A flexible data-driven audiological patient stratification method for deriving auditory profiles

Abstract

For characterizing the complexity of hearing deficits, it is important to consider di erent aspects of auditory functioning in addition to the audiogram. For this purpose, extensive test batteries have been developed aiming to cover all relevant aspects as defined by experts or model assumptions. However, as the assessment time of physicians is limited, such test batteries are often not used in clinical practice. Instead, fewer measures are used, which vary across clinics. This study aimed at proposing a flexible data-driven approach for characterizing distinct patient groups (patient stratification into auditory profiles) based on one prototypical database (N =) containing audiogram data, loudness scaling, speech tests, and anamnesis questions. To further maintain the applicability of the auditory profiles in clinical routine, we built random forest classification models based on a reduced set of audiological measures which are often available in clinics. Di erent parameterizations regarding binarization strategy, cross-validation procedure, and evaluation metric were compared to determine the optimum classification model. Our data-driven approach, involving model-based clustering, resulted in a set of patient groups, which serve as auditory profiles. The auditory profiles separate patients within certain ranges across audiological measures and are audiologically plausible. Both a normal hearing profile and profiles with varying extents of hearing impairments are defined. Further, a random forest classification model with a combination of a one-vs.-all and one-vs.-one binarization strategy, -fold cross-validation, and the kappa evaluation metric was determined as the optimal model. With the selected model, patients can be classified into of the auditory profiles with adequate precision (mean across profiles = . ) and sensitivity (mean across profiles = . ). The proposed approach, consequently, allows generating of audiologically plausible and interpretable, data-driven clinical auditory profiles, providing an e cient way of characterizing hearing deficits, while maintaining clinical applicability. The method should by design be applicable to all audiological data sets from clinics or research, and in addition be flexible to summarize Frontiers in Neurology frontiersin.org Saak et al. . /fneur. .information across databases by means of profiles, as well as to expand the approach toward aided measurements, fitting parameters, and further information from databases.

Spatio-temporal Integration of Speech Reflections in Hearing-Impaired Listeners

“…From a clinical perspective, it would be interesting to compare the performance in conditions like the ones tested here with basic psychoacoustic performance measures, measures of cognitive function, aspects such as hearing-aid satisfaction in aided real-life listening conditions, or more generally to include such conditions in auditory profiling (see, e.g., Sanchez-Lopez et al, 2020 ; Iliadou et al, 2022 ; Saak et al, 2022 ). It is possible that a systematic manipulation of spatio-temporal integration demands in laboratory settings can help to magnify interindividual performance differences and hence to identify listeners with specific difficulties in their everyday life, thus supporting the diagnostics and fitting processes of hearing devices.…”

Development and verification of non-supervised smartphone-based methods for assessing pure-tone thresholds and loudness perception

“…The task of the CLS requires participants to select the descriptors from an 11-point scale, e.g., ‘too loud’, ‘medium’, ‘soft’, etc., based on their loudness perception. The CLS is a supra- threshold listening test that has been included in the ‘auditory profile’ (i.e., a comprehensive and well-specified set of audiological test procedures described in Van Esch et al, 2013) and has also recently been proposed for usage in machine-learning-supported auditory profiles by Saak et al (2022).…”

Assessing Clustering Replicability in Sensorineural Hearing Loss: Insights from the UK's Largest Audiogram Cohort

“…[14][15][16] One emerging approach to this problem has been to harness unsupervised machine learning methods to understand audiogram heterogeneity in a small number of studies. [16][17][18][19][20] Unsupervised machine learning methods identify high sample densities in datasets without imposing any prior knowledge or classi cation systems. This approach is particularly valuable given recent challenges to the traditional understanding of audiogrampathology associations.…”