Intensive care units (ICUs) in the United States admit over five million patients annually, with mortality rates ranging from 10% to 29%. Given that a patient’s condition can change rapidly, early detection of deterioration is crucial for enabling timely interventions and improving outcomes. While existing systems provide some guidance, they are often manual and offer only a single snapshot of a patient’s health. Recent AI models have been developed to provide automated, real-time assessments, yet many models are limited and only provide an incomplete picture of a patient’s clinical status.
Miguel Contreras, a Ph.D. student in the Rashidi Lab, is the first author of a newly published study in Nature Communications that introduces a real-time predictive model designed to support patient care in the ICU.
Read more about UF BME and COM Researchers Publish Innovative ICU Prediction Model in Nature Communications.
