UQ-CHI: An Uncertainty Quantification-Based Contemporaneous Health Index for Degenerative Disease Monitoring

by   Aven Samareh, et al.

Developing knowledge-driven contemporaneous health index (CHI) that can precisely reflect the underlying patient across the course of the condition's progression holds a unique value, like facilitating a range of clinical decision-making opportunities. This is particularly important for monitoring degenerative condition such as Alzheimer's disease (AD), where the condition of the patient will decay over time. Detecting early symptoms and progression sign, and continuous severity evaluation, are all essential for disease management. While a few methods have been developed in the literature, uncertainty quantification of those health index models has been largely neglected. To ensure the continuity of the care, we should be more explicit about the level of confidence in model outputs. Ideally, decision-makers should be provided with recommendations that are robust in the face of substantial uncertainty about future outcomes. In this paper, we aim at filling this gap by developing an uncertainty quantification based contemporaneous longitudinal index, named UQ-CHI, with a particular focus on continuous patient monitoring of degenerative conditions. Our method is to combine convex optimization and Bayesian learning using the maximum entropy learning (MEL) framework, integrating uncertainty on labels as well. Our methodology also provides closed-form solutions in some important decision making tasks, e.g., such as predicting the label of a new sample. Numerical studies demonstrate the effectiveness of the propose UQ-CHI method in prediction accuracy, monitoring efficacy, and unique advantages if uncertainty quantification is enabled practice.


Uncertainty in Extreme Multi-label Classification

Uncertainty quantification is one of the most crucial tasks to obtain tr...

Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science

Modern science, technology, and politics are all permeated by data that ...

Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification

Effective quantification of uncertainty is an essential and still missin...

Uncertainty Quantification for Fisher-Kolmogorov Equation on Graphs with Application to Patient-Specific Alzheimer Disease

The Fisher-Kolmogorov equation is a diffusion-reaction PDE that is used ...

Assessing transfer functions in control systems

When dealing with control systems, it is useful and even necessary to as...

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial

On top of machine learning models, uncertainty quantification (UQ) funct...

Evaluating and Boosting Uncertainty Quantification in Classification

Emergence of artificial intelligence techniques in biomedical applicatio...

Please sign up or login with your details

Forgot password? Click here to reset