FEB 24, 2016 10:00 AM PST

Harnessing the Power of Healthcare Data with Causal Machine Learning to drive Personalized and Effective Treatment Interventions

Speaker

Abstract

Every day, vast amounts of healthcare data are collected from clinical trials as well as real world medical visits on patient treatment regimens and subsequent clinical outcomes. This big data raw material provides a rich asset to investigate for understanding therapeutic effectiveness and patient care. Datasets range from genomics and other ‘omic data to clinical to medical and pharmacy claims to electronic medical records to registries and beyond, and analysis can discover and predict biomarkers, drivers of disease, novel interventions, mechanisms of action, drug combinations, disease models, portfolio optimization, and personalized care/treatment algorithms. Key to leveraging this data and uncovering which treatments and interventions specifically improve a patient’s health, are powerful analytic approaches. Utilizing causal mathematics and machine learning to create in silico disease networks directly from data has been a successful approach to identify predictive and causal mechanistic associations. Simulations of resultant models unlock the knowledge within complex data, enabling personalized, actionable predictions and precision targeting of interventions. Fully realizing the power of precision medicine to identify and predict patient outcomes will significantly increase the ability of healthcare leaders and professionals to make better decisions to improve patient care.

The seminar will focus on the following learning objectives: [1] case studies detailing the data and experimental design that have yielded success and [2] key actionable insights generated from models and analytics that can be leveraged in drug discovery and development all the way to healthcare patient setting.
 


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FEB 24, 2016 10:00 AM PST

Harnessing the Power of Healthcare Data with Causal Machine Learning to drive Personalized and Effective Treatment Interventions



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