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Current neonatal illness scoring systems are not designed to predictoutcomes for individual patients, but rather can provide an overview of a population of patients for objective comparison when reporting outcomes. Having more patient-specific predictions may help physicians make better treatment decisions in a Neonatal Intensive Care Unit (NICU) environment. We developed neonatal mortality prediction models using C5.0 decision tree software that met criteria for clinically useful results (>50-60% sensitivity, >90% specificity) for individual patients using data from real-time medical measurement devices. The models were evaluated to identify: (1) the model with the bestperformance based on minimizing false positives, and (2) the attributes used most often in the best clinically useful models. Performance results showed that the mortality model using summary data duringthe first 48 hours after NICU admission provided, on average, the highest sensitivity and specificity with the least number of false positives (sensitivity=63%, specificity=94%, positive predictive value=38%), exceeding the performance criteria requested by our clinicalpartners. The attributes used most often in the best models for predicting mortality with our data were: mean blood pressure, serum pH,immature/total neutrophil ratio, serum sodium, serum glucose, respiratory rate, heart rate, and pO2 blood oxygen level.

Extubation failure (EF) is an ongoing problem in the neonatal intensive care unit (NICU). Nearly 25% of neonates fail their first extubation attempt, requiring re-intubations that are associated with riskfactors and financial costs. We identified 179 mechanically ventilated neonatal patients that were intubated within 24 hours of birth in the MIMIC-II intensive care database. We analyzed data from the patients 2 hours prior to their first extubation attempt, and developed a prediction algorithm to distinguish patients whose extubation attempt was successful from those that had EF. From an initial list of57 candidate features, our machine learning approach narrowed downto six features useful for building an EF prediction model: monocytecell count, rapid shallow breathing index, fraction of inspired oxygen (FiO2), heart rate, PaO2/FiO2 ratio where PaO2 is the partial pressure of oxygen in arterial blood, and work of breathing index. Algorithm performance had an area under the receiver operating characteristic curve (AUC) of 0.871 and sensitivity of 70.1% at 90% specificity.