OBJECTIVE: To evaluate the efficacy of artificial @entity1274 in categorizing pediatric @entity130 @entity1 into four distinct acuity of care groups and in determining the length of stay (LOS) within specific areas of the hospital. DESIGN: Using historical information from >8,000 pediatric @entity130 @entity1 records, train and evaluate artificial @entity1274 to predict the @entity130 and LOS for each @entity1 in pediatric intensive care units (PICUs), step-down units, and floor units. Each artificial @entity1274 is evaluated for categorization accuracy and mean absolute error difference on the predicted LOS. SUBJECTS: A total of 10,353 @entity1 records from the National Pediatric Trauma registry, representing all pediatric @entity130 @entity1 treated at affiliated hospitals from April 1994 through December 1996. Records with incomplete information were eliminated from the study, leaving 8,081 usable @entity1 records. MEASUREMENTS: A total of 14 variables are selected from the 81 values present in the National Pediatric Trauma Registry as independent variables for the artificial @entity1274 . Each @entity1274 produces nine output values: five for categorizing the @entity1 's @entity130 , three for the LOS in the PICU, step-down unit, and floor units, and one for the @entity1 's total LOS. RESULTS: A fuzzy ARTMAP @entity1274 accurately categorizes 88% of mortality @entity1 and 58.3% of critical PICU @entity1 . A backpropagation @entity1274 succeeded in predicting the total LOS to within 1 day for 51.4% and the ICU LOS to within 1 day for 70.4% of all evaluated @entity1 . CONCLUSION: Information available in the first 10 mins of a @entity1 's presentation at the emergency room can be used by an artificial @entity1274 to predict @entity130 and LOS. Artificial @entity1274 enable more effective resource planning and @entity1 management.

Predicting pediatric length of stay and acuity of care in the first ten minutes with artificial XXXX .

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural network', 'neural networks'] @entity130 :: ('MESH:D014947', 'Disease') :: ['trauma', 'injury severity']
@entity1274 :: (MESH:D006319,Disease) :: ['neural networks']
