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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision

Pei-Fu Chen1,2, Tai-Liang He3, Sheng-Che Lin3

  • 1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

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Summary
This summary is machine-generated.

Federated learning enabled training of an International Classification of Diseases, 10th Revision (ICD-10) classification model using multicenter clinical text while preserving data privacy. The federated learning approach demonstrated superior performance compared to models trained on local data alone.

Keywords:
International Classification of Diseasesfederated learningmachine learningmultilabel text classificationnatural language processing

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning
  • Medical Informatics
  • Computational Linguistics

Background:

  • Automatic coding of clinical documents using International Classification of Diseases, 10th Revision (ICD-10) is crucial for statistical analysis and reimbursement.
  • Transformer-based NLP models, particularly those with attention mechanisms, have advanced clinical text analysis.
  • Multicenter training enhances model performance and generalizability, but necessitates robust data privacy measures.

Purpose of the Study:

  • To develop and evaluate a classification model for ICD-10 multilabel classification using federated learning.
  • To train a model on multicenter clinical text data while ensuring patient data privacy.
  • To compare the performance of a federated learning model against models trained on local hospital data.

Main Methods:

  • Discharge notes from three medical centers were utilized for training and testing.
  • PubMedBERT was selected for word embeddings, with non-alphanumeric characters retained to optimize performance.
  • A label attention mechanism was incorporated for model interpretability; models were trained locally and via federated learning for comparison using micro F1-score.

Main Results:

  • The chosen model achieved a micro F1-score of 0.6142 via federated learning, outperforming models trained on individual hospital data (0.4472-0.5353).
  • Retaining non-alphanumeric characters improved model performance (F1 score 0.8120) compared to their removal (F1 score 0.7875).
  • The label attention mechanism provided explainable predictions by highlighting key input words.

Conclusions:

  • Federated learning effectively trained an ICD-10 classification model on multicenter clinical text, successfully protecting data privacy.
  • The federated learning model exhibited superior performance compared to models trained solely on local data.
  • The study highlights the potential of federated learning for developing robust and privacy-preserving clinical NLP applications.