Healthcare and Life Sciences Agenda for Data + AI Summit Europe 2020

Looking for the best HLS events and sessions at Data + AI Summit Europe 2020 (Nov 17-19)? Below are some highlights. You can also find all Healthcare-related sessions, including customer case studies and extensive how-tos, within the event homepage by selecting “Healthcare & Life Sciences” from the “Industry” dropdown menu. You can still register for this free, virtual event here.

Learn more about the Healthcare and Life Sciences talks, training and events featured at the Data + AI 2020 Virtual Summit.

For Business Leaders

Improving Health Outcomes with Data + AI
To drive better outcomes and reduce the cost-of-care, healthcare and life sciences organizations need to deliver the right interventions to the right patients through the right vehicle at the right time. To achieve this, health organizations need to blend and analyze diverse sets of data across large populations, including electronic health records, healthcare claims, SoDH/demographics data, and precision medicine technologies like genomic sequences. Integrating these diverse data sources under a common and reproducible framework is a key challenge healthcare and life sciences companies face in their journey towards powering data driven outcomes. In this session, we explore the opportunities for optimization across the whole healthcare value chain through the unification of data and AI. Attendees will learn best practices for building data driven organizations and hear real-world stories for how advanced analytics is improving patient outcomes.


  • Joe Roemer, Sr. Director, Global Commercial IT Insight and Analytics, AstraZeneca
  • Iyibo Jack, Director Engineering, Milliman MedInsight
  • Arek Kaczmarek, Exec Director, Data Engineering, Providence St. Joseph Health

Keynote – AI & Predictive Analytics in Healthcare with Dr. Kira Radinsky

Dr. Kira Radinsky is the chairperson and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available.

(Swisscom Digital) Patient-centric AI App to reduce public health costs

Health costs are exploding year by year. Thanks to Artificial Intelligence it is possible to address patient needs in a cost-efficient manner.

In the case, we will demonstrate how as part of a telemedicine service we implemented a solution to reduce triage cost of patients by leveraging AI. The app we developed not only reduced costs but also significantly improved the patient experience.

For Practitioners

Unifying Multi-omics Data Together on the Databricks Platform, followed by AMA

Healthcare, life sciences, and agricultural companies are generating petabytes of data, whether through genome sequencing, electronic health records, imaging systems, or the Internet of Medical Things. The value of these datasets grows when we are able to blend them together, such as integrating genomics and EHR-derived phenotypes for target discovery, or blending IoMT data with medical images to predict patient disease severity. In this session, we will look at the challenges customers face when blending these data types together. We will then present an architecture that uses the Databricks Unified Data Analytics Platform to unify these data types into a single data lake, and discuss the use cases this architecture can empower. We will then dive into a workload that uses the whole genome regression method from Project Glow to accelerate the joint analysis of genotypes and phenotypes data.

Afterwards, Amir Kermany, Sr. SA for Healthcare and Life Sciences, will be available to answer questions about this solution or any other use case questions you may have across healthcare, the life sciences, or agriculture

(Wisecube AI) Using NLP to Explore Entity Relationships in COVID-19 Literature

In this talk, we will cover how to extract entities from text using both rule-based and deep learning techniques. We will also cover how to use rule-based entity extraction to bootstrap a named entity recognition model. The other important aspect of this project we will cover is how to infer relationships between entities, and combine them with explicit relationships found in the source data sets. Although this talk is focused on the COVID-19 data set, the techniques covered are applicable to a wide variety of domains. This talk is for those who want to learn how to use NLP to explore relationships in text. What you will learn – How to extract named entities without a model – How to bootstrap an NLP model from rule-based techniques – How to identify relationships between entities in text.

Looking forward to seeing you at the Data + AI Summit 2020.


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