Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities
Abstract The machine learning community has become alert to the ways that predictive algorithms can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts of algorithmic fairness might apply in healthcare, where predictive algorithms are being increasingly used to sup...
Guardado en:
Autores principales: | Jessica K. Paulus, David M. Kent |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e7bd8bf7145c42bc85f9bf7ab9b5b39e |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
por: Supreeth P. Shashikumar, et al.
Publicado: (2021) -
Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
por: Sonu Subudhi, et al.
Publicado: (2021) -
New machine learning model predicts who may benefit most from COVID-19 vaccination
por: Leia Wedlund, et al.
Publicado: (2021) -
A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients
por: Naveena Yanamala, et al.
Publicado: (2021) -
Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance
por: Nina Rank, et al.
Publicado: (2020)