Maharaja Agrasen Institute of Technology (MAIT)
Ashish Khanna focuses on Internal medicine, Artificial intelligence, Intensive care unit, Angiotensin II and Endocrinology. Ashish Khanna frequently studies issues relating to Ex vivo and Internal medicine. He has included themes like Machine learning, Parkinson's disease and Pattern recognition in his Artificial intelligence study.
Ashish Khanna has researched Intensive care unit in several fields, including Cohort study, Sepsis, Mechanical ventilation, Retrospective cohort study and Intensive care. His work deals with themes such as 2019-20 coronavirus outbreak, Virology and Pneumonia, which intersect with Angiotensin II. His work in the fields of Endocrinology, such as Dipeptidyl peptidase-4 and Metabolite, intersects with other areas such as Saxagliptin.
His scientific interests lie mostly in Internal medicine, Anesthesia, Artificial intelligence, Surgery and Intensive care medicine. He interconnects Endocrinology, Noncardiac surgery and Cardiology in the investigation of issues within Internal medicine. His work on Hypoxemia and Anesthesiology as part of general Anesthesia research is frequently linked to Mean arterial pressure, thereby connecting diverse disciplines of science.
The various areas that Ashish Khanna examines in his Artificial intelligence study include Machine learning and Pattern recognition. His research investigates the connection with Blood pressure and areas like Shock which intersect with concerns in Septic shock. His Angiotensin II study integrates concerns from other disciplines, such as Renin–angiotensin system and Vasodilation.
Ashish Khanna spends much of his time researching Anesthesia, Internal medicine, Artificial intelligence, Emergency medicine and Anesthesiology. His studies deal with areas such as Angiotensin II, Depression, Critically ill and Opioid as well as Anesthesia. His Angiotensin II research integrates issues from Vasoplegia and Shock.
His Internal medicine study deals with Cardiology intersecting with Noncardiac surgery. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition. The Anesthesiology study which covers MEDLINE that intersects with Collegiality.
Anesthesia, Confidence interval, Intensive care medicine, Intensive care unit and Mechanical ventilation are his primary areas of study. His Anesthesia study incorporates themes from Angiotensin II, Shock and Critically ill. His study focuses on the intersection of Angiotensin II and fields such as Cardiopulmonary bypass with connections in the field of Blood pressure.
His Confidence interval study improves the overall literature in Internal medicine. The Intensive care medicine study combines topics in areas such as Microcirculation and Resuscitation. His Intensive care unit study integrates concerns from other disciplines, such as Odds ratio and Emergency medicine.
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