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 feedback from doctors and nurses who treat patients in the emergency departments and ICUs where sepsis, the body’s overwhelming response to an infection, is most commonly seen. They reported dissatisfaction with an existing AI-assisted tool that generates a patient risk prediction score using only electronic health records, but no input data from clinicians.
Scientists at The Ohio State University designed SepsisLab to be able to predict a patient’s sepsis risk within four hours – but while the clock ticks, the system identifies missing patient information, quantifies how essential it is, and gives a visual picture to clinicians of how specific information will affect the final risk prediction. Experiments using a combination of publicly available and proprietary patient data showed that adding 8% of the recommended data improved the system’s sepsis prediction accuracy by 11%.
The research was published August 24 in KDD ’24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining and will be presented orally Wednesday (August 28) at SIGKDD 2024 in Barcelona, Spain.
This work builds upon a previous machine learning model developed by senior study author Ping 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.
“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,” said first author Changchang Yin.
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. “
