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
Related News
•EurekAlert
AI Accelerates Radiopharmaceuticals, Boosts Personalized Dosimetry in Cancer
Machine learning is driving advancements in radiopharmaceutical drug discovery and optimizing patient-specific dosimetry for precision cancer therapy.
•EurekAlert
Physicians Overly Trust Erroneous AI, Ignore Contradictory Evidence
Physicians tend to trust incorrect AI advice, even when evidence contradicts it, suggesting risks in clinical decision-making with AI tools.
•EurekAlert
Concerns Raised Over Unverified Datasets in AI Health Prediction Models
A new study finds widely used AI health prediction models are built on datasets with unverifiable origins, raising safety and validity concerns.