- November 14, 2019 from 12:00-1:00pm
- Christiana Hospital, Value Institute; 8E50 A/B
- Or live at https://bluejeans.com/361095905
Brown Bag Lunch – Please RSVP.
The talk will focus on strategies to integrate data for precision medicine. In particular the benefits of an infrastructure-as-code solution will be discussed touching upon generalizability, replicability, agility, flexibility, infrastructure and cloud computing. The combination of these beneficial traits within a data integration strategy supports the goal of interoperable solutions, and the ability to grow integrated data sets beyond institutional boundaries. With the advent of precision medicine and multi-omic data analysis, there is a pressing need to expand our capabilities for utilizing interoperable systems for data analysis. This talk will address how to enable health systems to adopt integrated solutions to support precision medicine.
Meet the Speaker
Given the advances in the use of multi-omic bioinformatics data in medicine and the application of artificial intelligence (AI) algorithms, an inflection point is forming that will transform our collective knowledge of health and mechanisms of disease progression. AI is essential to advance knowledge creation and drive new discoveries through the integration of vast amounts of digital data that are exponentially accruing.
I am interested in leveraging AI to accelerate knowledge discovery through the application of innovative, distributed and scalable approaches that expand our understanding of health and healthcare with a focus on cancer. Specifically, I examine novel hybrid clustering approaches such as combining clusters with network analysis, ontological knowledge, and risk models. In addition, I explore integrated AI learning applied to large repositories of medical data for knowledge discovery. I use infrastructure-as-code to model the integration of medical multi-omic data beyond institutional boundaries to generate reproducible models across healthcare networks.
This activity has been approved for AMA PRA Category 1 Credit