Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study.

ABSTRACT:

Background

Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations.

Objective

The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, an

SUBMITTER: Su L 

PROVIDER: S-EPMC7338927 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature