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A database model for medical consultation.

M Anvari1

  • 1Department of Electrical Engineering and Computer Science, University of California, Berkeley 94740.

Journal of the American Society for Information Science. American Society for Information Science
|September 1, 1991
PubMed
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This study introduces a novel database model for handling fuzzy data, enabling queries based on non-crisp attributes like "high" or "normal." This approach facilitates retrieving information, such as diseases with similar symptoms, for advanced applications.

Area of Science:

  • Computer Science
  • Medical Informatics
  • Database Systems

Background:

  • Traditional databases struggle with imprecise or subjective data.
  • Medical diagnoses often involve non-crisp attributes (e.g., 'normal' blood pressure).
  • Existing systems lack robust methods for querying based on fuzzy medical information.

Purpose of the Study:

  • To present a database model capable of managing both crisp and fuzzy data.
  • To enable queries that utilize non-crisp attribute values for data retrieval.
  • To lay the groundwork for advanced medical information systems.

Main Methods:

  • Developed a database model accommodating fuzzy attribute values.
  • Implemented a similarity measure for "indistinguishability" defined by users.

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  • Integrated the database with a rule base for enhanced functionality.

    • Main Results:

    • The database model effectively handles non-crisp attribute values (e.g., 'high', 'normal').
    • Queries can retrieve data based on fuzzy attribute similarity, such as similar disease symptoms.
    • The system supports user-defined similarity measures for flexible data interpretation.

    Conclusions:

    • The proposed database model is suitable for applications requiring non-crisp data references.
    • This system provides a framework for developing sophisticated medical consultation systems.
    • Fuzzy data handling in databases enhances the potential for advanced medical data analysis.