Deep Dynamic Gaussian Mixture Model for Dialysis Time Series Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for forecasting sparse multivariate time series data, such as those from dialysis patients, fail to effectively leverage dynamic distributions and correlations between time series, leading to sub-optimal results due to sparsity and missing values.
Innovation Solution
A Deep Dynamic Gaussian Mixture (DDGM) model is employed, which includes a pre-imputation component using temporal intensity functions based on Gaussian kernels and multi-dimensional correlations to fill missing values, and a forecasting component that captures latent dynamic clustering structures for robust forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing forecasting methods process multivariate time series individually, then the method complexity is low, but the forecasting accuracy deteriorates due to high sparsity and inability to leverage dynamic distributions
Solution Approach 1:
The patent merges multiple multivariate time series into a unified forecasting model that processes them jointly rather than individually. The Dynamic Gaussian Mixture Model integrates information across different time series to capture shared dynamic distributions, thereby improving forecasting accuracy while handling sparsity through combined data leverage.
Solution Approach 2:
The patent introduces a new dimension by modeling the dynamic distributions underlying the time series data using Gaussian mixture models. This adds a distributional dimension to the forecasting approach, allowing the system to capture temporal patterns and correlations that individual series processing cannot detect, thus improving accuracy without excessive complexity.
2Measurement precision
If existing methods do not leverage dynamic distributions underlying the time series, then the computational resources are conserved, but the forecasting accuracy deteriorates due to sub-optimal results under high sparsity
Solution Approach 1:
The patent changes the parameters by introducing dynamic distribution parameters through Gaussian mixture models. Instead of using fixed statistical parameters, the model learns time-varying distribution parameters that adapt to the underlying dynamics of the multivariate time series, improving forecasting accuracy under sparsity conditions.
Solution Approach 2:
The patent performs preliminary action by pre-processing the time series data to estimate and store the dynamic distribution parameters before the actual forecasting step. This preliminary estimation of Gaussian mixture parameters enables the forecasting model to leverage learned dynamic patterns without excessive computational burden during the forecasting phase itself.
3Reliability
If missing values are not handled in the input time series, then the data processing is simple, but the forecasting reliability deteriorates due to incomplete information
Solution Approach 1:
The patent implements self-service by enabling the model to automatically handle missing values through the dynamic Gaussian mixture framework. The model learns to infer missing values from the dynamic distributions and correlations in the observed data, without requiring manual imputation or complex preprocessing pipelines, thus improving reliability while maintaining reasonable processing complexity.
Data Source
AI summary
A method for managing data of dialysis patients by employing a Deep Dynamic Gaussian Mixture (DDGM) model to forecast medical time series data is presented. The method includes 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.