Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models.

Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models.

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<h4>Objectives</h4>Predicting optimal coagulation control using heparin in intensive care units (ICUs) remains a significant challenge. This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU patients receiving unfractionat...

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Bibliographic Details
Main Authors: Tadashi Kamio, Masaru Ikegami, Megumi Mizuno, Seiichiro Ishii, Hayato Tajima, Yoshihito Machida, Kiyomitsu Fukaguchi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0328709
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https://doi.org/10.1371/journal.pone.0328709

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