Linked Life Data

11340003

Source:http://linkedlifedata.com/resource/pubmed/id/11340003

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2001-5-7
pubmed:abstractText
Clinical pathways are widely adopted by many large hospitals around the world in order to provide high-quality patient treatment and reduce the length of hospital stay of each patient. The development of clinical pathways is a lengthy process, and may require the collaboration among physicians, nurses, and staffs in a hospital. However, the individual differences cause great variances in the execution of clinical pathways. It calls for a more dynamic and adaptive process to improve the performance of clinical pathways. This paper reports a data mining technique we have developed to discover the time dependency pattern of clinical pathways for managing brain stroke. The mining of time dependency pattern is to discover patterns of process execution sequences and to identify the dependent relation between activities in a majority of cases. By obtaining the time dependency patterns, we can predict the paths for new patients when he/she is admitted into a hospital; in turn, the health care procedure will be more effective and efficient.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1386-5056
pubmed:author
pubmed:issnType
Print
pubmed:volume
62
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
11-25
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Mining time dependency patterns in clinical pathways.
pubmed:affiliation
Department of Information Management, National Sun Yat-sen University, Kaohsiung 804, Taiwan, ROC. frlin@cc.nsysu.edu.tw
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't