Revealing chronic disease progression patterns using Gaussian process for stage inference.

ABSTRACT:

Objective

The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development.

Materials and methods

We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the O

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10797260 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature