Nosocomial Risk Datasets from MIMIC-III

Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to foster the development of generalizable models capable of leveraging clinical notes to predict healthcare-associated diseases 24-96 hours in advance. We developed data to explore the problem of predicting the risk of hospital acquired (occurring ≥ \geq 48 hours after admission) acute kidney injury, pressure injury, or anemia ≥ \geq 24 hours before it is implicated by the patient's chart, labs, or notes. We relied on the MIMIC-III critical care database and extract distinct positive and negative cohorts for each disease. We retrospectively determine the date-of- event using structured and unstructured criteria so that it may be used as a form of indirect supervision to train and evaluate automatic systems for predicting disease risk using clinical notes. This data was used as the experimental basis for the CANTRIP project.