Identifying risk factors for Alzheimer's disease from multivariate longitudinal clinical data using temporal pattern mining.

ABSTRACT:

Background

Patient data contain a wealth of information that could aid in understanding the onset and progression of disease. However, the task of modelling clinical data, which consist of multiple heterogeneous time series of different lengths, measured at different time intervals, is a complex one. A growing body of research has applied temporal pattern mining to this problem to identify common patterns in clinical attributes over time. However, the vast majority of these algorithms use techniques that are not ideally suited to clinical data. We present an efficient and scalable framework designed specifically for temporal pattern mining of real-world clinical data. Our framework combines temporal abstraction, an extended version of the efficient pattern-growth algorithm, TPMiner

SUBMITTER: Spooner A 

PROVIDER: S-EPMC11834509 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature