DoC-IT Visiting Speaker: Zeeshan Syed, PhD

Machine Intelligence for Healthcare Hyper-Personalization
-

Event Banner

Hybrid Event
Light refreshments will be provided.

Email [email protected] for the registration information.

About the Event

The burden of many diseases remains high despite available treatment options. Two causes of this are challenges in choosing treatments (i.e., failure to diagnose and match patients to treatments that are appropriate for their individual risks) and delivering treatments (i.e., failure to deliver chosen treatments in a manner optimized for each individual). Advances in machine intelligence offer significant potential to address these challenges and enable personalization in diagnosis, treatment selection, and care delivery.

In this talk, Dr. Syed will review research that leverages the expanding human physiome (e.g., clinical, physiologic, claims, lifestyle, digital data etc.) to better quantify individual health status and optimize “N-of-1” treatment strategies. We begin by examining our work on computational biomarkers from clinical and consumer wearable data. These novel digital measures of pathophysiology have demonstrated significant predictive power in identifying high-risk patients across multiple domains. We present results from large-scale clinical studies, establishing the utility of these markers in the setting of cardiovascular disease to identify patients at increased risk of death following heart attacks. Building on this foundation, we then discuss how AI can augment biomarker-driven patient-treatment matching with precision care delivery optimization.

Key areas of focus include: matching patients to individually-optimal provider choices and care settings, and screening for waste, abuse and errors with deep contextual awareness of individual health trajectories. In recent deployments at scale, including randomized controlled trials across millions of lives, this work has shown significant potential to improve outcomes and costs in production settings. We review the findings of these large-scale deployments and discuss opportunities to further advance precision healthcare delivery through advances in machine intelligence.

 

About Zeeshan Syed, PhD

Zeeshan Syed is the Co-Founder and CEO of Health at Scale. He was previously a Tenured Associate Professor of Computer Science and Engineering (CSE) at the University of Michigan – where he led the Computational Biomarker Discovery and Clinical Inference Group at the Michigan Artificial Intelligence (AI) Laboratory. Zeeshan was also a Clinical Associate Professor and Director of the Clinical Inference and Algorithms Program at Stanford University, and a member of the early-stage team at Google X that launched Verily. His research interests lie at the intersection of artificial intelligence and machine learning, systems physiology, clinical medicine, and public health – with a focus on the theory, application and operationalization of large population-scale machine intelligence systems for translational impact. Zeeshan received a joint PhD in Computer Science and Biomedical Engineering from MIT EECS and Harvard Medical School through the Harvard-MIT Health Sciences and Technology program, and MEng and SB degrees from MIT EECS. He is a recipient of multiple awards, including NSF CAREER, DARPA YFA, AHA Career, and Google Platinum Innovation awards.

 

Upcoming Events in this Series

Optimizing Large Language Models for Detecting Co-Morbid Depression or Anxiety Symptoms in Chronic Diseases: Insights from Patient Messages
Featuring Jiyeong Kim, PhD, MPH
Monday, March 10, 2025 | 2-3 p.m. | Hybrid Event

The Geometry of Thought: Computational Psychiatry, AI, and the Structure of Belief
Featuring Baihan Lin, PhD
Tuesday, March 18, 2025 | 3-4 p.m. | Hybrid Event

To learn more about the DoC-IT Visiting Speaker Series, click here.

Add to Calendar 2025-03-25 22:00:00 2025-03-25 23:00:00 DoC-IT Visiting Speaker: Zeeshan Syed, PhD Hybrid Event Light refreshments will be provided. Email [email protected] for the registration information. About the Event The burden of many diseases remains high despite available treatment options. Two causes of this are challenges in choosing treatments (i.e., failure to diagnose and match patients to treatments that are appropriate for their individual risks) and delivering treatments (i.e., failure to deliver chosen treatments in a manner optimized for each individual). Advances in machine intelligence offer significant potential to address these challenges and enable personalization in diagnosis, treatment selection, and care delivery. In this talk, Dr. Syed will review research that leverages the expanding human physiome (e.g., clinical, physiologic, claims, lifestyle, digital data etc.) to better quantify individual health status and optimize “N-of-1” treatment strategies. We begin by examining our work on computational biomarkers from clinical and consumer wearable data. These novel digital measures of pathophysiology have demonstrated significant predictive power in identifying high-risk patients across multiple domains. We present results from large-scale clinical studies, establishing the utility of these markers in the setting of cardiovascular disease to identify patients at increased risk of death following heart attacks. Building on this foundation, we then discuss how AI can augment biomarker-driven patient-treatment matching with precision care delivery optimization. Key areas of focus include: matching patients to individually-optimal provider choices and care settings, and screening for waste, abuse and errors with deep contextual awareness of individual health trajectories. In recent deployments at scale, including randomized controlled trials across millions of lives, this work has shown significant potential to improve outcomes and costs in production settings. We review the findings of these large-scale deployments and discuss opportunities to further advance precision healthcare delivery through advances in machine intelligence.   About Zeeshan Syed, PhD Zeeshan Syed is the Co-Founder and CEO of Health at Scale. He was previously a Tenured Associate Professor of Computer Science and Engineering (CSE) at the University of Michigan – where he led the Computational Biomarker Discovery and Clinical Inference Group at the Michigan Artificial Intelligence (AI) Laboratory. Zeeshan was also a Clinical Associate Professor and Director of the Clinical Inference and Algorithms Program at Stanford University, and a member of the early-stage team at Google X that launched Verily. His research interests lie at the intersection of artificial intelligence and machine learning, systems physiology, clinical medicine, and public health – with a focus on the theory, application and operationalization of large population-scale machine intelligence systems for translational impact. Zeeshan received a joint PhD in Computer Science and Biomedical Engineering from MIT EECS and Harvard Medical School through the Harvard-MIT Health Sciences and Technology program, and MEng and SB degrees from MIT EECS. He is a recipient of multiple awards, including NSF CAREER, DARPA YFA, AHA Career, and Google Platinum Innovation awards.   Upcoming Events in this Series Optimizing Large Language Models for Detecting Co-Morbid Depression or Anxiety Symptoms in Chronic Diseases: Insights from Patient Messages Featuring Jiyeong Kim, PhD, MPH Monday, March 10, 2025 | 2-3 p.m. | Hybrid Event The Geometry of Thought: Computational Psychiatry, AI, and the Structure of Belief Featuring Baihan Lin, PhD Tuesday, March 18, 2025 | 3-4 p.m. | Hybrid Event To learn more about the DoC-IT Visiting Speaker Series, click here. Division of Clinical Informatics and Digital Transformation America/Los_Angeles public