Interpretation of acid–base metabolism on arterial blood gas samples via machine learning algorithms
Habib Ozdemir,
Muhammed Ikbal Sasmaz,
Ramazan Guven
et al.
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.
Aim
This study aims to assess arterial blood gas evaluation using artificial intelligence al… Show more
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