Predicting speech discrimination scores from pure-tone thresholds—A machine learning-based approach using data from 12,697 subjects

Abstract

Diagnostic tests for hearing impairment not only determines the presence (or absence) of hearing loss, but also evaluates its degree and type, and provides physicians with essential data for future treatment and rehabilitation. Therefore, accurately measuring hearing loss conditions is very important for proper patient understanding and treatment. In current-day practice, to quantify the level of hearing loss, physicians exploit specialized test scores such as the pure-tone audiometry (PTA) thresholds and speech discrimination scores (SDS) as quantitative metrics in examining a patient’s auditory function. However, given that these metrics can be easily affected by various human factors, which includes intentional (or accidental) patient intervention, there are needs to cross validate the accuracy of each metric. By understanding a “normal” relationship between the SDS and PTA, physicians can reveal the need for re-testing, additional testing in different dimensions, and also potential malingering cases. For this purpose, in this work, we propose a prediction model for estimating the SDS of a patient by using PTA thresholds via a Random Forest-based machine learning approach to overcome the limitations of the conventional statistical (or even manual) methods. For designing and evaluating the Random Forest-based prediction model, we collected a large-scale dataset from 12,697 subjects, and report a SDS level prediction accuracy of 95.05% and 96.64% for the left and right ears, respectively. We also present comparisons with other widely-used machine learning algorithms (e.g., Support Vector Machine, Multi-layer Perceptron) to show the effectiveness of our proposed Random Forest-based approach. Results obtained from this study provides implications and potential feasibility in providing a practically-applicable screening tool for identifying patient-intended malingering in hearing loss-related tests.

Predictors of Speech-in-Noise Understanding in a Population of Occupationally Noise-Exposed Individuals

“…Recently, biomedical research in general has found an interest in this machine learning paradigm, given its interpretability, its nonparametric approach with large use case, and the potential mix between continuous and categorical variables [ 42 ]. Random forests have already been identified as an interesting choice among machine learning algorithms in hearing sciences [ 33 , 34 , 43 ].…”

“…The random forest tool is intuitive, and, more importantly, it has an inherent capacity to produce measures of “variable importance”. Kim et al [ 34 ] highlighted the usefulness of the random forest model, compared to other machine learning techniques, for predicting speech discrimination scores from pure tone audiometry thresholds.…”

Predictors of Speech-in-Noise Understanding in a Population of Occupationally Noise-Exposed Individuals

“…Recently, biomedical research in general has found an interest in this machine-learning paradigm, given its interpretability, its nonparametric approach with large use case, and the potential mix between continuous and categorical variables (REF). Random forests have already been identified as an interesting choice among machine learning algorithms in hearing sciences [11][12][13].…”

Predictors of Speech-in-Noise Understanding in a Population of Occupationally Noise-Exposed Individuals

“…The random forest tool is intuitive, and, more importantly, it has an inherent capacity of producing measures of "variable importance". Kim et al (2021) highlighted the usefulness of the random forest model, compared to other machine learning techniques, for predicting speech discrimination scores from pure tone audiometry thresholds.…”