Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease
This work addresses the challenge of analyzing complex longitudinal data for Alzheimer's disease risk prediction, though it appears incremental as it applies existing methods like temporal pattern mining and survival analysis to a new domain.
The authors tackled the problem of modeling longitudinal clinical data to identify risk factors for Alzheimer's disease, achieving a predictive Concordance index of up to 0.8 in survival analysis models.
A novel framework is proposed for handling the complex task of modelling and analysis of longitudinal, multivariate, heterogeneous clinical data. This method uses temporal abstraction to convert the data into a more appropriate form for modelling, temporal pattern mining, to discover patterns in the complex, longitudinal data and machine learning models of survival analysis to select the discovered patterns. The method is applied to a real-world study of Alzheimer's disease (AD), a progressive neurodegenerative disease that has no cure. The patterns discovered were predictive of AD in survival analysis models with a Concordance index of up to 0.8. This is the first work that performs survival analysis of AD data using temporal data collections for AD. A visualisation module also provides a clear picture of the discovered patterns for ease of interpretability.