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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)21-40
Number of pages20
JournalComputers in Biology and Medicine
Volume36
Issue number1
DOIs
StatePublished - Jan 2006

Keywords

  • Classification accuracy
  • Classification quality
  • Data mining
  • Hypoplastic left heart syndrome
  • Medical decision making
  • Medical knowledge discovery

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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