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In-situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones

In-situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones. Syed Shabih Hasan , Ryan Brummet , Octav Chipara , Tianbao Yang Department of Computer Science. Yu-Hsiang Wu Department of Computer Sciences and Disorders. ICHI 2015. Introduction.

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In-situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones

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  1. In-situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones Syed ShabihHasan, Ryan Brummet, OctavChipara, Tianbao Yang Department of Computer Science Yu-Hsiang Wu Department of Computer Sciences and Disorders ICHI 2015

  2. Introduction Hearing Loss in the United States • 11.3% of Americans are affected by hearing loss • Left untreated, hearing loss can lead to depression, anxiety, and isolation • Hearing Aids (HAs) are the primary treatment for sensorineuralhearing loss • As many as 50% of HA users do not use their HAs • Of those that do, 40% are unsatisfied. • HA satisfaction has been shown to be correlated with effectiveness and use Need to understand why users are dissatisfied

  3. Introduction HA Treatment and Assessment Recall Bias Fit and Adjust HA Evaluate Patient No Audio Context Information Patient Evaluates HA

  4. AudioSense AudioSense • Alternative to Lab Simulation, Surveys, and Diary Methods • Lab Simulation may not accurately reproduce the real world • Paper based methods do not scale • AudioSense scales well and collects surveys using EMA principles Context Question HA Question Home Screen S.S.Hasan, F. Lai, O. Chipara, Y-H. Wu AudioSense : Enabling real-time evaluation of hearing-aid technology in-situ CBMS 2013

  5. AudioSense Auditory Context and Measurement How satisfied were you with your HA? Acoustic Environment How noisy was it? Social Interaction Could you see the talker’s face? Were you inside or outside? Activity How much effort was required to listen?

  6. Results Remainder of the Talk • Can we predict if a new HA user will be successful with data collected via AuioSense? • Clinicians can take additional steps in the case that an user is predicted to be unsuccessful • If a current HA user changes his/her HA, can we predict if they will be successful? • Would allow clinicians to make more informed HA selections for patients

  7. Results Study Design • 34 patients were evaluated using 4 different HAs • Two different HAs with two program settings • 5671 surveys collected in total • Patients wore each HA one month before evaluating the HA for one week using AudioSense • We refer to each HA and patient combination as a condition for a total of 136 conditions • We additionally collect QuickSIN and PTA information (Lab Scores) during the enrollment process • Largest study to use computerized EMA within Audiology

  8. Results Combined Score Spearman’s Correlation ƒLE(SP) Highly correlated with aggregated ratings Speech Perception (SP) AVG ƒLE Listening Effort (LE) HA Satisfaction (ST) Combined Score (CB) Sound Localization (LCL) Activity Participation (AP)

  9. Results Models and Features • Identify the best features for predicting success • Does model choice make a difference? Linear Successful Mean Predicted Combined Score Linear Mixed Features Models HA Score HA Outcome Unsuccessful Lab Scores Regression Trees Patient ID HA Audio Context Only Lab Scores are continuous Hold one out validation Predicted samples averaged Average, mean Condition score discretizes

  10. Results Novel Patient 68% with only Lab Scores, Audio Context and HA Lab Scores roughly equivalent to random chance Including Audio context improves accuracy over 10% d:= Lab Scores x:= Audio Context Regression Trees Linear Linear Mixed

  11. Results Novel Patient With Some Data Including 5% equates to ~2 samples Equivalent to previous graph Possible to predict HA success more efficiently than the current clinical approach with over 90% accuracy d:= Lab Scores x:= Audio Context Regression Trees Linear Linear Mixed

  12. Results Novel Condition With No HA Information Novel Condition maximum accuracy ~88% Lab Scores better than Novel Patient Audio Context improves accuracy over 15% Difference from Novel Patient may be due to inclusion of some patient samples d:= Lab Scores x:= Audio Context Regression Trees Linear Linear Mixed

  13. Results Novel Condition With HA Information Patients are significantly different HAs are very similar Virtually no difference from Novel Condition with no HA information d:= Lab Scores x:= Audio Context Regression Trees Linear Linear Mixed

  14. Conclusion Conclusion / Future Work • Auditory Context is an essential feature to predict HA success • We can predict whether a Novel Patient will be a successful HA user with over 90% accuracy if we collect some information from them • A system like AudioSense could be used to more efficiently determine the success of a HA intervention • We can predict if patient will be a successful HA user if they switch their HA with 88% accuracy • Can we use objective information to achieve similar results? • Can we selectively sample to capture contexts in which users have trouble hearing?

  15. Conclusion Acknowledgements • Audiology collaborator: Elizabeth Stangl • Roy J. Carver Charitable Trust ( 14-43555) • National Institute on Disability, Independent Living, and Rehabilitation Research (90RE5020-01-00)

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