English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Developing knowledge-driven contemporaneous health index (CHI) that canprecisely reflect the underlying patient across the course of the condition'sprogression holds a unique value, like facilitating a range of clinicaldecision-making opportunities. This is particularly important for monitoringdegenerative condition such as Alzheimer's disease (AD), where the condition ofthe patient will decay over time. Detecting early symptoms and progressionsign, and continuous severity evaluation, are all essential for diseasemanagement. While a few methods have been developed in the literature,uncertainty quantification of those health index models has been largelyneglected. To ensure the continuity of the care, we should be more explicitabout the level of confidence in model outputs. Ideally, decision-makers shouldbe provided with recommendations that are robust in the face of substantialuncertainty about future outcomes. In this paper, we aim at filling this gap bydeveloping an uncertainty quantification based contemporaneous longitudinalindex, named UQ-CHI, with a particular focus on continuous patient monitoringof degenerative conditions. Our method is to combine convex optimization andBayesian learning using the maximum entropy learning (MEL) framework,integrating uncertainty on labels as well. Our methodology also providesclosed-form solutions in some important decision making tasks, e.g., such aspredicting the label of a new sample. Numerical studies demonstrate theeffectiveness of the propose UQ-CHI method in prediction accuracy, monitoringefficacy, and unique advantages if uncertainty quantification is enabledpractice.

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