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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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Библиографические подробности
Опубликовано в: :Sci Rep
Главные авторы: Hsu, Yin-Chen, Weng, Hsu-Huei, Kuo, Chiu-Ya, Chu, Tsui-Ping, Tsai, Yuan-Hsiung
Формат: Artigo
Язык:Inglês
Опубликовано: Nature Publishing Group UK 2020
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Online-ссылка: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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