Integration of Neonatal Mortality Prediction Models into a Clinical Decision Support System

This thesis describes the development of neonatal mortality risk estimation models using Artificial Neural Networks (ANNs), the integration of these models into the Physician-Parent Decision Support (PPADS) tool, and the pilot study to test the PPADS tool. A set of data mining programs were created to automate the data preparation, the development of ANN models and the selection of models that satisfy the usefulness criteria specified by our clinician partners. These programs were used to classify neonatal mortality data (6% mortality rate) with the average sensitivity and specificity of 81% and 98% respectively. The mortality models were integrated with the PPADS tool to provide predictions about the risk of mortality for neonates admitted to the Neonatal Intensive Care Unit (NICU). The observational and survey study conducted with parents whose infant did not graduate (died) from the NICU gave encouraging results regarding the usefulness of the PPADS tool.