An algorithmic approach to reducing unexplained pain disparities in underserved populations

Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors extern...

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Veröffentlicht in:	Nature medicine Jg. 27; H. 1; S. 136 - 140
Hauptverfasser:	Pierson, Emma, Cutler, David M., Leskovec, Jure, Mullainathan, Sendhil, Obermeyer, Ziad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Nature Publishing Group US 01.01.2021 Nature Publishing Group
Schlagworte:	631/114/1305 706/689 Aged Algorithms Arthritis Biomedical and Life Sciences Biomedical materials Biomedicine Cancer Research Confidence intervals Deep Learning Diagnosis Female Health Status Disparities Humans Infectious Diseases Knee Learning algorithms Machine learning Male Medical imaging Metabolic Diseases Middle Aged Molecular Medicine Neurosciences Osteoarthritis Osteoarthritis, Knee - diagnostic imaging Osteoarthritis, Knee - physiopathology Pain Pain - physiopathology Pain Measurement Patients Physicians Populations Race factors Race Factors - statistics & numerical data Racial differences Severity of Illness Index Socioeconomic Factors Stems Underserved populations Vulnerable Populations - statistics & numerical data United States
ISSN:	1078-8956, 1546-170X, 1546-170X
Online-Zugang:	Volltext
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