Speaker Profile

Ph.D., Associate Director, Precision Medicine Initiatives, GNS Healthcare, Inc.

Dr. Diane Wuest develops strategic relationships with precision medicine partners to develop and commercialize computer models capable of elucidating disease mechanisms, advancing drug discovery and development, and improving patient care. Diane manages ongoing alliances and leverages internal analytic and product development teams to implement company-wide initiatives. Prior to GNS Healthcare, Diane worked at Genentech and obtained a Ph.D. in chemical and biomolecular engineering from the University of Delaware. Diane holds a B.S. in chemical engineering from Cornell University.


Unlocking Precise Knowledge within Complex Data via Causal Machine Learning
Causal machine learning approaches generate disease models from multimodal datasets (‘omics, clinical, medical, claims, EHR, registries, etc.) and enable personalized, actionable predictions and precision targeting of interventions. Simulations, or “what if” questions, of the unbiased models identify what factors cause patient responses to treatments and identify biomarkers and drivers of disease for therapeutic discovery and implementation.

Session Abstract – PMWC 2017 Silicon Valley

Session Synopsis:
Many precision medicine initiatives at medical centers, foundations, and providers are aggregating vast amounts of data (e.g. genomics, clinical, and EMR data). Challenges and bottlenecks to overcome are associated with the data generation and aggregation process, and knowledge extraction from multi-modular healthcare data. Representatives from different organizations will discuss specific approaches and learnings.

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