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, the concepts of relative risk and the odds ratio to identify interesting and high-risk patterns and multiprocessing to improve computational efficiency. A complete set of cut-off values for discretisation and interpretation of the data is provided and is applicable to studies on ageing populations in general. We name this framework Clinical Temporal Pattern Mining or C-TPM.

Results: The framework is applied to data from two real-world studies of Alzheimer's disease (AD). The patterns discovered were predictive of AD in survival analysis models with a Concordance index of up to 0.87 and contain clinically relevant variables. A visualisation module provides a clear picture of the discovered patterns for ease of interpretability.

Conclusions: The framework provides an effective and scalable method of modelling multivariate, longitudinal clinical data and can identify patterns in uncommon diseases and those that progress slowly over time. It is generalisable to clinical data from other medical domains as well as non-clinical data.