<![CDATA[Total blood lactate levels monitoring through the use of Artificial Intelligence in individuals that have undergone cardio surgeries is a milestone in critical care because it indicates metabolic problems earlier than traditional approaches. Lactate levels have to be significantly raised in order they may indicate complications like tissue hypoxia, sepsis or organ dysfunction. The previous method of monitoring lactate entails conducting tests after a few hours or days and can be very unresponsive; in the application of AI models, the algorithm scans through data acquired from patient monitoring systems to predict and advance notice the clinicians on the trends in lactate levels. This review outlines the basic mechanisms, algorithms, and features required to build an AI-based lactate predictor and the multiple physiologic signals such as heart rate, oxygen saturation, and blood pressure into the support vector regression model. Illustrative cases show that AI can facilitate more effective clinical decision-making to increase ICU patient safety and decrease such hospital stays. While AI based lactate tracking is something that has been bandied about in the research literature for some time, there are real questions as to how this is implemented in existing hospitals, how one minimizes the negative impacts of alarm fatigue, and how the results are persistent across population groups. Ethical and legal necessities concerning patient’s data confidentiality, security, and further reporting also play the vital role of its clinical endorsement. Other directions for future work are more flexible and multiple modality models that include additional data and require learning from new patient data.]]>
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