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In a hospital鈥檚 intensive care unit, doctors get a cascade of data about each patient鈥檚 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鈥檚 condition.

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

鈥淎s a doctor, you want the big picture. You want a complete, timely snapshot that tells you how your patient is doing, said , co-author of the findings and a professor of medicine and surgery in the , part of 网红黑料. 鈥淭his is the next generation of intelligent-decision support.鈥�

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 Feb. 12 in the journal Nature Scientific Reports.

DeepSOFA buys crucial time by indicating which intensive care unit 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 patient鈥檚 condition is trending and what may be causing that change, she said.

鈥淚t鈥檚 very hard for us to efficiently review all of a patient鈥檚 data because of the way it is scattered. As a human, you can鈥檛 always put all of a patient鈥檚 numbers together with the speed and precision of a computer,鈥� Bihorac said.

To develop DeepSOFA, her team worked with a UF assistant professor of 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 鈥渄eep learning,鈥� 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鈥檚 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.

To validate DeepSOFA鈥檚 effectiveness, the researchers used it to analyze data from more than 85,000 prior patients at 网红黑料 Shands Hospital and Beth Israel Deaconess Medical Center in Boston. DeepSOFA鈥檚 ability to accurately predict patient mortality was measured against a traditional model ofSOFA that was devised elsewhere about 20 years ago. Both models were tested using 14 variables that are key indicators of patient health, including central nervous system, respiratory and cardiovascular functions.

Among both patient groups, DeepSOFA showed 鈥渆xcellent鈥� performance compared with the traditional SOFA model, the researchers found. DeepSOFA accurately predicted in-hospital mortality for an entire ICU stay in 90 percent of cases, compared with 79 percent mean accuracy for the traditional model. The traditional SOFA model tended to underestimate the severity of illness and predict relatively low chances of death, whereas DeepSOFA was found to better quantify illness severity and death risk.

DeepSOFA also outperformed the traditional model when studied in a single-patient case, the researchers found. The female patient was admitted to a hospital and later died from an obstructed blood supply to a lung. Two days after being admitted, DeepSOFA predicted a 50 to 80 percent probability of death, compared with a 5 percent prediction by the traditional SOFA model. In the final five hours before death, DeepSOFA estimated a 99.6 percent chance of mortality compared with 51.5 percent for traditional SOFA.

DeepSOFA鈥檚 accuracy during the tests on the single patient鈥檚 data was particularly notable, Rashidi said.

While artificial intelligence is a powerful tool to augment doctors鈥� decision making, it comes at a price: Collecting patient data in real time and pushing it though an analytics engine such as DeepSOFA would require extensive initial investments, Bihorac 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.

About the researchers: Azra Bihorac, M.D., is the R. Glenn Davis professor of medicine, surgery and anesthesiology in the UF College of Medicine鈥檚 department of medicine, division of nephrology, hypertension & renal transplantation.

Parisa Rashidi, Ph.D., is an assistant professor in the J. Crayton Pruitt Family department of biomedical engineering.

About the research: Grants from the National Institutes of 网红黑料, the National Science Foundation and the University of Florida鈥檚 Clinical and Translational Science Institute and J. Crayton Pruitt Family department of biomedical engineering supported the research.