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Healthcare Disparities

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Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Epilepsia
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluation...

Teaching yourself about structural racism will improve your machine learning.

Biostatistics (Oxford, England)
In this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for gener...

Deep transfer learning for reducing health care disparities arising from biomedical data inequality.

Nature communications
As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. H...

Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning.

Journal of the American Medical Informatics Association : JAMIA
Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require a...

Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.

Journal of the American Medical Informatics Association : JAMIA
The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease,...