Acute Respiratory Distress Syndrome (ARDS) is a severe form of respiratory failure that impairs normal lung function resulting in high mortality rates. High incidence exists among mechanically ventilated patients. Early and accurate identification of ARDS may lead to improved outcomes of this life-threatening disease. We describe results of an NLP tool with sound operating characteristics demonstrating the efficacy of using NLP clinical decision support for early detection of ARDS from radiographic evidence of disease.
Learning Objective: To understand how natural language processing can be used to aid in case detection of complex diseases.
Jeffrey Ferraro (Presenter)
Jason Jacobs, Intermountain Healthcare
Ithan Peltan, Intermountain Healthcare
Michael Lanspa, Intermountain Healthcare
Raj Srivastava, Intermountain Healthcare
Colin Grissom, Intermountain Healthcare