Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.

Journal: BMC medical informatics and decision making
PMID: 40128792

Abstract

BACKGROUND: Acute upper gastrointestinal bleeding (UGIB) is common in clinical practice and has a wide range of severity. Along with medical therapy, endoscopic intervention is the mainstay treatment for hemostasis in high-risk rebleeding lesions. Predicting the need for endoscopic intervention would be beneficial in resource-limited areas for selective referral to an endoscopic center. The proposed risk stratification scores had limited accuracy. We developed a machine learning model to predict the need for endoscopic intervention in patients with acute UGIB.

Authors

  • Kajornvit Raghareutai
    Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
  • Watcharaporn Tanchotsrinon
    Siriraj Informatics and Data Innovation Center, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Onuma Sattayalertyanyong
    Siriraj GI endoscopy Center, Siriraj Hospital, Bangkok, Thailand.
  • Uayporn Kaosombatwattana
    Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand. uayporn.kao@mahidol.ac.th.

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