{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Davoudi A"],"funding":["National Institute of Neurological Disorders and Stroke","NIBIB NIH HHS","NIA NIH HHS","National Natural Science Foundation of China","NINDS NIH HHS","National Institute of Biomedical Imaging and Bioengineering"],"pagination":["970281"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9874861"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["4"],"pubmed_abstract":["<h4>Introduction</h4>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.<h4>Objective</h4>This study aimed to evaluate prediction bias in machine learning models used for predicting acute postoperative pain.<h4>Method</h4>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"],"journal":["Frontiers in digital health"],"pubmed_title":["Fairness in the prediction of acute postoperative pain using machine learning models."],"pmcid":["PMC9874861"],"funding_grant_id":["R01NS120924","R01 AG055337","R01 NS120924","K07 AG073468, R01 AG121647","R21 EB027344","1750192, R01EB029699, R21EB027344","R01 EB029699","K07 AG066813","K07 AG073468"],"pubmed_authors":["Hagen J","Ison R","Rashidi P","Sajdeya R","Price CC","Tighe PJ","Davoudi A"],"additional_accession":[]},"is_claimable":false,"name":"Fairness in the prediction of acute postoperative pain using machine learning models.","description":"<h4>Introduction</h4>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.<h4>Objective</h4>This study aimed to evaluate prediction bias in machine learning models used for predicting acute postoperative pain.<h4>Method</h4>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","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-03-15T13:49:36.043Z","creation":"2025-04-06T14:11:33.63Z"},"accession":"S-EPMC9874861","cross_references":{"pubmed":["36714611"],"doi":["10.3389/fdgth.2022.970281"]}}