Description
Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and costs an estimated $3.1 billion annually. The ability to predict HAP could improve patient outcomes and reduce costs. Traditional pneumonia risk prediction models rely on a small number of hand-chosen signs and symptoms and have been shown to poorly discriminate between low- and high-risk individuals. Consequently, we wanted to investigate whether modern data-driven techniques applied to respective pneumonia cohorts could provide more robust and discriminative prognostication of pneumonia risk. In this paper we present a deep learning system for predicting imminent pneumonia risk one or more days into the future using clinical observations documented in ICU notes for an at-risk population (n = 1,467). We show how the system can be trained without direct supervision or feature engineering from sparse, noisy, and limited data to predict future pneumonia risk with 96% Sensitivity, 72% AUC, and 80% F1-measure, outperforming SVM approaches using the same features by 20% Accuracy (relative; 12% absolute).
Learning Objective: Predict imminent pneumonia risk from ICU notes with deep learning
Authors:
Travis Goodwin (Presenter)
National Library of Medicine
Dina Demner-Fushman, National Library of Medicine
Presentation Materials: