Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Schedule: Healthcare sessions

9:00am5:00pm Tuesday, March 14, 2017
Location: LL20 A
Barbara Eckman (Comcast), Dirk Jungnickel (Emirates Integrated Telecommunications Company (du)), Kishore Papineni (Astellas Pharma), Paul Barth (Podium Data), Carlo Torniai (Pirelli Tyre), Bryan Harrison (American Express), Chris Murphy (Zurich Insurance Group), Martin Lidl (Deloitte), Maura Lynch (Pinterest), Nixon Patel (Kovid Group), Bas Geerdink (ING), Robin Li (Tapjoy), Yohan Chin (Tapjoy), Jim Harrold (NationBuilder), Lana Novikova (Heartbeat AI Technologies)
In a series of 12 half-hour talks aimed at a business audience, you’ll hear data-themed case studies from household brands and global companies, explaining the challenges they wanted to tackle, the approaches they took, and the benefits—and drawbacks—of their solutions. If you want practical insights about applied data, look no further. Read more.
1:50pm2:30pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 230 C Level: Intermediate
David Talby (Pacific AI), Claudiu Branzan (Accenture)
Average rating: ****.
(4.14, 7 ratings)
David Talby and Claudiu Branzan offer a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records. Infrastructure components include Kafka, Spark Streaming, Spark, and Elasticsearch; data science components include spaCy, custom annotators, curated taxonomies, machine-learned dynamic ontologies, and real-time inferencing. Read more.
1:50pm2:30pm Wednesday, March 15, 2017
Sensors, IOT & Industrial Internet
Location: LL20 D Level: Non-technical
Julie Lockner (17 Minds Corporation)
Average rating: *****
(5.00, 1 rating)
How can we empower individuals with special needs to reach their potential? Julie Lockner offers an overview of a project to develop collaboration applications that use wearable device data to improve the ability to develop the best possible care and education plans. Join in to learn how real-time IoT data analytics are making this possible. Read more.
1:50pm2:30pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 210 C/G Level: Advanced
Michael Dusenberry (IBM Spark Technology Center), Frederick Reiss (IBM)
Average rating: *****
(5.00, 2 ratings)
Estimating the growth rate of tumors is a very important but very expensive and time-consuming part of diagnosing and treating breast cancer. Michael Dusenberry and Frederick Reiss describe how to use deep learning with Apache Spark and Apache SystemML to automate this critical image classification task. Read more.
2:40pm3:20pm Wednesday, March 15, 2017
Real-time applications
Location: LL20 D Level: Intermediate
Joseph Blue (MapR), ed00425e 963b0803 (MapR Technologies)
Average rating: ****.
(4.50, 2 ratings)
Joseph Blue and Carol Mcdonald walk you through a reference application that processes ECG data encoding using HL7 with a modern anomaly detector, demonstrating how combining visualization and alerting enables healthcare professionals to improve outcomes and reduce costs and sharing lessons learned from their experience dealing with real data in real medical situations. Read more.
2:40pm3:20pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 230 C Level: Intermediate
Robert Grossman (University of Chicago)
Average rating: ***..
(3.73, 11 ratings)
When there is a strong signal in a large dataset, many machine-learning algorithms will find it. On the other hand, when the effect is weak and the data is large, there are many ways to discover an effect that is in fact nothing more than noise. Robert Grossman shares best practices so that you will not be accused of p-hacking. Read more.
11:50am12:30pm Thursday, March 16, 2017
Business case studies, Strata Business Summit
Location: 210 D/H Level: Intermediate
. . (ProKarma)
Average rating: ****.
(4.25, 4 ratings)
Many hospitals combine early warning systems with rapid response teams (RRT) to detect patient decline and respond with elevated care. Predictive models can minimize RRT events by identifying at-risk patients, but modeling is difficult because events are rare and features are varied. Emily Spahn explores the creation of one such patient-risk model and shares lessons learned along the way. Read more.