TABLE VII.

Selected Methods for EHR Data Pre-processing

Method	Advantages	Limitations
Missing data: list-wise deletion, mean filling* [118, 119]	Simple to implement; complete case analysis	Loss of statistical power; introduces biases; underestimates variances
Missing data: hot deck, nearest neighbor* [120]	Simple to implement and interpret; immune to cross-user inconsistencies	Introduces biases; underestimates variances
Missing data: interpolation (linear, piece-wise linear, spline, cubic) [121]	Simple to implement and interpret; direct estimation on the basis of neighbors	Does not account for relationships among different features
Missing data: model-based filling (expectation maximization, maximum likelihood, multiple imputations)* [122]	Accounts for uncertainty in imputations	Does not account for missing data mechanisms (i.e., MCAR, MAR, and MNAR)
Waveforms: noise filtering (IIR, FIR, PCA, ICA, Kalman filter, wavelets) [50, 123]	Generally simple to implement	Falls short in situations where “true” waveform is obscured by artifact such as patient motion
Waveforms: signal quality indices [123–125]	Human-interpretable metrics of signal quality	Can be complex to implement and computationally intensive; may require ad-hoc calibration based on the features of the target waveform
Waveforms: sensor fusion [126, 127]	Improved SNR; reduces data dimensionality while increasing data quality	Computationally intensive; loss of detail from individual sensor waveforms

^*Highly impactful method with more than 50,000 relevant papers.