Learning “Healthy” Models for Healthcare: What you can do
Education
On October 24, Dr. Marzyeh Ghassemi presented at City-Wide Medical Grand Rounds on machine learning in healthcare and the uses, advantages and pitfalls of machine learning in health and clinical practice. At the end of her talk, Dr. Ghassemi identified three areas Department of Medicine faculty and trainees can get involved:
Help identify targets for clinical machine learning (ML) that matters: Current ML research targets tasks that may not be actually useful! Help establish clinical opinions on existing machine learning targets, and suggest additional targets: https://goo.gl/forms/xEd9fcWcO80GuNJt1
Mentor a team in new project-based CS grad course: Create collaborations between technical and non-technical researchers, and consider the implications of machine learning in health. If you have a potential project with a) data that ML students could access, b) a supervisor for the Winter term and c) an interest in publishing the work with the student if it goes well! Topics in Machine Learning: Machine Learning for Health
Indicate interest in ML4H 2019 Unconference held in Toronto, Ontario: Invitational "unconference" style meeting in May 2019 to facilitate junior ML researchers and doctors connecting. Many projects in ML4H suffer from a mismatch in data, tools and skills. Our focus this year will be on What Problems Should ML4H Be Solving? https://goo.gl/forms/jzIvKaDpxfY0doYy2
