PT - JOURNAL ARTICLE AU - Riccardo Levi AU - Francesco Carli AU - Aldo Robles Arévalo AU - Yuksel Altinel AU - Daniel J Stein AU - Matteo Maria Naldini AU - Federica Grassi AU - Andrea Zanoni AU - Stan Finkelstein AU - Susana M Vieira AU - João Sousa AU - Riccardo Barbieri AU - Leo Anthony Celi TI - Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding AID - 10.1136/bmjhci-2020-100245 DP - 2021 Jan 01 TA - BMJ Health & Care Informatics PG - e100245 VI - 28 IP - 1 4099 - http://informatics.bmj.com/content/28/1/e100245.short 4100 - http://informatics.bmj.com/content/28/1/e100245.full SO - BMJ Health Care Inform2021 Jan 01; 28 AB - Objective Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU.Methods A machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates.Results The optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all.Conclusions The potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.