HIMSS18 Annual Conference
Wynn Las Vegas
Mar. 5, 2018

Zeeshan Syed

Director of the Clinical Inference and Algorithms Program
Stanford Health Care

Zeeshan Syed is the inaugural Director of the Clinical Inference and Algorithms Program at Stanford Health Care and a Clinical Associate Professor at the Stanford University School of Medicine. Before joining Stanford in 2016, Dr. Syed was an Associate Professor with Tenure in Computer Science and Engineering at the University of Michigan, where he was a Principal Investigator for the Artificial Intelligence Laboratory and led the Computational Biomarker Discovery and Clinical Inference Group. Dr. Syed received SB and MEng degrees in Electrical Engineering and Computer Science at MIT, and a PhD through a joint program between MIT’s School of Engineering and Harvard Medical School in Computer Science and Biomedical Engineering. Dr. Syed’s research investigates the design and application of advanced healthcare-specialized machine learning and artificial intelligence technologies for clinical effectiveness, high-value care and population health, and has featured at top machine learning and artificial intelligence conferences (NIPS, ICML, AAAI, KDD) as well as in the media (Wired, CBS, NPR, WSJ, Technology Review, ZDNet). Dr. Syed is the recipient of multiple national awards for his scholarship activities, including the prestigious CAREER award from the National Science Foundation. Dr. Syed is also actively engaged with the healthcare analytics industry, having been part of the core early-stage team for the Google[X] Life Sciences initiative (now Verily) and as a founder of HEALTH[at]SCALE Technologies.

March 5, 2018
4:10pm - 4:35pm
Mouton 2

Personalized medicine and machine learning are two of the hottest topics in healthcare, and no event at HIMSS would be complete without offering a perspective on their potential to transform healthcare. In this session, attendees will learn how the cloud offers a great opportunity to maximize their combined potential to drive value by improving outcomes.

Zeeshan Syed, founder of Health at Scale and director and clinical associate professor at Stanford Medicine, will discuss the use of the public cloud's development capabilities for iterative design of powerful machine learning systems and the execution of massive-scale experiments for machine-based learning.

As a case study, Zahoor Elahi, senior vice president of Optum, will discuss how UnitedHealthcare leveraged Health at Scale's machine-learning cloud platform to coordinate care for 40 million members and proactively refer patients to individually-optimal providers within complex care networks.

Key takeaways:

  • As more clinical and claims data becomes available, cloud-based machine learning allows for continuous refinement of predictive algorithms.
  • Cloud deployment allows modular machine learning algorithms to be updated and improved via input from a multi-disciplinary team of stakeholders.
  • Cloud computing democratizes machine learning and makes it accessible to different stakeholders in the health care system.
  • Running parallel experiments at scale allows machine learning to iteratively get smarter about predicting optimal providers for specific patients.

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