Hypoplastic left heart syndrome: knowledge discovery with a data mining approach

Andrew Kusiak; Christopher A Caldarone; Michael D Kelleher; Fred S Lamb; Thomas J Persoon; Alex Burns

doi:10.1016/j.compbiomed.2004.07.007

Back

Hypoplastic left heart syndrome: knowledge discovery with a data mining approach

Journal article

Peer reviewed

Hypoplastic left heart syndrome: knowledge discovery with a data mining approach

Andrew Kusiak, Christopher A Caldarone, Michael D Kelleher, Fred S Lamb, Thomas J Persoon and Alex Burns

Computers in biology and medicine, Vol.36(1), pp.21-40

2006

DOI: 10.1016/j.compbiomed.2004.07.007

PMID: 16324907

View Online

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 30 s 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.

Data Mining

Medical Decision Making

Classification accuracy

Classification quality

Hypoplastic left heart syndrome

Medical knowledge discovery

Details

Title: Subtitle: Hypoplastic left heart syndrome: knowledge discovery with a data mining approach
Creators: Andrew Kusiak - University of Iowa, Industrial and Systems Engineering
Christopher A Caldarone - Division of Cardiovascular Surgery, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada
Michael D Kelleher - Department of Pediatrics, Division of Critical Care, Children's Memorial Hospital, 2300 Children's Plaza, Box 73 Chicago, IL 60614, USA
Fred S Lamb - Department of Pediatrics, The University of Iowa Hospital and Clinics, The University of Iowa, Iowa City, IA 52242, USA
Thomas J Persoon - Department of Pathology, The University of Iowa Hospital and Clinics, The University of Iowa, Iowa City, IA 52242, USA
Alex Burns - Intelligent Systems Laboratory, MIE 3131, Seamans Center, The University of Iowa, Iowa City, Iowa 52242 - 1527, USA
Resource Type: Journal article
Publication Details: Computers in biology and medicine, Vol.36(1), pp.21-40
Publisher: Elsevier Ltd
DOI: 10.1016/j.compbiomed.2004.07.007
PMID: 16324907
ISSN: 0010-4825
eISSN: 1879-0534
Language: English
Date published: 2006
Academic Unit: Industrial and Systems Engineering; Nursing
Record Identifier: 9983557504802771

Metrics

42 Record Views

15 Times Cited - Web of Science