imputeCGM 0.1.0

CGM missing glucose imputation with MICE, ARIMA, XGBoost, and optional GAIN-based methods

imputeCGM

imputeCGM is a Python package for imputing missing glucose values in continuous glucose monitoring (CGM) data.

The package supports:

• Automatic method selection: MICE+ARIMA when missingness is low and MICE+XGBoost when missingness is higher
• Forced MICE+ARIMA
• Forced MICE+XGBoost
• GAIN-only imputation
• GAIN+ARIMA
• GAIN+XGBoost

Installation

For MICE, ARIMA, and XGBoost methods:

python -m pip install -e .

For GAIN-based methods, install the optional TensorFlow dependency:

python -m pip install -e .[gain]

Python usage

from imputeCGM import impute_cgm

out = impute_cgm(
    df,
    timestamp_col="timestamp",
    subjectid_col="subjectid",
    glucose_col="glucose_value",
    interval_minutes=5,
    time_gap_tolerance_minutes=3,
    model_method="auto",
)

Valid model_method values are:

auto
mice_arima
mice_xgboost
gain
gain_arima
gain_xgboost

CLI usage

imputeCGM --input ExampleData.csv --output ExampleData_imputed.csv --model-method auto