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Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery.

Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insight about the pathophysiology and prognosis of neurological conditions. This chapter provides a guide for clinicians on relevant aspects of machine learning and reviews selected application of these methods in intramedullary spinal cord tumors. The potential areas of application of machine learning extend far beyond the analyses of clinical data to include several areas of artificial intelligence, such as genomics and computer vision. Integration of various sources of data and application of advanced analytical approaches could improve risk assessment for intramedullary tumors.

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Group 7SearchHeart failure treatmentPapersTopicsCollectionsEffects of Sodium-Glucose Cotransporter 2 Inhibitors for the Treatment of Patients With Heart Failure Importance: Only 1 class of glucose-lowering agents-sodium-glucose cotransporter 2 (SGLT2) inhibitors-has been reported to decrease the risk of cardiovascular events primarily by reducingSeptember 1, 2017: JAMA CardiologyAssociations of albuminuria in patients with chronic heart failure: findings in the ALiskiren Observation of heart Failure Treatment study.CONCLUSIONS: Increased UACR is common in patients with heart failure, including non-diabetics. Urinary albumin creatininineJul, 2011: European Journal of Heart FailureRandomized Controlled TrialEffects of Liraglutide on Clinical Stability Among Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial.Review

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