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LIME results individualized patient factors with model probability and explanation of fit. The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high-risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality. The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high-risk cardiac surgery

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