Robust forecasting system on irregular time series in dialysis medical records

A non-transitory computer-readable storage medium comprising a computer-readable program for managing data of dialysis patients by employing a Deep Dynamic Gaussian Mixture (DDGM) model to forecast medical time series data, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:

• filling missing values in an input multivariate time series by model parameters, via a pre-imputation component, by using a temporal intensity function based on Gaussian kernels and multi-dimensional correlation based on correlation parameters to be learned;
• and
• storing, via a forecasting component, parameters that represent cluster centroids used by the DDGM to cluster time series for capturing correlations between different time series samples.

A system for managing data of dialysis patients by employing a Deep Dynamic Gaussian Mixture (DDGM) model to forecast medical time series data, the system comprising:

• a pre-imputation component for filling missing values in an input multivariate time series by model parameters by using a temporal intensity function based on Gaussian kernels and multi-dimensional correlation based on correlation parameters to be learned;
• and
• a forecasting component for storing parameters that represent cluster centroids used by the DDGM to cluster time series for capturing correlations between different time series samples.