Developing ANN approaches to estimate neonatal ICU outcomes.

Abstract

A medical database is a valuable resource for medical research. By analyzing the patient records, the physicians can know better about the patient outcomes and resources utilization. In NICU where the medical resources are very expensive, it is particularly important for the physicians to know the relationship between the patient measurements and the outcomes (e.g. death, ventilation, length of stay, and other complications including lung disease and brain damage). Many techniques have been used in predicting or estimating patient outcomes in the literature. A backpropagation neural network (NN) was selected. Because neural networks cannot handle missing information, whereas incomplete patient records is a very common occurrence in the Neonatal Intensive Care Unit (NICU) database, two sets of experiments were conducted. In the first set of experiments, all the cases with missing information in a NICU database of about seven thousand patients were excluded. Only complete records were kept. In the second set of experiments, we tended to make full use of the records with missing information. The missing values were replaced with their NORMAL values. (Abstract shortened by UMI.)