Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore

Mining electronic health records and the web for drug repurposing

Kira Radinsky (eBay | Technion)
10:00am10:20am Thursday, December 7, 2017
Machine Learning
Location: Hall 404AXF
Average rating: *****
(5.00, 8 ratings)

Researchers decide on exploratory targets for drug repurposing—the process of applying known drugs in new ways to treat diseases—based on trends in research and observations on small numbers of cases, leading to potentially costly biases of focus and neglect. Kira Radinsky offers an overview of a system that jointly mines 10 years of nationwide medical records of more than 1.5 million people and extracts medical knowledge from Wikipedia to help reduce spurious correlations and provide guidance about drug repurposing. The resulting system seeks to identify potential biological processes to justify potential influences between medications and target diseases via links on a graph constructed from Wikipedia data. Kira shares results of the system on two studies on drug repurposing for hypertension and diabetes. In both cases, the algorithm identified drug families that were previously unknown, and clinical opinion by experts in the field and clinical trials on those drug families suggest that these drugs show promise.

Photo of Kira Radinsky

Kira Radinsky

eBay | Technion

Kira Radinsky is the chief scientist and director of data science at eBay, where she is building the next-generation predictive data mining, deep learning, and natural language processing solutions that will transform ecommerce. She also serves as a visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine. Kira cofounded SalesPredict (acquired by eBay in 2016), a leader in the field of predictive marketing—the company’s solutions that leveraged large-scale data mining to predict sales conversions. One of the up-and-coming voices in the data science community, Kira is pioneering the field of web dynamics and temporal information retrieval. She gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early-warning signs of globally impactful events, including political riots and disease epidemics. She was named one of MIT Technology Review’s 35 young innovators under 35 for 2013 and one of Forbes’s 30 under 30 rising stars in enterprise technology for 2015; in 2016, she was recognized as woman of the year by Globes. Kira is a frequent presenter at global tech events, including TEDx and the World Wide Web Conference, and she has published in Harvard Business Review.