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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.

Using smartphones for mobile self-testing could provide easy access to speech intelligibility testing for a large proportion of the world population. The matrix sentence test (MST) is an ideal candidate in this context, as it is a repeatable and accurate speech test currently available in 20 languages. In clinical practice, an experimenter uses professional audiological equipment and supervises the MST, which is infeasible for smartphone-based self-testing. Therefore, it is crucial to investigate the feasibility of self-conducting the MST on a smartphone, given its restricted screen size. We compared the traditional closed matrix user interface, displaying all 50 words of the MST in a 10x5 matrix, and three alternative, newly-developed interfaces (slide, type, wheel) regarding SRT consistency, user preference, and completion time, across younger normal hearing (N=15) and older hearing impaired participants (N=14). The slide interface is most suitable for mobile implementation. While the traditional matrix interface works well for most participants, not every participant could perform the task with this interface. The newly-introduced slide interface could serve as a plausible alternative on the small screen of a smartphone. This might be more attractive for elderly patients that may exhibit more tactile and visual impairments than our test subjects employed here., Comment: 26 pages, 9 Figures, 3 Tables, Supplementary Material