File:Fig5 Beaulieu-JonesJMIRMedInfo2018 6-1.png

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Summary

Description

Figure 5. Imputation root mean square error (RMSE) for a subset of 10,000 patients from simulation 4. A total of 12 imputation methods were tested (x-axis), and each color corresponds to a Logical Observation Identifiers Names and Codes (LOINC) code. The black line shows the theoretical error from random sampling. FI: fancyimpute; KNN: k-nearest neighbors; MICE: Multivariate Imputation by Chained Equations; pmm: predictive mean matching; RF: random forest; SVD: singular value decomposition.

Source

Beaulieu-Jones, B.K.; Lavage, D.R.; Snyder, J.W.; Moore, J.H.; Pendergrass, S.A.; Bauer, C.R. (2018). "Characterizing and managing missing structured data in electronic health records: Data analysis". JMIR Medical Informatics 6 (1): e11. doi:10.2196/medinform.8960. 

Date

2018

Author

Beaulieu-Jones, B.K.; Lavage, D.R.; Snyder, J.W.; Moore, J.H.; Pendergrass, S.A.; Bauer, C.R.

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Creative Commons Attribution 4.0 International

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current20:30, 6 March 2018Thumbnail for version as of 20:30, 6 March 20182,400 × 1,500 (330 KB)Shawndouglas (talk | contribs)