Fairness in the prediction of acute postoperative pain using machine learning models.

ABSTRACT:

Introduction

Overall performance of machine learning-based prediction models is promising; however, their generalizability and fairness must be vigorously investigated to ensure they perform sufficiently well for all patients.

Objective

This study aimed to evaluate prediction bias in machine learning models used for predicting acute postoperative pain.

Method

We conducted a retrospective review of electronic health records for patients undergoing orthopedic surgery from June 1, 2011, to June 30, 2019, at the University of Florida Health system/Shands Hospital. CatBoost machine learning models were trained for predicting the binary outcome of low (≤4) and high pain (>4). Model biases were assessed against seven protected attributes of age, sex, race, area deprivation i

SUBMITTER: Davoudi A 

PROVIDER: S-EPMC9874861 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature