Machine Learning Reimagined: The Promise of Interpretability to Combat Bias
Author:
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Surgery
Reference5 articles.
1. Implementing machine learning in health care–ddressing ethical challenges;Char;N EnglJ Med,2018
2. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator;Bertsimas;Ann Surg,2018
3. Racial disparities in post-discharge healthcare utilization after trauma;Fat;Am J Surg,2019
4. Machine learning and health care disparities in dermatology;Adamson;JAMA Dermatol,2018
5. Dissecting racial bias in an algorithm used to manage the health of populations;Obermeyer;Science,2019
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1. Can Artificial Intelligence Improve the Appropriate Use and Decrease the Misuse of REBOA?;2024-06-07
2. Use of artificial intelligence for nonlinear benchmarking of surgical care;Surgery;2023-12
3. Development of a Machine Learning–Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma;JAMA Surgery;2023-10-01
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