@entity1699 ( @entity1699 ) affects @entity1 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 @entity1 . 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 @entity1 in an intensive care unit. The discovered knowledge can improve care of complex to manage @entity1 by the development of an intelligent bedside advisory system.

XXXX : knowledge discovery with a data mining approach.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient', 'infants'] @entity1699 :: ('MESH:D018636', 'Disease') :: ['Hypoplastic left heart syndrome', 'HLHS']
@entity1699 :: (MESH:D018636,Disease) :: ['Hypoplastic left heart syndrome']
