  Audiological datasets contain valuable knowledge about hearing loss in
patients, which can be uncovered using data-driven, federated learning
techniques. Our previous approach summarized patient information from one
audiological dataset into distinct Auditory Profiles (APs). To cover the
complete audiological patient population, however, patient patterns must be
analyzed across multiple, separated datasets, and finally, be integrated into a
combined set of APs. This study aimed at extending the existing profile
generation pipeline with an AP merging step, enabling the combination of APs
from different datasets based on their similarity across audiological measures.
The 13 previously generated APs (NA=595) were merged with 31 newly generated
APs from a second dataset (NB=1272) using a similarity score derived from the
overlapping densities of common features across the two datasets. To ensure
clinical applicability, random forest models were created for various
scenarios, encompassing different combinations of audiological measures. A new
set with 13 combined APs is proposed, providing well-separable profiles, which
still capture detailed patient information from various test outcome
combinations. The classification performance across these profiles is
satisfactory. The best performance was achieved using a combination of loudness
scaling, audiogram and speech test information, while single measures performed
worst. The enhanced profile generation pipeline demonstrates the feasibility of
combining APs across datasets, which should generalize to all datasets and
could lead to an interpretable population-based profile set in the future. The
classification models maintain clinical applicability. Hence, even if only
smartphone-based measures are available, a given patient can be classified into
an appropriate AP.
