Elizabeth Flott, Sarah Ball, Jacquie Hanks, Margo Minnich, Amanda Kirkpatrick, Lori Rusch, Dana Koziol, Ann Laughlin, Jacqueline Williams
Fostering collaborative learning and leadership through near-peer mentorship among undergraduate nursing students
Nursing forum
overall performance machine learningbased prediction models promising however generalizability fairness must vigorously investigated ensure perform sufficiently well patients study aimed evaluate prediction bias machine learning models used predicting acute postoperative pain 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 4 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 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 findings show need evaluate fairness machine learning models involved perioperative pain implemented clinical decision support tools