Mining temporal data sets: Hypoplastic left heart syndrome case study

Andrew Kusiak*, Christopher A. Caldarone, Michael D. Kelleher, Fred S. Lamb, Thomas J. Persoon, Yuan Gan, Alex Burns

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Hypoplastic left heart syndrome (HLHS) affects infants and is uniformly fatal without surgery. Post-surgery mortality rates are highly variable and dependent on postoperative management. The high mortality after the first stage surgery usually occurs within the first few days after procedure. Typically, the deaths are attributed to the unstable balance between the pulmonary and systemic circulations. An experienced team of physicians, nurses, and therapists is required to successfully manage the infant. However, even the most experienced teams report significant mortality due to the extremely complex relationships among physiologic parameters in a given patient. A data acquisition system was developed for the simultaneous collection of 73 physiologic, laboratory, and nurse-assessed variables. Data records were created at intervals of 30 seconds. An expert-validated wellness score was computed for each data record. A training data set consisting of over 5000 data records from multiple patients was collected. Preliminary results demonstrated that the knowledge discovery approach was over 94.57% accurate in predicting the "wellness score" of an infant. The discovered knowledge can improve care of complex patients by the development of an intelligent simulator that can be used to support decisions.

Original languageEnglish (US)
Pages (from-to)193-201
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2003
EventData Mining and Knowledge Discovery: Theory, Tools and Technology V - Orlando, FL, United States
Duration: Apr 21 2003Apr 22 2003


  • Data mining expressions
  • Derived features
  • Feature transformation
  • Hypoplastic left heart syndrome
  • Medical informatics
  • Temporal data mining

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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