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A history-taking system that uses continuous speech recognition.

K Johnson1, A Poon, S Shiffman

  • 1Section on Medical Informatics, Stanford University School of Medicine, CA 94305-5479.

Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1992
PubMed
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Q-MED, an automated medical history system, uses speech recognition for patient symptom entry. Its natural language parser achieved 87% semantic accuracy, improving diagnostic efficiency.

Area of Science:

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

Background:

  • Automated systems can streamline patient data collection.
  • Speech recognition offers a natural interface for medical history taking.
  • Challenges exist in accurately interpreting patient utterances in a dialog system.

Purpose of the Study:

  • To evaluate the performance of the Q-MED automated history-taking system.
  • To assess the semantic accuracy of the natural language parser within Q-MED.
  • To determine the system's effectiveness in capturing patient-reported symptoms.

Main Methods:

  • Q-MED utilizes speaker-independent continuous speech recognition for patient interaction.
  • The system employs a dialog-based approach for symptom elicitation.

Related Experiment Videos

  • Error-recovery mechanisms are integrated to handle misrecognitions and parsing errors.

    • Main Results:

    • The natural language parser demonstrated an overall semantic accuracy of 87 percent.
    • Q-MED effectively captures volunteered findings and clarifies unparsed information through targeted questions.
    • The system facilitates automated patient history taking.

    Conclusions:

    • Q-MED's automated history-taking system shows significant potential in clinical settings.
    • The high semantic accuracy of its natural language parser supports efficient symptom capture.
    • Q-MED's dialog capabilities enhance the completeness of patient-reported data.