Identifying risk factors for two complication types for neonatal intensive care patients (NICU).
This paper discusses the results of applying artificial neural networks to predicting complication for neonatal intensive care patients. Risk factors that lead to necrotizing entero-colitis or broncho-pulmonary dysplasia were identified. Future work will expand this work to other outcomes and add probability information to the estimations.
Frize
Monique
M
Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. mfrize@connect.carleton.ca
Walker
R C
RC
Ibrahim
Doaa
D
D001997:Bronchopulmonary Dysplasia / Q000175:diagnosis / Q000453:epidemiology*; D002170:Canada / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D020345:Enterocolitis, Necrotizing / Q000175:diagnosis / Q000453:epidemiology*; D005103:Expert Systems; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000706:statistics & numerical data*; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity