MACHINE LEARNING FOR PRECISION MEDICINE

RUISHAN LIU – STANFORD UNIVERSITY

ABSTRACT

Toward a new era of medicine, our mission is to benefit every patient with individualized medical care. This talk explores how machine learning can make precision medicine more effective and diverse. I will first discuss Trial Pathfinder, a computational framework to optimize clinical trial designs (Liu et al. Nature 2021). Trial Pathfinder simulates synthetic patient cohorts from medical records, and enables inclusive criteria and data valuation. In the second part, I will discuss how to leverage large real-world data to identify genetic biomarkers for precision oncology (Liu et al. Nature Medicine 2022), and how to use language models and causal inference to form individualized treatment plans.

BIO: Ruishan Liu is a postdoctoral researcher in Biomedical Data Science at Stanford University, working with Prof. James Zou. She received her PhD in Electrical Engineering at Stanford University in 2022. Her research lies in the intersection of machine learning and applications in human diseases, health and genomics. She was the recipient of Stanford Graduate Fellowship, and was selected as the Rising Star in Data Science by University of Chicago, and the Rising Star in Engineering in Health by Johns Hopkins University and Columbia University. She led the project Trial Pathfinder, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021.

URLs of related papers:

• Liu et al. Nature (2021) Evaluating eligibility criteria of oncology trials using real-world data and AI. https://www.nature.com/articles/s41586-021-03430-5
• Liu et al. Nature Medicine (2022) Systematic pan-cancer analysis of mutation–treatment interactions using large real-world clinicogenomics data. https://www.nature.com/articles/s41591-022-01873-5