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

Author

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  • Cole Pavelchek
  • Andrew P Michelson
  • Amit Walia
  • Amanda Ortmann
  • Jacques Herzog
  • Craig A Buchman
  • Matthew A Shew

Abstract

Objective: Assess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data. Methods: 7,451 audiograms from patients undergoing CI candidacy evaluation were pooled from 32 institutions with complete case analysis yielding 1,304 audiograms. Imputation model performance was assessed with nested cross-validation on randomly generated sparse datasets with various amounts of missing data, distributions of sparsity, and dataset sizes. A threshold for safe imputation was defined as root mean square error (RMSE) 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.

Suggested Citation

  • Cole Pavelchek & Andrew P Michelson & Amit Walia & Amanda Ortmann & Jacques Herzog & Craig A Buchman & Matthew A Shew, 2023. "Imputation of missing values for cochlear implant candidate audiometric data and potential applications," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-20, February.
    • Handle: RePEc:plo:pone00:0281337
    DOI: 10.1371/journal.pone.0281337
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