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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

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Interpretable Clinical Decision Support System for Audiology Based on Predicted Common Audiological Functional

Mareike Buhl^1,2

¹Medizinische Physik, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany.

Diagnostics (Basel, Switzerland)

|February 25, 2022

Summary

This study developed a data-driven clinical decision support system (CDSS) for audiology using predicted Common Audiological Functional Parameters (CAFPAs). The system achieves interpretable and accurate classification, demonstrating the feasibility of combining expert knowledge with machine learning.

Keywords:

CDSS audiology expert knowledge interpretability machine learning precision medicine

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Area of Science:

Audiology
Clinical Decision Support Systems
Machine Learning in Healthcare

Background:

Common Audiological Functional Parameters (CAFPAs) provide an abstract, measurement-independent representation of audiological knowledge.
Expert-estimated CAFPAs have been validated as an interpretable layer within clinical decision support systems (CDSS).
Previous work established prediction models for CAFPAs based on expert knowledge and audiological databases.

Purpose of the Study:

To explore the feasibility of constructing a CDSS that is both interpretable (like expert knowledge) and data-driven (like machine learning).
To evaluate the performance equivalence between predicted CAFPAs and expert-estimated CAFPAs in audiological classification.
To analyze the contribution of individual CAFPAs to classification performance and explain classification discrepancies.

Main Methods:

Developed prediction models for CAFPAs using expert knowledge and an audiological database.
Integrated predicted CAFPAs into a CDSS framework for audiological classification.
Compared the performance of the data-driven CDSS against expert-estimated CAFPAs and analyzed classification results.

Main Results:

The combination of predicted CAFPAs and statistical classification successfully created an interpretable, data-driven CDSS.
The classification achieved good accuracy, with most categories correctly classified.
Observed classification confusions were explainable by the characteristics of the utilized audiological database.

Conclusions:

A data-driven CDSS using predicted CAFPAs is feasible, offering interpretability comparable to expert systems.
The framework allows for performance enhancement by incorporating additional audiological databases.
This approach bridges the gap between expert knowledge and data-driven machine learning in audiological diagnostics.