Contributing variables for predictive performance in (A) first and (B) multiple heparin infusion patients.

<p>The contributions to the prediction model were calculated as follows. For the first and multiple heparin infusion patient prediction, the random forest model with data imputed based on static data was used. aPTT: activated partial thromboplastin time; BHC after SA: blood heparin concentrati...

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Автор:	Tadashi Kamio (16820275) (author)
Інші автори:	Masaru Ikegami (1332699) (author), Megumi Mizuno (21755733) (author), Seiichiro Ishii (21755736) (author), Hayato Tajima (4675363) (author), Yoshihito Machida (16820278) (author), Kiyomitsu Fukaguchi (19065995) (author)
Опубліковано:	2025
Предмети:	Biochemistry Biological Sciences not elsewhere classified Information Systems not elsewhere classified unfractionated heparin administration tokushukai medical database roc auc values received unfractionated heparin precision &# 8211 intensive care units explore additional factors covering six hospitals multiple heparin doses highest predictive performance improve predictive models highest classification accuracy xlink "> data train ml models xlink "> series data multiple infusions vital signs study aimed significant challenge model performance marginal improvements machine learning incorporating time icu patients future studies first infusion f1 scores f1 score evaluated using dynamic features dataset comprised
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Contributing variables for predictive performance in (A) first and (B) multiple heparin infusion patients.

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