Pubblicazione scientifica

Finding group structures in “Big Data” in healthcare research using mixture models

FILONE TECNOLOGICO	TEMA
trasformazione e commercializzazione

RIASSUNTO

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.

DATA

Data di pubblicazione: 01/12/2016

AUTORI

SHU-KAY NG
GEOFFREY J MCLACHLAN

ENTE DI AFFERENZA

SCHOOL OF MEDICINE MENZIES HEALTH INST QUEENSLAND GRIFFITH UNIV NATHAN 4111 AUSTRALIA
DEPT MATHEMATICS UNIV QUEENSLAND BRISBANE 4072 AUSTRALIA

RIVISTA

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Page(s): 1214-1219)

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