title: Improvement and automation of artificial neural networks to estimate medical outcomes.
abstract: The lengthy process of manually optimizing a feedforward backpropagation artificial neural network (ANN) provided the incentive to develop an automated system that could fine-tune the network parameters without user supervision. A new stopping criterion was introduced--the logarithmic-sensitivity index--that manages a good balance between sensitivity and specificity of the output classification. The automated network automatically monitored the classification performance to determine when was the best time to stop training-after no improvement in the performance measure (either highest correct classification rate, lowest mean squared error or highest log-sensitivity index value) occurred in the subsequent 500 epochs. Experiments were performed on three medical databases: an adult intensive care unit, a neonatal intensive care unit and a coronary surgery patient database. The optimal network parameter settings found by the automated system were similar to those found manually. The results showed that the automated networks performed equally well or better than the manually optimized ANNs, and the best classification performance was achieved using the log-sensitivity index as a stopping criterion.
authors.affiliation: Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada.
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Ennett
authors.forename: Monique
authors.initials: M
authors.lastname: Frize
authors.forename: Elaine
authors.initials: E
authors.lastname: Charette
mesh_terms: D016000:Cluster Analysis; D003422:Critical Care / Q000379:methods*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006331:Heart Diseases / Q000175:diagnosis* / Q000601:surgery; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000175:diagnosis* / Q000401:mortality; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010363:Pattern Recognition, Automated; D011379:Prognosis; D011786:Quality Control; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D016896:Treatment Outcome