Evidence-based hearing aid algorithms

Hearing aid (HA) algorithms contain a large number of tuning parameters that need to be optimized with respect to the expected patient satisfaction. Here we report on a new fitting-engineering approach where patient measurements (audiogram, listening tests etc.) are transferred without loss of information to optimal values for HA tuning parameters. Our approach is rooted in Bayesian decision theory and takes properly account of inconsistencies in the measured patient data. The presented approach is envisioned to assist the experienced HA dispenser in the challenging task of tting a HA algorithm to a specific patient in a limited time.