model achieved superior performance on the MIMIC-III dataset compared to 12 benchmark models.
  • Key performance metrics included MiF (0.586), MaF (0.109), MiAUC (0.989), MaAUC (0.937), and P@8 (0.758).
  • These results demonstrate significant improvements in automatic ICD coding quality.

Conclusions:

  • The TRIC model effectively addresses the challenges of semantic representation and structural characteristics in clinical records.
  • It offers a robust solution for accurate and efficient automatic ICD coding of unstructured EMR data.
  • The study validates the TRIC model's capability to enhance the quality of ICD automatic coding.