Anis Davoudi|Ruba Sajdeya|Ron Ison|Jennifer E. Hagen|Parisa Rashidi|Catherine C. Price|Patrick Tighe
Fairness in the prediction of acute postoperative pain using machine learning models
Florida College|University of Florida|Florida College|University of Florida|Florida College|University of Florida|Florida College|University of Florida|University of Florida|Florida College|University of Florida|Florida College|University of Florida
introduction overall performance machine learningbased prediction models promising however generalizability fairness must vigorously investigated ensure perform sufficiently well patients objective study aimed evaluate prediction bias machine learning models used predicting acute postoperative pain method conducted retrospective review electronic health records patients undergoing orthopedic surgery june 1 2011 june 30 2019 university florida health systemshands hospital catboost machine learning models trained predicting binary outcome low 4 high pain ampgt4 model biases assessed seven protected attributes age sex race area deprivation index adi speaking language health literacy insurance type reweighing protected attributes investigated reducing model bias compared base models fairness metrics equal opportunity predictive parity predictive equality statistical parity overall accuracy equality examined results final dataset included 14263 patients age 6072 1603 years 5387 female 3913 low acute postoperative pain machine learning model area curve 071 biased terms age race adi insurance type terms sex language health literacy despite promising overall performance predicting acute postoperative pain machine learningbased prediction models may biased respect protected attributes conclusion findings show need evaluate fairness machine learning models involved perioperative pain implemented clinical decision support tools