A machine learning model can identify patients at high risk for developing hospital-acquired Clostridioides difficile infection (HA-CDI) and help guide potential early preventive measures. Although the model yields more false positives than rectal swabbing surveillance, researchers believe that combining the two modalities could limit C. difficile colonization in the hospital and subsequent HA-CDI.
“Overall, the machine learning model enables us to have another path toward CDI surveillance