Development of a machine learning model to predict intraoperative transfusion and guide type and screen ordering.

Study Objective: To develop an algorithm to predict intraoperative Red Blood Cell (RBC) transfusion from preoperative variables contained in the electronic medical record of our institution, with the goal of guiding type and screen ordering.

Design: Machine Learning model development on retrospective single-center hospital data.

Setting: Preoperative period and operating room.

Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients.

Aims: There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.

Methods And Results: De-identified patient data were obtained from Vanderbilt University Medical Center.