Summary
This paper presents management algorithms powered by artificial intelligence systems designed to support clinical decision-making during cardiopulmonary bypass, specifically for optimising metabolic management. The authors describe the development of algorithms to identify optimal approaches for assessing metabolic parameters and note that similar systems are already in clinical use. The stated goal is to reduce operator error and improve the consistency of metabolic management during extracorporeal procedures.
Regional applicability
This work is clinical in nature and concerns cardiopulmonary bypass management technology; it does not directly address farming systems, soil health, nutrient density, or dietary nutrition relevant to Pulse Brain's core scope.
Key measures
Metabolic parameters assessed during extracorporeal procedures; algorithm guidance for metabolic strategy selection
Outcomes reported
The paper describes the development and application of management algorithms interfaced with metabolic monitoring systems to guide operators in selecting optimal metabolic strategies during cardiopulmonary bypass procedures. The algorithms aim to reduce human error and optimise clinical management decisions.
Topic tags
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