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Common Audiological Functional Parameters (CAFPAs) for single patient cases: deriving statistical models from an

Mareike Buhl1,2, Anna Warzybok1,2, Marc René Schädler1,2

  • 1Medizinische Physik, Universität Oldenburg, Oldenburg, Germany.

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Summary

Machine learning can support hearing healthcare professionals by analyzing patient data. This study collected expert labels for audiological findings using Common Audiological Functional Parameters (CAFPAs) to build statistical models for better data integration and classification.

Keywords:
Medical audiologyTele-audiology/tele-healthmachine learningprecision diagnostics

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Area of Science:

  • Audiology
  • Machine Learning
  • Data Science

Background:

  • Leveraging statistical knowledge from patient data can aid otolaryngologists and hearing healthcare professionals.
  • Accessibility of this data is crucial for practical application.
  • Common Audiological Functional Parameters (CAFPAs) offer an abstract representation for integrating diverse audiological measurement databases.

Purpose of the Study:

  • To collect expert labels for a sample audiological database.
  • To develop statistical models from this expert-labeled dataset.
  • To establish a foundation for machine learning applications in audiology.

Main Methods:

  • An expert survey was conducted with twelve highly experienced audiological experts.
  • CAFPAs, audiological findings, and treatment recommendations were collected for 287 patient cases.
  • Probability density functions were derived from expert labels to create training distributions.

Main Results:

  • The labeled dataset demonstrated realistic variability suitable for estimating training distributions.
  • Appropriate statistical distribution functions were identified.
  • Derived training distributions were compared for various audiological inquiries.

Conclusions:

  • The methodology of expert surveys, data categorization, and training distribution determination is effective.
  • This approach can be extended to other datasets for integration via CAFPAs.
  • The integrated data can be utilized for machine learning classification tasks in audiology.