title: Contrasting temporal trend discovery for large healthcare databases.
abstract: With the increased acceptance of electronic health records, we can observe the increasing interest in the application of data mining approaches within this field. This study introduces a novel approach for exploring and comparing temporal trends within different in-patient subgroups, which is based on associated rule mining using Apriori algorithm and linear model-based recursive partitioning. The Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality was used to evaluate the proposed approach. This study presents a novel approach where visual analytics on big data is used for trend discovery in form of a regression tree with scatter plots in the leaves of the tree. The trend lines are used for directly comparing linear trends within a specified time frame. Our results demonstrate the existence of opposite trends in relation to age and sex based subgroups that would be impossible to discover using traditional trend-tracking techniques. Such an approach can be employed regarding decision support applications for policy makers when organizing campaigns or by hospital management for observing trends that cannot be directly discovered using traditional analytical techniques.
authors.affiliation: Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia. Electronic address: goran.hrovat@uni-mb.si.
authors.forename: Goran
authors.initials: G
authors.lastname: Hrovat
authors.forename: Gregor
authors.initials: G
authors.lastname: Stiglic
authors.forename: Peter
authors.initials: P
authors.lastname: Kokol
authors.forename: Milan
authors.initials: M
authors.lastname: Ojsteršek
mesh_terms: D003628:Database Management Systems; D006801:Humans; D006949:Hyperlipidemias; D006973:Hypertension; D016247:Information Storage and Retrieval; D010351:Patient Discharge
keywords: Data mining; Decision support; Trend discovery