Machine learning in cardiac surgery: a narrative review

CONCLUSIONS: Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.PMID:38738250 | PMC:PMC11087616 | DOI:10.21037/jtd-23-1659
Source: Journal of Thoracic Disease - Category: Respiratory Medicine Authors: Source Type: research