The Machine Learning for Health group (ML4H@cs.toronto) targets "Healthy ML", focusing on creating applying machine learning to understand and improve health.

We believe that health is important, and improvements in health improve lives. However, we still don't fundamentally understand what it means to be healthy, and the same patient may receive different treatments across different hospitals or clinicians as new evidence is discovered, or individual illness is interpreted. Health is unlike many success stories in machine learning so far - games like Go and self-driving cars - because we do not have well-defined goals that can be used to learn rules. The nuance of health also requires that we keep machine learning models "healthy" - working to ensure that they do not learn biased rules or detrimental recommendations.

Improving health requires targeting and evidence – our group tackles part of this puzzle with machine learning. There are many novel technical opportunities for machine learning in health challenges, and important progress to be made with careful application to domain.

Read more about our Research Directions, Publications, and Joining ML4H.

Recent Talks

• Opening Keynote lecture at DALI Healthcare in Machine Learning workshop, Jan 5, 2019
• 3 minute Innovator Spotlight for MIT Tech Review 35 Under 35 Award on Sept 12, 2018
• Elevate AI Panelist with Jimmy Ba, Jordan Jacobs, and Inmar Givoni on September 26, 2018
• Syndemics Workshop Panelist at Public Health Ontario with Laura Rosella on September 26, 2018
• Invited Talk at the Fields Institute on September 27, 2018
• Invited keynote at Machine Learning and the Market for Intelligence with Russ Salakhutdinov, Geoff Hinton, and Ilya Sutskever on October 23, 2018.
• Invited talk at City-wide Medical Grand Rounds on October 24, 2018.
• Panelist at Techna 2018: Enabling AI In Healthcare on Nov 2, 2018.