Finding group structures in “Big Data” in healthcare research using mixture models
Shu‐Kay Ng, Geoffrey John McLachlan · 2016
Big data in healthcare research is now common-place. The extraction of useful information on group structures from these data can contribute to improve the quality and effectiveness of care for a sustainable health system. It is not only the sheer size of the data that imposes difficulty in direct application of conventional clustering methods, big data in healthcare often exhibit a multilevel structure with complex correlation among observations and/or a mix of variable types. This paper considers two aspects of extension of mixture models in random effects modelling and multitask clustering of big data. The applicability of these extended mixture models is illustrated using simulated data and real data sets.