A proposed artificial intelligence tool to support clinician decision-making about hospital patients at risk for sepsis has an unusual feature: accounting for its lack of certainty and suggesting what demographic data, vital signs and lab test results it needs to improve its predictive performance.
The system, called SepsisLab, was developed based on and will be presented orally Wednesday (Aug. 28) at SIGKDD 2024 in Barcelona, Spain.
Sepsis is a life-threatening medical emergency – it can rapidly lead to organ failure – but it’s not easy to diagnose because its symptoms of fever, low blood pressure, increasing heart rate and breathing problems can look like a lot of other conditions. This work builds upon a previous machine learning model developed by Zhang and colleagues that estimated the optimal time to give antibiotics to patients with a suspected case of sepsis.
SepsisLab is designed to come up with a risk prediction quickly, but produces a new prediction every hour after new patient data has been added to the system.
“When a patient first comes in, there are many missing values, especially for lab tests,” said first author Changchang Yin, a computer science and engineering PhD student in Zhang’s Artificial Intelligence in Medicine lab.
In most AI models, missing data points are accounted for with a single assigned value – a process called imputation – “but the imputation model could suffer from uncertainty that can be propagated to the downstream prediction model,” Yin said.
“If the imputation model cannot accurately impute the missing value and it’s a very important value, the variable should be observed. Our active sensing algorithm aims to find such missing values and tell clinicians what additional variables they might need to observe – variables that can make the prediction model more accurate.”
Equally important to removing uncertainty from the system over the passage of time is providing clinicians with actionable recommendations. These include lab tests rank-ordered based on their value to the diagnostic process and estimates of how a patient’s sepsis risk would change depending on specific clinical treatments.
Experiments showed adding 8% of the new data from lab tests, vital signs and other high-value variables reduced the propagated uncertainty in the model by 70% – contributing to its 11% improvement in sepsis risk accuracy.
“The algorithm can select the most important variables, and the physician’s action reduces the uncertainty,” said Zhang, also a core faculty member in Ohio State’s Translational Data Analytics Institute. “This fundamental mathematics work is the most important technical innovation – the backbone of the research.”
Zhang sees human-centered AI as part of the future of medicine – but only if AI interacts with clinicians in a way that makes them trust the system.
“This is not about building an AI system that can conquer the world,” he said. “The center of medicine is hypothesis testing and making decisions minute after minute that are not just ‘yes’ or ‘no.’ We envision a person at the center of the interaction using AI to help that human feel superhuman.”
This research was supported by the National Science Foundation, the National Institutes of Health and an Ohio State President’s Research Excellence Accelerator Grant. Zhang has received additional NIH funding to continue collaborating with clinicians on this work.
Additional co-authors include Jeffrey Caterino of The Ohio State University Wexner Medical Center, Bingsheng Yao and Dakuo Wang of Northeastern University, and Pin-Yu Chen of IBM Research.
