Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction

BACKGROUND: Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the systematic changes that occur in the distribution of data. In this paper, we study the ext...

Descripción completa

Detalles Bibliográficos
Autores principales: Rahmani, Keyvan, Thapa, Rahul, Tsou, Peiling, Chetty, Satish Casie, Barnes, Gina, Lam, Carson, Tso, Chak Foon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196120/
https://www.ncbi.nlm.nih.gov/pubmed/35702157
http://dx.doi.org/10.1101/2022.06.06.22276062

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196120/
https://www.ncbi.nlm.nih.gov/pubmed/35702157
http://dx.doi.org/10.1101/2022.06.06.22276062

Ejemplares similares