Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study

As the performance of current fall risk assessment tools is limited, clinicians face significant challenges in identifying patients at risk of falling. This study proposes an automatic fall risk prediction model based on eXtreme gradient boosting (XGB), using a data-driven approach to the standardiz...

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Detalles Bibliográficos
Autores principales: Hsu, Yin-Chen, Weng, Hsu-Huei, Kuo, Chiu-Ya, Chu, Tsui-Ping, Tsai, Yuan-Hsiung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544690/
https://www.ncbi.nlm.nih.gov/pubmed/33033326
http://dx.doi.org/10.1038/s41598-020-73776-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544690/
https://www.ncbi.nlm.nih.gov/pubmed/33033326
http://dx.doi.org/10.1038/s41598-020-73776-9

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