  Acute kidney injury (AKI) in critically ill patients is associated with
significant morbidity and mortality. Development of novel methods to identify
patients with AKI earlier will allow for testing of novel strategies to prevent
or reduce the complications of AKI. We developed data-driven prediction models
to estimate the risk of new AKI onset. We generated models from clinical notes
within the first 24 hours following intensive care unit (ICU) admission
extracted from Medical Information Mart for Intensive Care III (MIMIC-III).
From the clinical notes, we generated clinically meaningful word and concept
representations and embeddings, respectively. Five supervised learning
classifiers and knowledge-guided deep learning architecture were used to
construct prediction models. The best configuration yielded a competitive AUC
of 0.779. Our work suggests that natural language processing of clinical notes
can be applied to assist clinicians in identifying the risk of incident AKI
onset in critically ill patients upon admission to the ICU.
