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

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

PLOS ONE, 2023, vol. 18, issue 2, 1-20

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.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281337 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 81337&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0281337

DOI: 10.1371/journal.pone.0281337

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().