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This study introduces machine learning frameworks for automatic medical consultations, including dialogue understanding and task-oriented interaction. A new dataset and benchmark results are provided to advance research in this area.

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Natural Language Processing

Background:

  • Growing interest in machine learning (ML) for improving automatic medical consultations.
  • Need for enhanced patient experience in digital health interactions.
  • Limited availability of large-scale, annotated medical dialogue datasets.

Purpose of the Study:

  • To propose two novel frameworks for automatic medical consultation: doctor-patient dialogue understanding and task-oriented interaction.
  • To introduce a new, large-scale medical dialogue dataset with multi-level, fine-grained annotations.
  • To establish five independent tasks to evaluate the proposed frameworks and dataset.

Main Methods:

  • Development of two distinct frameworks for medical dialogue systems.
  • Creation of a comprehensive medical dialogue dataset with detailed annotations.
  • Implementation of five key tasks: named entity recognition, dialogue act classification, symptom label inference, medical report generation, and diagnosis-oriented dialogue policy.

Main Results:

  • A new, large medical dialogue dataset with multi-level fine-grained annotations has been created.
  • Five independent tasks were established, including NER, dialogue act classification, and diagnosis-oriented dialogue policy.
  • Benchmark results were reported for each task, demonstrating dataset usability and setting a baseline.

Conclusions:

  • The proposed frameworks and dataset provide a valuable resource for advancing automatic medical consultation systems.
  • The benchmark results establish a foundation for future research in medical dialogue understanding and task-oriented interaction.
  • The availability of code and data facilitates further development and application of these technologies.