Development of a Machine Learning–Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma

IMPORTANCE: The use of artificial intelligence (AI) in clinical medicine risks perpetuating existing bias in care, such as disparities in access to postinjury rehabilitation services. OBJECTIVE: To leverage a novel, interpretable AI-based technology to uncover racial disparities in access to postinj...

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Veröffentlicht in:	Archives of surgery (Chicago. 1960) 2023-10, Vol.158 (10), p.1088-1095
Hauptverfasser:	Gebran, Anthony, Thakur, Sumiran S, Maurer, Lydia R, Bandi, Hari, Sinyard, Robert, Dorken-Gallastegi, Ander, Bokenkamp, Mary, El Moheb, Mohamad, Naar, Leon, Vapsi, Annita, Daye, Dania, Velmahos, George C, Bertsimas, Dimitris, Kaafarani, Haytham M. A
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Bias Black or African American - statistics & numerical data Female Health care Health care access Health disparities Health Services Accessibility - statistics & numerical data Healthcare Disparities - ethnology Humans Machine Learning Male Middle Aged Online First Original Investigation Patient Discharge - statistics & numerical data United States White - statistics & numerical data Wounds, Penetrating - ethnology Wounds, Penetrating - therapy
Online-Zugang:	Volltext
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