  This study explores the creation of a machine learning model to automatically
identify whether a Neonatal Intensive Care Unit (NICU) patient was diagnosed
with neonatal jaundice during a particular hospitalization based on their
associated clinical notes. We develop a number of techniques for text
preprocessing and feature selection and compare the effectiveness of different
classification models. We show that using ensemble decision tree
classification, both with AdaBoost and with bagging, outperforms support vector
machines (SVM), the current state-of-the-art technique for neonatal jaundice
coding.
