Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.
Journal:
BMC medical informatics and decision making
Published Date:
Jul 24, 2025
Abstract
BACKGROUND: In the Intensive Care Unit (ICU), data stored in patient data management systems (PDMS) is commonly used in clinical practice and research. Parameters from point-of-care arterial blood gas (BG) analysis are used in the diagnosis and definition of syndromes such as sepsis and ARDS, but manual entry of the blood source (arterial or venous) into the PDMS introduces the risk of mislabeling venous samples as arterial. Our study aimed to employ supervised machine learning to accurately identify blood gas samples as arterial or venous using PDMS data.
