Interpretation of acid-base metabolism on arterial blood gas samples via machine learning algorithms.

Journal: Irish journal of medical science

PMID: 39088159

Abstract

BACKGROUND: Arterial blood gas evaluation is crucial for critically ill patients, as it provides essential information about acid-base metabolism and respiratory balance, but evaluation can be complex and time-consuming. Artificial intelligence can perform tasks that require human intelligence, and it is revolutionizing healthcare through technological advancements.

Authors

Habib Ozdemir

Department of Medical Biochemistry, Faculty of Medicine, Manisa Celal Bayar University, Manisa, Türkiye.
Muhammed Ikbal Sasmaz

Faculty of Medicine, Department of Emergency Medicine, Manisa Celal Bayar University, Manisa, Türkiye.
Ramazan Guven

Department of Emergency Medicine, Istanbul Basaksehir Cam and Sakura City Research and Training Hospital, Health Science University, Istanbul, Türkiye.
Akkan Avci

Department of Emergency Medicine, Adana City Research and Training Hospital, Health Science University, Adana, 01060, Türkiye. drakkanavci@gmail.com.

Keywords

Acid-Base Equilibrium Algorithms Blood Gas Analysis Humans Machine Learning Retrospective Studies ROC Curve Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (39088159)

Interpretation of acid-base metabolism on arterial blood gas samples via machine learning algorithms.

Abstract

Authors

Keywords

External Resources

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