Imputation of missing values for cochlear implant candidate audiometric data and potential applications.

Precision medicine will inevitably play an integral role in the future of hearing healthcare. These methods are data dependent, and rigorously validated imputation models are a key tool for maximizing datasets. Using the largest CI audiogram dataset to-date, we demonstrate that in a real-world scenario MICE can safely impute missing data for the vast majority (>99%) of audiograms with RMSE well below a clinically significant threshold of 10dB. Evaluation across a range of dataset sizes and sparsity distributions suggests a high degree of generalizability to future applications.