A proposed artificial intelligence tool to support clinician decision-making about hospital patients at risk for sepsis has an unusual feature: It can account for its lack of certainty and suggest 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 existing model represents a more a traditional human-AI competition paradigm, generating numerous annoying false alarms in ICUs and emergency rooms without listening to clinicians," said senior study author Ping Zhang, associate professor of computer .