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Related Experiment Video

Updated: Dec 13, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Published on: December 9, 2015

StoCast: Stochastic Disease Forecasting With Progression Uncertainty.

Xian Teng, Sen Pei, Yu-Ru Lin

IEEE Journal of Biomedical and Health Informatics

|August 6, 2020

Summary

Forecasting patient disease progression is improved with a new deep generative model, Stochastic Disease Forecasting Model (StoCast). StoCast handles uncertainty in patient data and progression, offering more reliable future health trajectory predictions.

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Last Updated: Dec 13, 2025

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Published on: October 23, 2020

Area of Science:

Computational biology
Medical informatics
Machine learning

Background:

Accurate forecasting of patient disease progression using longitudinal clinical data is crucial for healthcare.
Existing machine learning and deep learning models struggle with inherent uncertainties like progression and data variability.

Purpose of the Study:

To introduce a novel deep generative model, Stochastic Disease Forecasting Model (StoCast), and its neural network architecture, StoCastNet.
To address limitations in current models regarding progression and data uncertainty in disease forecasting.

Main Methods:

Developed StoCast, a deep generative model with internal stochastic components to model data and progression uncertainty.
Utilized StoCastNet, a neural network architecture, trained efficiently via stochastic optimization.
Applied the model to Alzheimer's disease and Parkinson's disease datasets.

Main Results:

StoCast demonstrated robust and superior performance compared to deterministic baseline approaches.
The model effectively handles progression uncertainty (multiple trajectories) and data uncertainty (imprecise observations).
StoCast provides comprehensive estimates of future disease progression trajectories.

Conclusions:

StoCast offers a powerful approach for disease progression forecasting in complex, uncertain scenarios.
The model's ability to convey richer information can enhance clinical decision-making confidence.
StoCast represents a significant advancement in leveraging longitudinal data for predictive healthcare.