  Sepsis is an important cause of mortality, especially in intensive care unit
(ICU) patients. Developing novel methods to identify early mortality is
critical for improving survival outcomes in sepsis patients. Using the
MIMIC-III database, we integrated demographic data, physiological measurements
and clinical notes. We built and applied several machine learning models to
predict the risk of hospital mortality and 30-day mortality in sepsis patients.
From the clinical notes, we generated clinically meaningful word
representations and embeddings. Supervised learning classifiers and a deep
learning architecture were used to construct prediction models. The
configurations that utilized both structured and unstructured clinical features
yielded competitive F-measure of 0.512. Our results showed that the approaches
integrating both structured and unstructured clinical features can be
effectively applied to assist clinicians in identifying the risk of mortality
in sepsis patients upon admission to the ICU.
