Researchers developed a deep learning model achieving high accuracy in automatic ICD-11 coding of Chinese electronic medical records.
Key Details
- 1The new LA-TextCNN-BiLSTM model leverages MC-BERT and a label attention mechanism.
- 2Evaluated on real-world EMRs, it achieved 83.86% accuracy, 75.82% macro-F1, and 82.83% micro-F1 scores.
- 3Label attention helps the model focus on diagnostically relevant text, improving performance over traditional methods.
- 4Automated ICD-11 coding can streamline hospital workflows and reduce manual coding errors, especially in complex Chinese clinical language settings.
- 5Research was conducted by Peking Union Medical College Hospital & WHO Family of International Classification Collaborating Center.
Why It Matters
Automating ICD-11 coding with advanced NLP can greatly reduce the labor and error burden in extracting structured diagnostic data, which has wide-reaching implications for radiology AI, clinical research, and healthcare efficiency—particularly in linguistically complex environments like Chinese EMRs.
Source
EurekAlert
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