Cross-validation results of the machine learning model, LightGBM.

<p>Cross-validation results of the machine learning model, LightGBM.</p>

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Bibliographic Details
Main Author:	Hiroshi Hoshijima (17480570) (author)
Other Authors:	Tomo Miyazaki (17730516) (author), Yuto Mitsui (19429560) (author), Shinichiro Omachi (14717824) (author), Masanori Yamauchi (16985736) (author), Kentaro Mizuta (19429563) (author)
Published:	2024
Subjects:	Medicine Genetics Developmental Biology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified total infusion volume tohoku university hospital patient &# 8217 many applications due common adverse effect aged &# 8805 60 &# 8211 400 min ), potential risk factor total blood loss identifying risk factors used artificial intelligence anesthesia care unit adults </ p 548 adult patients identified risk factors model implementation used risk factors blood transfusion strongest contributor predicts probability postoperative nausea postoperative medical perioperative costs nursing records may correlate machine learning lumbar level longer stay lightgbm framework lateral positioning january 1 general anesthesia experienced nausea epidural anesthesia december 31 based identification anesthesia time 676 patients 20 years
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