21170468

Source:http://linkedlifedata.com/resource/pubmed/id/21170468

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0004083, umls-concept:C0020205, umls-concept:C0026175, umls-concept:C0086388, umls-concept:C0441471, umls-concept:C0442043, umls-concept:C0870077, umls-concept:C1511726
pubmed:issue	2
pubmed:dateCreated	2011-3-18
pubmed:abstractText	The analysis of administrative health care data can be helpful to conveniently assess health care activities. In this context temporal data mining techniques can be suitably exploited to get a deeper insight into the processes underlying health care delivery. In this paper we present an algorithm for the extraction of temporal association rules (TARs) on sequences of hybrid events and its application on health care administrative databases.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0210453
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:issn	0026-1270
pubmed:author	pubmed-author:BellazziRR, pubmed-author:CerraCC, pubmed-author:ConcaroSS, pubmed-author:FratinoPP, pubmed-author:SacchiLL
pubmed:issnType	Print
pubmed:volume	50
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	166-79
pubmed:meshHeading	pubmed-meshheading:21170468-Algorithms, pubmed-meshheading:21170468-Computational Biology, pubmed-meshheading:21170468-Data Mining, pubmed-meshheading:21170468-Delivery of Health Care, pubmed-meshheading:21170468-Diabetes Mellitus, Type 1, pubmed-meshheading:21170468-Diabetes Mellitus, Type 2, pubmed-meshheading:21170468-Humans
pubmed:year	2011
pubmed:articleTitle	Mining health care administrative data with temporal association rules on hybrid events.
pubmed:affiliation	Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy.
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't