In-Situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones
| Autor: | Ryan Brummet, Syed Shabih Hasan, Yu-Hsiang Wu, Octav Chipara, Tianbao Yang |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Hearing aid
Engineering medicine.medical_specialty business.industry medicine.medical_treatment Social environment Context (language use) Audiology Outcome (game theory) medicine.anatomical_structure Mobile phone otorhinolaryngologic diseases medicine Candidacy Auditory system Active listening business |
| Zdroj: | ICHI |
| DOI: | 10.1109/ichi.2015.101 |
| Popis: | Audiologists have devised a battery of clinical tests to measure auditory abilities. While these tests can help determine the candidacy of patients for amplification intervention, they do not accurately predict the degree to which a patient would benefit from using a hearing aid (i.e., The hearing aid outcome). Measuring hearing aid outcomes in the real-world is challenging as it not only depends on a patient's auditory abilities, but also on auditory contexts that include characteristics of the listening activity, social context, and acoustic environment. This paper explores the problem of creating predictive models for hearing aid outcomes that incorporate information about auditory abilities, hearing-aid features, and auditory contexts. Our models are built on a dataset collected using a mobile phone application that measures auditory contexts and hearing aid outcomes using Ecological Momentary Assessments. The use of a mobile application allowed us to collect fine-grained hearing aid outcome measures in different auditory contexts. The dataset includes 5671 surveys from 34 patients collected over two years. Our analysis focuses on identifying the features necessary for predicting hearing aid outcomes in different clinical scenarios. Most importantly, we show that models that only included measures of auditory ability as features are cannot predict the hearing aid outcome of a patient with accuracy better than chance. Incorporating information about auditory contexts increases the prediction accuracy to 68%. More excitingly, accuracies as high as 90% can be achieved when a small amount of training data is collected from a patient in-situ. These results suggest that audiologists could prescribe a mobile phone application at the time of dispensing the hearing aid in order to accurately predict a patient's likelihood of becoming a successful and satisfied hearing aid user. |
| Databáze: | OpenAIRE |
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