[HTML][HTML] Evaluation and mitigation of racial bias in clinical machine learning models: scoping review
Background Racial bias is a key concern regarding the development, validation, and
implementation of machine learning (ML) models in clinical settings. Despite the potential of …
implementation of machine learning (ML) models in clinical settings. Despite the potential of …
Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review: Scoping review examines racial and ethnic bias in clinical …
In August 2022 the Department of Health and Human Services (HHS) issued a notice of
proposed rulemaking prohibiting covered entities, which include health care providers and …
proposed rulemaking prohibiting covered entities, which include health care providers and …
Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia
Objective: Child undernutrition is a global public health problem with serious implications. In
this study, we estimate predictive algorithms for the determinants of childhood stunting by …
this study, we estimate predictive algorithms for the determinants of childhood stunting by …
Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb …
AB Bagheri, MD Rouzi, NA Koohbanani… - Seminars in Vascular …, 2023 - Elsevier
Chronic limb-threatening ischemia (CLTI) is the most advanced form of peripheral artery
disease. CLTI has an extremely poor prognosis and is associated with considerable risk of …
disease. CLTI has an extremely poor prognosis and is associated with considerable risk of …
[HTML][HTML] Predicting falls in long-term care facilities: machine learning study
Background Short-term fall prediction models that use electronic health records (EHRs) may
enable the implementation of dynamic care practices that specifically address changes in …
enable the implementation of dynamic care practices that specifically address changes in …
Improving child health through Big Data and data science
ZA Vesoulis, AN Husain, FS Cole - Pediatric research, 2023 - nature.com
Child health is defined by a complex, dynamic network of genetic, cultural, nutritional,
infectious, and environmental determinants at distinct, developmentally determined epochs …
infectious, and environmental determinants at distinct, developmentally determined epochs …
Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models
Objectives Leveraging artificial intelligence (AI) in conjunction with electronic health records
(EHRs) holds transformative potential to improve healthcare. However, addressing bias in …
(EHRs) holds transformative potential to improve healthcare. However, addressing bias in …
Ethical redress of racial inequities in AI: lessons from decoupling machine learning from optimization in medical appointment scheduling
Abstract An Artificial Intelligence algorithm trained on data that reflect racial biases may yield
racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms …
racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms …
Machine learning and artificial intelligence: applications in healthcare epidemiology
AJ Hamilton, AT Strauss, DA Martinez… - Antimicrobial …, 2021 - cambridge.org
Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily
associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the …
associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the …
[HTML][HTML] Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis
HE Wang, JP Weiner, S Saria, H Kharrazi - Journal of medical Internet …, 2024 - jmir.org
Background The adoption of predictive algorithms in health care comes with the potential for
algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been …
algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been …