Hey Copenhageners, I’m happy to announce the first talk in a long, long while.
We’re extremely lucky to have Dr. Leo Anthony Celi from MIT kick things off with a talk about his work with Machine Learning for health care. Talk details are below – and if you keep reading, there’s also more info about Leo’s incredibly impressive resume and ongoing work (read more here).
- Time: Wednesday August 25th, 13:30.
- Location: Technical University of Denmark, Building 321 ,1st floor lab-space
- Title: Ensuring machine learning for healthcare works for all
- Abstract: The gaps in the medical knowledge system stem from the systematic exclusion of the majority of the world’s population from health research. These gaps combined with implicit and explicit biases lead to suboptimal medical decision making which negatively impact health outcomes for everyone, but especially those in groups typically under-represented in health research. Recent developments in machine learning and AI technologies hold some promise to address the issues with the generation of scientific evidence and human decision making. They also, however, have spurred concerns about their potential to maintain if not exacerbate these problems. These concerns must be aggressively addressed by adopting necessary structural reforms to ensure that the field is both equitable and ethical by design.
Affiliations: Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Department of Medicine, Beth Israel Deaconess Medical Center, Department of Biostatistics, Harvard T.H. Chan School of Public Health
Short bio: As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP), and as a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC), Leo brings together clinicians and data scientists to support research using data routinely collected in the process of care. His group built and maintains the publicly-available Medical Information Mart for Intensive Care (MIMIC) database and the Philips-MIT eICU Collaborative Research Database, with more than 20,000 users from around the world. In addition, Leo is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. “Secondary Analysis of Electronic Health Records” has been downloaded more than a million times, and has been translated to Mandarin, Spanish and Korean. He is the inaugural editor of PLOS Digital Health.
You can read more about Leo’s amazing work below: