Paper Info
Title
Hypoplastic left heart syndrome: knowledge discovery with a data mining approach.
Abstract
Hypoplastic left heart syndrome (HLHS) affects infants and is uniformly fatal without surgical palliation. Post-surgery mortality rates are highly variable and dependent on postoperative management. A data acquisition system was developed for collection of 73 physiologic, laboratory, and nurse-assessed parameters. The acquisition system was designed for the collection on numerous patients. Data records were created at 30s intervals. An expert-validated wellness score was computed for each data record. To efficiently analyze the data, a new metric for assessment of data utility, the combined classification quality measure, was developed. This measure assesses the impact of a feature on classification accuracy without performing computationally expensive cross-validation. The proposed measure can be also used to derive new features that enhance classification accuracy. The knowledge discovery approach allows for instantaneous prediction of interventions for the patient in an intensive care unit. The discovered knowledge can improve care of complex to manage infants by the development of an intelligent bedside advisory system.
Year
DOI
Venue
2006
10.1016/j.compbiomed.2004.07.007
Comp. in Bio. and Med.
Keywords
DocType
Volume
intensive care unit,medical decision making,knowledge discovery approach,medical knowledge discovery,data record,acquisition system,combined classification quality measure,hypoplastic left heart syndrome,classification quality,heart syndrome,proposed measure,data mining,classification accuracy,data mining approach,intelligent bedside advisory system,data utility,data acquisition system
Journal
36
Issue
ISSN
Citations 
1
0010-4825
7
PageRank 
References 
Authors
0.57
4
6
Name
Order
Citations
PageRank
A. Kusiak1724111.33
Christopher A Caldarone270.57
Michael D Kelleher370.57
Fred S Lamb470.57
Thomas J Persoon570.57
Alex Burns670.57