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DAVID HODGSON, MD, MPH, FRCPC University of Toronto MiCHAMP / IHPI SEMINAR Friday, May 4, 2018 | 2:30-3:30 PM NCRC B10 G063 Change has its Enemies: Opportunities and Barriers in the Adoption of Machine Learning Methods in Oncology Bio & event info DANIELLE RODIN, MD, MPH, FRCPC University of Toronto CHOP / IHPI SEMINAR…
Every year nearly 30,000 Americans die from an aggressive, gut-infecting bacteria called Clostridium difficile (C. difficile), which is resistant to many common antibiotics and can flourish when antibiotic treatment kills off beneficial bacteria that normally keep it at bay. Investigators from Massachusetts General Hospital (MGH), the University of Michigan (U-M) and Massachusetts Institute of Technology (MIT) now…
See the full announcement in The University Record
JOHN P.A. IOANNIDIS, MD, DSc Stanford University PROFESSOR of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics C.F. REHNBORG CHAIR in Disease Prevention CO-DIRECTOR of Meta-Research Innovation Center DIRECTOR of the PhD program in Epidemiology and Clinical Research The Power of Bias and What to do About it MIP/DCMB/MIDAS SEMINAR Wednesday, April 18,…
In November of 2017, the Leadership Consortium for a Value & Science-Driven Health System of the National Academy of Medicine met in Washington D.C. to discuss the future of artificial intelligence and machine learning in health and health care. Follow the link to read the briefing in full.
Today’s hospitals are collecting an immense amount of patient data. My group aims to develop the machine learning tools needed to detect patterns in these data that can help inform clinical decisions, leading to improved patient care.
The goal of Kayvan Najarian, Ph.D.’s research, funded by a grant from the American Heart Association (AHA), is to exploit recent advancements in signal processing and machine learning algorithms to construct a fully automated, computer-based platform — AngioAid — that efficiently assists cardiologists with coronary angiogram video evaluation. Preliminary work by the group suggests such…
When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of patients may adversely affect the performance of the algorithm. For example, even clinical experts may have less confidence when assigning a medical diagnosis to some patients because of ambiguity in the patient’s case or imperfect…
Dr. Jenna Wiens’ research on machine learning and Clostridium difficile was recently featured by the Washington Post. Jenna Wiens Photo: Joseph Xu/Senior Multimedia Content Producer, University of Michigan – College of Engineering
In partnership with the U-M Department of Computational Medicine & Bioinformatics, and the Michigan Integrated Center for Health Analytics & Medical Prediction, we’re excited to welcome Steffen Leonhardt, MD, PhD to Michigan! His talk is titled: “Thou shalt not touch – Nonobtrusive and Noncontact Monitoring Techniques for Medical Applications”. This talk is co-sponsored by MCIRCC,…