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Deeptankar DeMazumder – Automated Bayesian Analysis of Physiological Waveforms Adds Major Independent Prognostic Value to Conventional Clinical Practice (AI PHI Affinity Group)
March 1 @ 9:00 am - 10:00 amFree
Automated Bayesian Analysis of Physiological Waveforms Adds Major Independent Prognostic Value to Conventional Clinical Practice
This talk will describe Dr. DeMazumder’s longstanding goals to transform clinical observations into testable research hypotheses, translate basic research findings into medical advances, and evaluate personalized treatment protocols in rigorous clinical trials, while caring for and improving the quality of life in patients with heart rhythm disorders. He plans to continue to participate in mentoring future physicians and biomedical researchers. This work integrates basic mechanistic studies of stress-induced alterations in oxidative stress and heart-brain signaling, and complementary translational research using novel machine learning algorithms in multicenter clinical studies, aimed at early diagnosis and treatment of critical illness.
Presented by Deeptankar DeMazumder
Dr. DeMazumder works as an attending Physician in Clinical Cardiac Electrophysiology at the Veterans Administration Pittsburgh Health System (VAPHS), an Associate Professor at the McGowan Institute for Regenerative Medicine, the Department of Surgery, and the Department of Internal Medicine (Cardiology) at the University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center. Dr. DeMazumder’s research has been supported by the NIH (e.g., NHLBI K99, R00, U54 and the NIH Director’s New Innovator DP2 awards), two Department of Defense grants, an award from the Leducq Foundation, and five grants from the American Heart Association (AHA), including the AHA Transformational Project Award and four awards on Artificial Intelligence and Machine Learning.
Artificial Intelligence in Cancer Research – AI PHI Affinity Group
(First Friday of each month)
This group was formed to discuss the current trends and applications of artificial intelligence in cancer research and clinical practice. The group brings together AI researchers in a variety of fields (computer science, engineering, nutrition, epidemiology radiology, etc) with clinicians and advocates. Students, trainees and faculty with any or no background in AI are encouraged to attend. The goal is to foster collaborative interactions to solve problems in cancer that were thought to be unsolvable a decade ago before the broad use of deep learning and AI in medicine.