Skip to content

Explore the most effective imputation strategies for EHR data

License

Notifications You must be signed in to change notification settings

yhzhu99/ImputeEHR

Repository files navigation

ImputeEHR

Explore the most effective imputation strategies for EHR data

The example dataset is TJH dataset, preprocessed following pyehr scripts.

Imputation Methods

Rule-based

  • LOCFImpute (last-observation-carried-forward)
  • ZeroImpute
  • MeanImpute
  • MedianImpute
  • ModeImpute
  • SimpleImpute (GRU-D Simple, concatenating the measurement with masking and time interval)

ML-based

  • InterpolationImpute
  • SmoothingImpute
  • SplineImpute
  • KNNImpute
  • MICEImpute (multiple imputation by chained equations)
  • RFImpute (Random Forest, e.g. MissForest)
  • MFImpute (matrix factorization)
  • PCAImpute
  • SoftImpute

DL-based

  • MLPImpute
  • RNNImpute
  • AEImpute (AutoEncoder)
  • GANImpute

About

Explore the most effective imputation strategies for EHR data

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages