A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study.

ABSTRACT:

Background

Anticoagulation therapy with heparin is a frequent treatment in intensive care units and is monitored by activated partial thromboplastin clotting time (aPTT). It has been demonstrated that reaching an established anticoagulation target within 24 hours is associated with favorable outcomes. However, patients respond to heparin differently and reaching the anticoagulation target can be challenging. Machine learning algorithms may potentially support clinicians with improved dosing recommendations.

Objective

This study evaluates a range of machine learning algorithms on their capability of predicting the patients' response to heparin treatment. In this analysis, we apply, for the first time, a model that considers time series.

Methods

We extracted patient dem

SUBMITTER: Boie SD 

PROVIDER: S-EPMC9614623 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature