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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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Detalhes bibliográficos
Publicado no:Sci Rep
Main Authors: Hsu, Yin-Chen, Weng, Hsu-Huei, Kuo, Chiu-Ya, Chu, Tsui-Ping, Tsai, Yuan-Hsiung
Formato: Artigo
Idioma:Inglês
Publicado em: Nature Publishing Group UK 2020
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7544690/
https://ncbi.nlm.nih.gov/pubmed/33033326
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-020-73776-9
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