Abstract
Although there exist nearly 35 × 106 hearing impaired people in the U.S., only an estimated 25% use hearing aids (HA), while others elect not to use prescribed HAs. Lack of HA acceptance can be attributed to several factors including (i) performance variability in diverse environments, (ii) time-to-convergence for best HA operating configuration, (iii) unrealistic expectations, and (iv) cost/insurance. This study examines a nationwide dataset of pure-tone audiograms and HA fitting configurations. An overview of data characteristics is presented, followed by use of machine learning clustering to suggest ways of obtaining effective starting configurations, thereby reducing time-to-convergence to improve HA retention.
Highlights
This study examines a nationwide dataset of pure-tone audiograms and hearing aids (HA) fitting configurations
Of those HA owners, many choose not to use their HAs at a rate of 4.2%–24% reported in various international surveys
It is important to note that the data from both the audiogram database and the HA fitting database came from a vast number of individual sites located throughout the U.S Data were collected by individuals of varied levels of training and experience, and so a level of data variability is involved in the data collection
Summary
Studies suggest that mild to severe hearing impairment causes adverse effects on quality of life and that these effects are largely reversible through the use of HAs. a 2008 survey found that of the 34.25 Â 106 people in the U.S who reported experiencing hearing difficulty, only 24.6% owned HAs. Of those HA owners, many choose not to use their HAs at a rate of 4.2%–24% reported in various international surveys.. A 2008 survey found that of the 34.25 Â 106 people in the U.S who reported experiencing hearing difficulty, only 24.6% owned HAs.. Among HA owners who do not use them, some of the most prevalent reasons cited across studies are reported low HA value and issues with fit or comfort of the HA.. Low HA value is associated with several factors, including issues with noise, difficulty in human adjustment to HA use, and the need for repeated clinical visits for finetuning of HA system settings to achieve the best programming for an individual. This study, explores a massive nationwide corpus to research potential ML methods to establish better starting configurations for amplification products.
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