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n a hospital’s intensive care unit, doctors get a cascade of data about each patient’s condition that can be challenging to quickly organize and interpret. Now, University of Florida researchers have developed and successfully tested an artificial intelligence system that delivers streamlined and timely details about crucial changes in a patient’s condition.

The system, known as Deep Sequential Organ Failure Assessment, or DeepSOFA, works by collecting, organizing and presenting a patient’s medical data so that doctors can make nimbler, better-informed decisions.

  Category: Paper Release

9 posts
February 19th, 2019

News Coverage in UFHealth News

n a hospital’s intensive care unit, doctors get a cascade of data about each patient’s condition that can be challenging […]

August 29th, 2018

Survey on EHR Deep learning available on IEEE JBHI

Our survey paper on deep learning for EHR will appear in the September Issue of IEEE JBHI: Link  

April 30th, 2018

Intelligent ICU paper available on arXiv

Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions […]

April 16th, 2018

CVPR Workshop paper accepted

CVPR workshop paper on activity recognition in the ICU is accepted. Congratulations to Anis and Kumar!   Patients staying in […]

April 16th, 2018

EMBC Paper accepted

Our EMBC paper is accepted, congratulations to Anis, Raha, and Paul!   Physiological timeseries such as vital signs contain important […]

March 13th, 2018

Paper on surgical team network structure is online

We examined the structure of intra- and postoperative case-collaboration networks among the surgical service providers in a quaternary-care academic medical […]

March 12th, 2018

Paper on the cover of IEEE JBHI

The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in […]

March 12th, 2018

DeepSOFA beats your old SOFA score

Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require manual, time-consuming, and error-prone calculations […]

March 10th, 2018

Paper available for open peer review

Chronic pain, including arthritis, affects about 100 million adults in the United States. Complexity and diversity of pain experience across […]