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21 March 2003 Mining temporal data sets: hypoplastic left heart syndrome case study
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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 demonstratd 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 development of an intelligent simulator that can be used to support decisions.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew Kusiak, Christopher A. Caldarone, Michael D. Kelleher, Fred S. Lamb, Thomas J. Persoon, Yuan Gan, and Alex Burns "Mining temporal data sets: hypoplastic left heart syndrome case study", Proc. SPIE 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V, (21 March 2003);

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