The online version contains supplementary material available at 10.1038/s41746-021-00385-9. Professor Marzyeh Ghassemi spoke about “Learning ‘Healthy’ Models in Machine Learning for Health” at the 2018 Machine Learning and the Market for Intelligence conference at the Rotman School of Management at the University of Toronto. Follow us by email Follow the tech’s Who’s Who directly from your inbox. Marzyeh Ghassemi’s research goal is to create novel machine learning approaches that can be used to improve healthcare delivery, understand what it means to be healthy, and quantify the impact of possible interventions. In this talk, Dr. Marzyeh Ghassemi will discuss the role of machine learning in health, argue that the demand for model interpretability is dangerous, and explain why models used in health settings must also be “healthy”. Block or report user Report or block mghassem. Marzyeh Ghassemi worries that AI which provides explanations for how it came to its decisions may make things worse rather than better. https://www.innovatorsunder35.com/the-list/marzyeh-ghassemi So she designed a suite of machine-learning methods to turn messy After collaborating with doctors in the intensive care unit at Beth Israel Deaconess Medical Center during her PhD studies, Marzyeh Ghassemi realized that one of their biggest challenges was information overload. Her research suggests that explainable AI is perceived to be more trustworthy even when it is in fact less accurate. For example, diseases in EHRs are poorly labeled, conditions can encompass … Unfolding physiological state: Mortality modelling in intensive care units. Marzyeh Ghassemi is an assistant professor at the University of Toronto in computer science and medicine, and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. PhD. Background: Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described. This session will cover some of the novel technical opportunities for machine learning in health challenges, and the important progress to be made with careful application to domain. Marzyeh Ghassemi is best known as a Mathematician. Learn more about blocking users. Go to: Contributor Information. Currently, many cancers don’t fit into the category of chronic conditions. Semantic Scholar profile for Marzyeh Ghassemi, with 39 highly influential citations and 47 scientific research papers. Marzyeh completed her PhD at MIT where her research focused on machine learning in health care, exploring how to predict immediate and long-term patient needs to inform decisions in the intensive care unit and ambulatory care. Effectiveness of N95 respirators versus surgical masks in protecting health care workers from acute respiratory infection: a systematic review and meta-analysis Her current research interests include clinical risk prediction with semi-supervised learning, optimal treatment discovery using expert demonstrations, and non-invasive Affiliate Scientist, LKS-CHART; Assistant Professor, Computer Science and Medicine, affiliated with the Vector Institute; Visiting Researcher with Google’s Verily The coolest things I've done in my career are: ‪Assistant Professor, Computer Science/Medicine, University of Toronto & Vector Institute‬ - ‪‪Cited by 2,288‬‬ - ‪Artificial Intelligence‬ - ‪Machine Learning‬ - ‪Interprettable Big Data Models‬ - ‪Clinical Inference‬ Marzyeh Ghassemi mghassem. Learn more about reporting abuse. Improving health requires targeting and evidence. Hide content and notifications from this user. Marzyeh Ghassemi is a Canada-based researcher in the field of computational medicine, where her research focuses on developing machine-learning algorithms to i… Learn more about Ghassemi’s research. Why aren't mistakes always a bad thing? They intend to ramp up the GEMINI data for COVID-19 research, by supporting an expansion of the number of hospitals contributing data, the type of data submitted, and the frequency with which it’s added. Her research focuses on using machine learning algorithms to leverage healthcare data to make better clinical decisions and to predict things like the length of patient hospital stays or whether they will need interventions such as blood transfusions or ventilators. Marzyeh tackles part of this puzzle with machine learning. Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. Ghassemi et al. Vector Faculty Member Marzyeh Ghassemi and her team received COVID-19 research funding through the CIHR Rapid Research Response program. AI for Healthcare session will feature Marzyeh Ghassemi who targets “Healthy ML” focusing on creating and applying machine learning to understand and improve health. Dr. Marzyeh Ghassemi Assistant Professor. These authors jointly supervised this work: Errol Colak, Marzyeh Ghassemi. Susanne Gaube, Email: ed.ru@ebuag.ennasus. In such settings, methods for differentially private (DP) learning provide a general-purpose approach to learn models with privacy guarantees. D. researcher focuses on machine learning with clinical data to predict and stratify relevant human risks. We are building an open database of COVID-19 cases with chest X-ray or CT images. Dr. Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. Block user. Along with Fidler, the campaign highlights physiologist Patricia Brubaker, cosmologist Renée Hložek, evolutionary biologist Maydianne Andrade, computational medical expert Marzyeh Ghassemi, hepatologist Mamatha Bhat and biomedical engineer Molly Shoichet – just a few of the university’s many award-winning women researchers in STEM. Marzyeh Ghassemi was born on June 6, 1985 in United States.. Marzyeh Ghassemi is one of the successful Mathematician. Marzyeh Ghassemi is a Visiting Researcher with Google’s Verily and a post-doc in the Clinical Decision Making Group at MIT’s Computer Science and Artificial Intelligence Lab … In International Conference on Knowledge Discovery and … However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. Marzyeh has ranked on the list of those famous people who were born on June 6, 1985.Marzyeh Ghassemi is one of the Richest Mathematician who was born in United States.Marzyeh Ghassemi also has a position … Biography. Share: Contact Support about this user’s behavior. [2014] Marzyeh Ghassemi, Tristan Naumann, Finale Doshi-Velez, N. Brimmer, Rohit Joshi, Anna Rumshisky, and Peter Szolovits. And what does AI have to do with that? She will be moving to MIT's EECS/IMES in July 2021. Marzyeh Ghassemi. About. Modern methods for DP learning ensure privacy through mechanisms that censor information judged as too unique. Marzyeh Ghassemi, PhD is an assistant professor of computer science and medicine at the University of Toronto and a faculty member at the Vector Institute, both in in Ontario, Canada. While Ghassemi’s project focuses on depression (and so far is looking only at a user’s location data), the same tools could be adapted for managing other chronic diseases such as diabetes or Chronic Obstructive Pulmonary Disease (COPD). Professor Marzyeh Ghassemi presents at AI for Good seminar series with her critical and thoughtful assessment of the current state and future potential of AI in healthcare By Izzy Pirimai Aguiar Professor Marzyeh Ghassemi empowered this week’s audience at the AI for Good seminar series with her critical and thoughtful assessment of the current state and future potential of AI in healthcare. Follow. Marzyeh Ghassemi is a Ph. AI for Healthcare Improving health requires targeting and evidence. The resulting … Machine learning models in health care are often deployed in settings where it is important to protect patient privacy. The growing data in EHRs makes healthcare ripe for the use of machine learning. She will focus on a progression of work that encompasses prediction, time series analysis, and representation learning. But the University of Toronto’s Marzyeh Ghassemi, an assistant professor in the Temerty Faculty of Medicine and in the department of computer science in the Faculty of Arts & Science, says that explainable AI may actually make matters worse, pointing to her own research that suggests explainable AI is perceived to be more trustworthy despite being less accurate. To view her full recorded talk, visit here. Harini Suresh, Email: ude.tim@hserush. Marzyeh Ghassemi, University of Toronto EPISODE SUMMARY Marzyeh Ghassemi, an assistant professor at the University of Toronto, is focused on Healthy ML—applying machine learning to understand and improve health. Marzyeh tackles part of this puzzle with machine learning. Marzyeh Ghassemi’s research goal is to create novel machine learning approaches that can be used to improve healthcare delivery, understand what it means to … Go to: Supplementary information. Ghassemi comes to us as a rising star out of MIT, having been named one of the Top 35 ‘Innovators under 35’.