Search Results for author:
Found 5 papers, 1 papers with code
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data
The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision.
Regional Tree Regularization for Interpretability in Black Box Models
The lack of interpretability remains a barrier to the adoption of deep neural networks.
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability
Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physicians as the corresponding increase in blood pressure has been hard to predict.
Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital
MIMIC-III, a freely accessible critical care database of Beth Israel Deaconess Medical Center, Boston, USA was used to retrospectively study trends and outcomes of cancer patients admitted to the ICU between 2002 and 2011.
Deep Reinforcement Learning for Sepsis Treatment
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually.
