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Summary

This study introduces a real-time, low-latency named entity recognition (NER) system for cross-lingual speech-to-text in healthcare. It enhances medical data extraction from cancer therapy and traditional Chinese medicine records using deep learning.

Keywords:
deep learningmachine translationneural processingreal-time NERtextual electronic clinical recordstherapies in cancer

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence in Healthcare
  • Medical Informatics

Background:

  • Named Entity Recognition (NER) is crucial for extracting information from unstructured text.
  • Clinical applications of NER face challenges due to complex medical terminology and accuracy requirements.
  • Growing volume of unstructured medical data necessitates efficient information extraction.

Purpose of the Study:

  • To develop a real-time, low-latency NER system for cross-lingual speech-to-text applications.
  • To focus on cancer therapy and traditional Chinese medicine (TCM) clinical records.
  • To extract structured information from multilingual spoken medical content.

Main Methods:

  • Exploration of deep learning (DL) architectures optimized for low-latency neural processing.
  • Evaluation of DL-based methods for NER in multimodal environments.
  • Proposal of a semi-supervised approach combining TCM corpora and biomedical resources.

Main Results:

  • Demonstrated feasibility of real-time, low-latency NER for clinical speech-to-text.
  • Improved recognition accuracy through a semi-supervised approach for TCM data.
  • Provided insights into building practical clinical information extraction systems.

Conclusions:

  • Real-time NER systems are vital for efficient healthcare data management.
  • Deep learning and semi-supervised methods enhance accuracy in specialized medical domains like TCM.
  • The developed system supports clinical decision-making and information retrieval.