In 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 UF 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.
“As a doctor, you want the big picture. You want a complete, timely snapshot
that tells you how your patient is doing,” said Azra Bihorac, MD, MS, co-author
of the findings and a professor of medicine and surgery in the UF College of
Medicine. “This is the next generation of intelligent decision support.”
Using data and outcomes from prior patients to test DeepSOFA, the
researchers found that it delivers more accurate predictions of in-hospital
mortality than other models. The findings were published in February in the
journal Nature Scientific Reports.
DeepSOFA buys crucial time by indicating which ICU patients may need a
lifesaving intervention to prevent potentially fatal conditions, Bihorac said.
DeepSOFA can be a powerful predictive tool to help doctors determine how a
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patient’s condition is trending and what may be causing that change, she said.
“It’s very hard for us to efficiently review all of a patient’s data because of the
way it is scattered,” Bihorac said. “As a human, you can’t always put all of a
patient’s numbers together with the speed and precision of a computer.”
To develop DeepSOFA, her team worked with Parisa Rashidi, PhD, a UF
assistant professor of biomedical engineering and co-author of the paper.
Rashidi and doctoral student Benjamin Shickel spent several years working
with Bihorac and her team to develop the algorithm that powers the
program. The system uses “deep learning,” a type of artificial intelligence
that automatically processes large amounts of raw data and discovers latent
patterns within those numbers. The result: a real-time, autonomous system
that gives doctors an efficient but thorough look at a patient’s condition and
how it is trending. It is the first time that deep-learning technology has been
used to generate patient viability scores, the researchers said.
Next, the researchers will work on the technical infrastructure needed to
integrate DeepSOFA with electronic health records in real time, which would
allow it to run autonomously in hospital settings, Rashidi said.
College News
INNOVATION CORNER:
Make a long story short
UF researchers develop AI system to help in patient care decision-making
BY DOUG BENNETT
Parisa Rashidi, PhD; Azra Bihorac, MD; and Benjamin Shickel