This event is co-organized by the National University of Singapore (NUS), National University Health System (NUHS) and MIT Critical Data. We aim to bring together clinicians, data scientists and innovators in healthcare to address current problems in healthcare with data analytics technologies. The increasing wealth of patient data available through electronic health records has created a surge in research funding and industry interest in health data analytics. Challenges in extracting knowledge from health record databases, however, are significant.The event will strengthen cross-disciplinary collaboration around secondary analysis of electronic health record data, helping to pave the way for reliable knowledge to be translated into action, for the benefit of patients. It will be an event for students, engineers and scientists from Singapore and the region.
WHO SHOULD ATTEND?
Physicians, nurses, data scientists, data engineers, software engineers and students who are passionate about healthcare analytics, who are open to collaborate with like-minded people from a different discipline, who are energetic and fun!
WHAT WILL YOU LEARN?
The team will be addressing real clinical problems raised by participating physicians and nurses and will be generating solutions through analyzing de-identified real-world clinical data. Participants will be working in a cross-disciplinary team and will also be mentored by experienced scientists from NUS, MIT and other collaborating institutes.
Program in a Glance
18 July 2019: Technical Workshops
Four parallel technical/hands-on workshops on applications of AI in Healthcare
19 July 2019: Healthcare AI Seminar
One-day Healthcare AI technical seminar that will be featuring speakers from NUS, MIT, UIUC, AI Singapore, MSD, Google, Philips, Ping An (China), etc.
19-21 July 2019: Healthcare AI Datathon (NUS Saw Swee Hock School of Public Health)
A data-centric hackathon --- Datathon --- expecting 20 cross-disciplinary teams with a mixture of physicians, data scientists, students, entrepreneurs, etc. We have three different tracks this year:
Track 1: AI for Critical Care: supported by clinical data from over 200K ICU patients from US hosted on Google Cloud
Track 2: AI for General Care: supported by synthetic EMR data from NUH hosted on Google Cloud
Track 3: AI for Medical Imaging: supported by two image datasets hosted on both the DGX-1 machines from NSCC and TPU from Google Cloud