Imputation of missing values for cochlear implant candidate audiometric data and potential applications
Copyright: © 2023 Pavelchek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
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) <10dB. Models included univariate imputation, interpolation, multiple imputation by chained equations (MICE), k-nearest neighbors, gradient boosted trees, and neural networks.
RESULTS: Greater quantities of missing data were associated with worse performance. Sparsity in audiometric data is not uniformly distributed, as inter-octave frequencies are less commonly tested. With 3-8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB- 5.8 dB, non-overlapping 99% confidence intervals). With a real-world sparsity distribution, models were able to safely impute up to 6 missing datapoints in an 11-frequency audiogram. MICE consistently outperformed other models across all metrics and sparsity distributions (p < 0.01, Wilcoxon rank sum test). With sparsity capped at 6 missing features per audiogram but otherwise equivalent to the raw dataset, MICE imputed with RMSE of 7.83 dB [95% CI 7.81-7.86]. Imputing up to 6 missing features captures 99.3% of the audiograms in our dataset, allowing for a 5.7-fold increase in dataset size (1,304 to 7,399 audiograms) as compared with complete case analysis.
CONCLUSION: 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
PloS one - 18(2023), 2 vom: 28., Seite e0281337 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Pavelchek, Cole [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 08.02.2023 Date Revised 06.04.2023 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0281337 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM352556463 |
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| 245 | 1 | 0 | |a Imputation of missing values for cochlear implant candidate audiometric data and potential applications |
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| 520 | |a Copyright: © 2023 Pavelchek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | ||
| 520 | |a OBJECTIVE: Assess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data | ||
| 520 | |a 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) <10dB. Models included univariate imputation, interpolation, multiple imputation by chained equations (MICE), k-nearest neighbors, gradient boosted trees, and neural networks | ||
| 520 | |a RESULTS: Greater quantities of missing data were associated with worse performance. Sparsity in audiometric data is not uniformly distributed, as inter-octave frequencies are less commonly tested. With 3-8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB- 5.8 dB, non-overlapping 99% confidence intervals). With a real-world sparsity distribution, models were able to safely impute up to 6 missing datapoints in an 11-frequency audiogram. MICE consistently outperformed other models across all metrics and sparsity distributions (p < 0.01, Wilcoxon rank sum test). With sparsity capped at 6 missing features per audiogram but otherwise equivalent to the raw dataset, MICE imputed with RMSE of 7.83 dB [95% CI 7.81-7.86]. Imputing up to 6 missing features captures 99.3% of the audiograms in our dataset, allowing for a 5.7-fold increase in dataset size (1,304 to 7,399 audiograms) as compared with complete case analysis | ||
| 520 | |a CONCLUSION: 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 | ||
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| 700 | 1 | |a Walia, Amit |e verfasserin |4 aut | |
| 700 | 1 | |a Ortmann, Amanda |e verfasserin |4 aut | |
| 700 | 1 | |a Herzog, Jacques |e verfasserin |4 aut | |
| 700 | 1 | |a Buchman, Craig A |e verfasserin |4 aut | |
| 700 | 1 | |a Shew, Matthew A |e verfasserin |4 aut | |
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