Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Schedule: Health and Medicine sessions

9:00am–12:30pm Tuesday, 09/11/2018
Location: 1A 23/24 Level: Intermediate
Patrick Hall (H2O.ai | George Washington University), Avni Wadhwa (H20.ai), Mark Chan (H2O.ai)
Average rating: ****.
(4.50, 4 ratings)
Transparency, auditability, and stability are crucial for business adoption and human acceptance of complex machine learning models. Patrick Hall, Avni Wadhwa, and Mark Chan share practical and productizable approaches for explaining, testing, and visualizing machine learning models using open source, Python-friendly tools such as GraphViz, H2O, and XGBoost. Read more.
9:00am–5:00pm Tuesday, 09/11/2018
Location: 1E 10
Paco Nathan (derwen.ai), Katharina Warzel (EveryMundo), Mike Berger (Mount Sinai Health System), Sam Helmich (Deere & Company), Stephanie Fischer (datanizing GmbH), Maryam Jahanshahi (TapRecruit), Greg Quist (SmartCover Systems), Ann Nguyen (Whole Whale), Steve Otto (Navistar), Jennifer Lim (Cerner), S Anand (Gramener), Ian Brooks (Hortonworks)
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.
9:00am–5:00pm Tuesday, 09/11/2018
Location: 1A 08
Alistair Croll (Solve For Interesting), Robert Passarella (Alpha Features), Amro Alkhatib (National Health Insurance Company-Daman), Mridul Mishra (Fidelity Investments), Patrick Angeles (Cloudera), James Psota (Panjiva ), Andreas Kohlmaier (Munich Re), Paul Lashmet (Arcadia Data), Nick Curcuru (Mastercard), Robin Way (Corios), Theresa Johnson (Airbnb), Jane Tran (Unqork), Swatee Singh (American Express)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry. Read more.
11:20am–12:00pm Wednesday, 09/12/2018
Location: 1A 12/14 Level: Beginner
Olga Cuznetova (Optum), Manna Chang (Optum)
Average rating: ***..
(3.33, 3 ratings)
Olga Cuznetova and Manna Chang demonstrate supervised and unsupervised learning methods to work with claims data and explain how the methods complement each other. The supervised method looks at CKD patients at risk of developing end-stage renal disease (ESRD), while the unsupervised approach looks at the classification of patients that tend to develop this disease faster than others. Read more.
1:10pm–1:50pm Thursday, 09/13/2018
Location: 1A 06/07 Level: Intermediate
David Talby (Pacific AI), Alberto Andreotti (John Snow Labs), Stacy Ashworth (SelectData), Tawny Nichols (Select Data)
Average rating: ***..
(3.00, 4 ratings)
David Talby, Alberto Andreotti, Stacy Ashworth, and Tawny Nichols outline a question-answering system for accurately extracting facts from free-text patient records and share best practices for training domain-specific deep learning NLP models. The solution is based on Spark NLP, an extension of Spark ML that provides state-of-the-art performance and accuracy for natural language understanding. Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Location: 1A 21/22 Level: Intermediate
Occhio Orsini (Aetna)
Occhio Orsini offers an overview of Aetna's Data Fabric platform. Join in to learn the needs and desires that led to the creation of the advanced analytics platform, explore the platform's architecture, technology, and capabilities, and understand the key technologies and capabilities that made it possible to build a hybrid solution across on-premises and cloud-hosted data centers. Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Location: 1E 12/13 Level: Non-technical
Chiny Driscoll (MetiStream), Jawad Khan (Rush University Medical Center )
Average rating: ****.
(4.00, 5 ratings)
Chiny Driscoll and Jawad Khan offer an overview of a solution by Cloudera and MetiStream that lets healthcare providers automate the extraction, processing, and analysis of clinical notes within an electronic health record in batch or real time, improving care, identifying errors, and recognizing efficiencies in billing and diagnoses. Read more.