Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Schedule: Health and Medicine sessions

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9:00am–5:00pm Tuesday, March 26, 2019
Data Case Studies
Location: 2022
Alex Kudriashova (Astro Digital), Jonathan Francis (Starbucks), JoLynn Lavin (General Mills), Robin Way (Corios), June Andrews (GE), Kyungtaak Noh (SK Telecom), Taposh DuttaRoy (Kaiser Permanente), Sabrina Dahlgren (Kaiser Permanente), Craig Rowley (Columbia Sportswear), Ambal Balakrishnan (IBM), Benjamin Glicksberg (UCSF), Patrick Lucey (Stats Perform), Rhonda Textor (True Fit)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions. Read more.
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11:50am–12:30pm Wednesday, March 27, 2019
Enabling insights and analytics with data streaming architectures and pipelines using Kafka and Hadoop
Data Engineering & Architecture
 Location: 2006
Mohammad Quraishi (Cigna)
Average rating: ****.
(4.60, 5 ratings)
In a large global health services company, streaming data for processing and sharing comes with its own challenges. Data science and analytics platforms need data fast, from relevant sources, to act on this data quickly and share the insights with consumers with the same speed and urgency. Join Mohammad Quraishi to learn why streaming data architectures are a necessity—Kafka and Hadoop are key. Read more.
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4:20pm–5:00pm Wednesday, March 27, 2019
Spark NLP: How Roche automates knowledge extraction from pathology and radiology reports
Data Science, Machine Learning & AI
 Location: 2009
Yogesh Pandit (Roche), Saif Addin Ellafi (John Snow Labs), Vishakha Sharma (Roche Molecular Solutions)
Average rating: ****.
(4.67, 3 ratings)
Yogesh Pandit, Saif Addin Ellafi, and Vishakha Sharma discuss how Roche applies Spark NLP for healthcare to extract clinical facts from pathology reports and radiology. They then detail the design of the deep learning pipelines used to simplify training, optimization, and inference of such domain-specific models at scale. Read more.
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11:00am–11:40am Thursday, March 28, 2019
Framework to quantitatively assess ML safety: Technical implementation and best practices
Data Science, Machine Learning & AI
 Location: 2010
Ram Shankar Siva Kumar (Microsoft (Azure Security))
Average rating: ****.
(4.33, 3 ratings)
How can we guarantee that the ML system we develop is adequately protected from adversarial manipulation? Ram Shankar Kumar shares a framework and corresponding best practices to quantitatively assess the safety of your ML systems. Read more.
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11:00am–11:40am Thursday, March 28, 2019
Data Science University: Transforming a Fortune 5 workforce
Culture and organization, Strata Business Summit
 Location: 2018
Marc Paradis (UnitedHealth Group)
Average rating: ****.
(4.75, 4 ratings)
Data Science University (DSU) was established to bring analytics education to UnitedHealth Group, the world’s largest healthcare company, with over 270,000 employees. Marc Paradis explains how DSU was built out over time in an era of rapidly changing analytics technology and capabilities in an industry ripe for disruption, covering the challenges faced and lessons learned. Read more.
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2:40pm–3:20pm Thursday, March 28, 2019
Community and regional data sharing policy frameworks: Frontier stories
Case studies, Strata Business Summit
 Location: 2018
Mei Fung (People Centered Internet)
Average rating: ****.
(4.67, 3 ratings)
Data sharing necessitates stakeholders and populations of people to come together to learn the benefits, risks, challenges, and known and unknown "unknowns." Data sharing policies and frameworks require increasing levels of trust, which takes time to build. Join Mei Fung for trail-blazing stories from Solano County, California, and ASEAN (SE Asia), which offer important insights Read more.
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2:40pm–3:20pm Thursday, March 28, 2019
Machine learning prediction of blood alcohol content: A digital signature of behavior
Data Science, Machine Learning & AI
 Location: 2009
Kirstin Aschbacher (UCSF Cardiology)
Average rating: ****.
(4.20, 5 ratings)
Some people use digital devices to track their blood alcohol content (BAC). A BAC-tracking app that could anticipate when a person is likely to have a high BAC could offer coaching in a time of need. Kirstin Aschbacher shares a machine learning approach that predicts user BAC levels with good precision based on minimal information, thereby enabling targeted interventions. Read more.
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3:50pm–4:30pm Thursday, March 28, 2019
Nutrition data science
Data Science, Machine Learning & AI
 Location: 2009
Noah Gift (UC Davis ), Michelle Davenport (Quantitative Nutrition)
Average rating: **...
(2.89, 9 ratings)
Noah Gift and Michelle Davenport explore exciting ideas in nutrition using data science; specifically, they analyze the detrimental relationship between sugar and longevity, obesity, and chronic diseases. Read more.