MACHINE LEARNING & AI: A HIMSS EVENT
LAS VEGAS, NV - MARCH 5, 2018
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.
Machine Learning for healthcare differs significantly from other application domains in terms of the use cases, data resources, operational constraints and evaluation metrics. Despite these differences, the majority of existing machine learning solutions for healthcare applications remain grounded in conventional methodologies that are re-purposed from unrelated application domains.
This session addresses the specific characteristics unique to healthcare that distinguish it from other application domains and creates the need for specialized solutions. It will also discuss the requirements created by these characteristics and how healthcare-focused machine learning technologies should meet these requirements to maximize practical impact.
Key discussion points:
There’s a ton of buzz around machine learning and artificial intelligence and the role they’ll play in revolutionizing and improving healthcare, but what are these evolving technologies, how are they different, and what are the multiple layers of use?
No complex health IT ever created has been plug-and-play. The same goes for ML and AI. To take full advantage of its potential, will require a lot of work.
In this morning leadership panel, our speakers look at the current state of machine learning and AI in healthcare and address where we are, where we are going, and what we need to do to get their faster.
What education do stakeholders need? What new vocabulary is required? How do you integrate ML and AI into operational and clinical processes? How do you show ROI?
Our speakers will address these questions and others head-on in a fascinating and insightful discussion that sets the stage for the speakers to follow.