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

ABSTRACT:

Objectives

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 unfractionated heparin for anticoagulation and to identify key predictive factors.

Methods

Data were obtained from the Tokushukai Medical Database, covering six hospitals with ICUs in Japan, collected between 2018 and 2022. The study included 945 ICU patients who received unfractionated heparin. The dataset comprised both static and dynamic features, which were used to construct and train ML models. Models were developed to predict aPTT following initial and multiple heparin doses. Model performance was evaluate

SUBMITTER: Kamio T 

PROVIDER: S-EPMC12279130 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature