Agenda

Conference Day One Plenary Sessions | Thursday, June 13, 2019
8:00AM
Registration Opens and Continental Breakfast
9:00AM
Conference Chair's Welcome and Opening Remarks
Brian Martin, Head of AI, ABBVIE
9:15AM
Leverage Big Data for a Healthier World
  • Understand the implications of the data deluge for the world’s largest biopharmaceutical company
  • Learn about the drivers behind the data growth, the potential it may hold for our ability to improve health and well-being at every stage of life
  • Examine Pfizer’s partnerships with health systems and tech to deliver better care, improve outcomes, and ensure sustainable success
Josh Raysman, Head, AI Center for Excellence, PFIZER
10:00AM
CX (Conversational Experience) With Pharma Data
  • Artificial Intelligence has exciting applications in Pharma, especially in the planning and conduct of a clinical trial: Virtual Assistants, like Saama's Deep Learning Intelligent Assistant, or DaLIA, is one such application that can enable natural language conversation with the data
  • We will cover the algorithms, tools, and best practices to build a virtual assistant and Saama's award-winning approach
Josh Raysman, Head, AI Center for Excellence, PFIZER
10:45AM
Leveraging Machine Learning/Deep Learning Insights From Other Industries Including YouTube to Address Healthcare Data Requirements
  • Case Study: Machine learning imaging technology from YouTube translated to cell phenotyping

  • Case Study: AlphaFold — A review of DeepMind's research to use Artificial Intelligence for Scientific Discovery

  • Case Study: Deep Learning Technology use for Electronic Health Records

Zeenat Patrawala, MBA, M.S., Partner Development, GOOGLE BRAIN
11:30AM
Networking Break
Conference Day One Tracked Sessions | Thursday, June 13, 2019
AI in Drug and Clinical Development
AI in Healthcare
Wearables and Digital Therapeutics
12:00PM
Track Chairperson Welcoming Remarks
Amit Gulwaldi, Senior Vice President, Clinical Innovations, SAAMA TECHNOLOGIES, INC.
Track Chairperson Welcoming Remarks
Evon Holladay, Chief Operating Officer, A HEALTHIER WE
Track Chairperson Welcoming Remarks
Dave Malenfant, Executive Vice President, Industry Liaison and Talent Development, Bio Supply Management Alliance (BSMA); Director, Outreach & Partnerships, Center for Supply Chain Innovation Texas Christian University
12:15PM
Transforming Clinical Development Using Artificial Intelligence and Machine Learning
  • AI/Maching Learning/Natural Language Processing simplified
  • Discuss choice of models, framework and fabric
  • Identify practical methodology and metrics
  • Hear examples of use case implementations
  • Consider a realistic lens of requirements for successful implementation
Prasanna Rao, Head, Artificial Intelligence and Data Science, PFIZER
Opportunities in Evolving Healthcare Landscape to Explore the Foundation of Specialty Clinical Innovation
  • Introduce disruptive technology to increase clinical outcomes and improve patient care
  • Review innovation performance and impact to measure its true successes within an organization
  • Gain a more profound patient population understanding that is converted back into the care continuum through artificial intelligence systems
Christine Sawicki, R.Ph., MBA, LSSMB, Senior Director of Product Development and Innovation, CVS HEALTH
Digital Continuous Care – AI-Powered Wearable Technology of the Future
  • Utilization of new technology disrupts the paradigm of many ecosystems and is often resisted in Healthcare
  • However, when a natural evolution of technology is incorporated into an existing organization, the innovations out-pace the disruptive effects while encouraging widespread acclamation
  • Digital Continuous Care model incorporates the convenience of wearable medical monitoring technologies and AI’s predictive power. Such a natural evolution of healthcare enables proactive patient continuous supervision and can be fine-tuned with existing healthcare ecosystems
  • Harnessing Digital Continuous Care model will improve patient lifestyle, quality of care while reducing risk and overall costs

Leon Eisen, Ph.D., Founder and Chief Executive Officer, OXITONE MEDICAL
1:00PM
Networking Lunch
2:00PM
Machine Learning in Drug Development: Apply Artificial Intelligence to Develop Novel Therapeutics for Neurodegenerative Diseases
  • Hear an example ML platform to improve efficiency during the drug development process
  • Leverage an innovative partnership model to maximize the use and access to a development platform
  • Understand the impact on identifying novel treatments and parsing patient populations by combining ML experts and neuroscience drug developers
Irene Choi, Ph.D., Director, Drug Discovery, VERGE GENOMICS
MONARCSi Case Study: Machine Learning Advancement That Help Physicians Discern Connections Between Adverse Events and Therapeutics
  • Hear about the Machine learning-based tool called MONARCSi
  • This is a decision support tool that is a modification of the well-known Naranjo score that helps physicians and scientists assess whether a drug and an adverse event are related
  • Participate in a demonstration of the system
Bruce A. Donzanti, Senior Group Director, Global Regulatory Pharmacovigilance Innovation Policy, Genentech
Data Wars: The Struggle Between Interoperability, Security, and Progress
  • Best practices in decision management to ensure the subject matter expert is in control
  • Prioritize AI systems that are able to explain decision-making processes
  • Question why your data-driven, unbiased AI project can be derailed by people’s mindsets
  • Find the balance between innovative technology adoption and business goals
Kelly Stanton, Senior Data Scientist, POTRERO MEDICAL
2:45PM
Transform Drug Development With Model Training Predictive Analytics
  • Effectively harness HPC cloud resources for drug discovery
  • Hear an example of how a combination of artificial intelligence and chemistry are accelerating drug discovery
  • Protect proprietary data while leveraging shared, cloud-based HPC resources
  • Discuss how highly accurate computations can help companies save money, save time, and lower risk across all phases of drug development
Brian Martin, Head of AI, ABBVIE
Machine Learning and AI for Precision Medicine — Can It Be Explained and Validated?
  • Identify patterns of care (real-world treatment pathways and temporal patient phenotypes) and outcomes of care with ML/AI
  • Interpret the patterns and models from ML/AI through interactive visualization
  • Support clinical and patient decision-making using the models — opportunities to enable Precision Medicine
  • Validate ML/AI predictions with scientific research — can it be done with observation studies or RCT?
John Cai, Executive Director, Real-World Data Analytics, MERCK
Challenges in Abstractive Summarization With Limited Data and the Value of Detailed Scoping/Planning When Building Complex Platforms

  • ​Define the Ideal Product Experience to Inform Innovation and Product Development Programs
  • Dive into key challenges of qualitative consumer research of data generated live in the form of audio and video files and in-person attendance, summarized in terms of key learnings and conclusions, and then data is stored but no longer exploited. Retrieving and maximizing the use of raw data is extremely time-consuming
  • Case Study: In a collaborative project with Google and executed by Gigster, GSK sought to create a user-friendly, end-to-end, AI-powered cloud platform that allows their team to deploy real-time audio translation/transcription (powered by Google’s Speech-to-Text API)
  • Streamline report-generation using bespoke summarization and topic models. This also provides a repository for future data mining activities
  • Outline key learnings from the team including strengths and limitations of Google’s speech-to-text API

Denise Pohlhaus, Principal Data Scientist, GLAXOSMITHKLINE AS
3:30PM
Networking Break
4:00PM
Risk-Based Process Optimization of Clinical Trials
  • Understand the implications of risk-based planning and scheduling of clinical trials with machine learning to improve trial efficiencies
  • Learn about the drivers of uncertainties, long cycle time and high costs associated with clinical trials for process optimization
  • Hear how Bayer is utilizing Machine learning (ML) to improve the core business and to transform to a data-driven enterprise
Kevin Hua, Senior Manager A.I./Machine Learning Development, BAYER
Opportunities in the Evolving Healthcare Landscape to Use Nutrition to Improve Health and Economic Outcomes Via Predictive Analytics
  • Using retrospective datasets to identify patients that will benefit from nutrition intervention
  • How large ACOs and IDNS can target the right patients at the right time for nutrition intervention using predictive analytics
  • How predictive analytics can improve health outcomes for patients with poor nutrition across the continuum of care
Jamie Partridge, Ph.D., MBA, Director, Global Scientific Affairs Global Health Economics Outcomes Research and Health Policy, ABBOTT NUTRITION
Moving Wearables From Monitor to Power Player in Patient-Centered Medical and Clinical Design
  • Understand the untapped potential of health wearable data for various use cases in a clinical and medical design
  • Identify the necessary missing pieces needed for applying intelligence and putting the data to work
  • Recalibrate the approach to data access, sharing, validation and utilization for use in clinical trials/research and precision medicine
Maria Palombini, Director, Communities and Initiatives Development, Emerging Technology, IEEE STANDARDS ASSOCIATION
4:45PM
  • Applications of Artificial Intelligence and Machine Learning in Drug Target Identification
  • Learn how AI/ML can help to address the problem of target identification in drug discovery
  • Understand the knowledge graph (KG) and applying machine learning (ML) to KG
  • Learn an example of using ML on biological knowledge discovery and drug target identification
Pankaj Agarwal, Senior Fellow, Computational Biology, GLAXOSMITHKLINE
Jin Yao, Ph.D., Computational Biologist, GLAXOSMITHKLINE
Platform AI: Using Artificial Intelligence for Predictive Assistance, Partnerships, and Consumer Engagement
  • Learn how to incorporate AI into digital assistants to keep patients on track
  • Model how to extend an AI platform to identify and go faster using strategic partnerships
  • Use internal and partner AI to advance population health by incorporating consumer point of view
Evon Holladay, Chief Operating Officer, A HEALTHIER WE
Gena Koufos, RN, M.S., MBA, Innovation and Digital Health Lead, AMAG Pharmaceuticals
Digitally Detecting Developing Diseases – Computational Intelligence Synergistically Applied to Medical Data and Life Data
  • Learn how BioEngine4D, BioTrillion’s health technology platform applying AI to data generated via Consumer smart devices, can early detect neurologic and respiratory diseases
  • Hear how novel digital biomarkers can be developed by mapping key diseases to digitally measurable expressions and can augment existing molecular biomarkers
  • Consider how common smartphones and smartwatches can effectively act as:
    • “A digital doctor” to continuously check for key disease indicators and catalyze earlier clinical diagnoses and intervention
    • “Clinical trials in a pocket” that disrupt
Savan Devani, Founder and CEO, BIOTRILLION
5:30PM
Day One Concludes
Conference Day Two Plenary Sessions | Friday, June 14, 2019
8:30AM
Registration Opens and Continental Breakfast
9:00AM
Conference Chair's Recap of Day One
Brian Martin, Head of AI, ABBVIE
9:15AM
Understanding the Impact of AI in an Organization
  • Discussing your companies starting point for AI and Technology
  • How to invest in AI in a way that not only reduces cost but drives product innovation, revenue growth, operational efficiency, and improved customer experience
  • Review how to embrace and capitalize from AI through new technologies and partnering with established startups to implement them
Andrew Eye, CEO, ClosedLoop
Victoria Gamerman, Head of U.S. Health Informatics and Analytics (Sr. Assoc. Director, Biostatistics and Data Sciences), BOEHRINGER INGELHEIM
Kevin Hua, Senior Manager A.I./Machine Learning Development, BAYER
Brian Martin, Head of AI, ABBVIE
Matthew A. Michela, President and CEO, LIFEIMAGE
Prasanna Rao, Head, Artificial Intelligence and Data Science, PFIZER
10:45AM
Networking Break
Conference Day Two Tracked Sessions | Friday, June 14, 2019
AI in Drug and Clinical Development
AI in Healthcare
Wearables and Digital Therapeutics
11:15AM
Data Mining Empowered by Real-World Data (RWD) and Federated Electronic Health Records (EHR) Network Platforms in Clinical Trials
  • Highlight key areas with case studies of using advanced data analytics and AI with real-world data and federated electronic health records network platforms to improve clinical trial protocol design
  • Provide deep insights of targeted patient population and treatment pathway for precision recruitment, to explore non-invasive diagnosis approach to improve identification of the right patient population for clinical research
  • Discuss new data science and analytics approaches that will lay out good foundation for us to apply AI in clinical development for new scientific discovery
Jane Fang, Director, R&D Information, ASTRAZENECA
Rejuvenate Rural Healthcare Delivery Through Artificial Intelligence
  • Review specific examples of utilizing AI to craft and parse the largest clinical electronic medical record database
  • Explore case studies where AI was applied to build tools to improve healthcare delivery, emphasizing on rural healthcare markets
William D. Paiva, Ph.D., Executive Director, Center for Health Systems Innovation (CHSI), OKLAHOMA STATE UNIVERSITY
Use AI to Solve Complex Global Supply Chain Management Challenges
  • Synchronize the digital supply network
  • Become more dynamic, flexible, and efficient in planning and execution
  • Shift consumer expectation
Dave Malenfant, Executive Vice President, Industry Liaison and Talent Development, Bio Supply Management Alliance (BSMA); Director, Outreach & Partnerships, Center for Supply Chain Innovation Texas Christian University
12:00PM
Machine Learning and Artificial Intelligence for Life Sciences
  • Challenges in integrating machine learning and artificial intelligence for biomedical research
  • Overlaying biological knowledge onto data-driven approaches
  • Developing predictive correlates for responses to cancer therapy
Hari Singhal, Lead Data Scientist, ROCHE MOLECULAR SYSTEMS, INC.
Data Integration and Sharing for Improved Analysis Leading to Better Patient Care
  • Understanding the value proposition of data standardization for data integration
  • Importance of transparency and reproducibility of research
  • Recommendations and best practices for data standards and data sharing
Victoria Gamerman, Head of U.S. Health Informatics and Analytics (Sr. Assoc. Director, Biostatistics and Data Sciences), BOEHRINGER INGELHEIM
Understanding Digital Transformation: The Time Is Now
  • How digital medicine platforms can enable next-generation patient-powered registries and real-world evidence
  • Discuss how the use of technology is partnered with medical societies and industry to create customized digital care and research plans
  • Review how technology enables the collection of real-world evidence from patient- and device-generated data for novel devices, drugs, and digital therapeutics
Ashish Atreja, M.D., MPH, Assistant Professor and Chief Innovation Officer, Medicine, ICHAN SCHOOL OF MEDICINE, MOUNT SINAI, NY
12:45PM
Networking Lunch
1:45PM
AstraZeneca’s Open Innovation Approach: Pushing the Boundaries of Scientific Collaboration Through Crowdsourcing
  • Examine AZ’s multipronged approach to collaboration to foster novel discoveries and speed the development of new medicines for patients in need
  • Determine best practices for collaborating within industrial-academic partnerships
  • Outline translational bioinformatics approaches to gain insight into novel connections between drugs and indications for the repositioning of discontinued compounds — a case study
Leslie Cousens, Associate Director, Translational Medicine and Emerging Innovations, ASTRAZENECA
Connectivity at Kaleida Health
  • Learn about the MyKaledia Mobile app for patients and their families
  • Hear about several tech initiatives at Kaleida Health that aim to improve health outcomes
  • Discuss how Kaleida Helath System is improving patient connectivity
Theo Kornyoh, Chief Technology Officer, KALEIDA HEALTH
2:30PM
Integrate Artificial Intelligence Across the Commercialization Value Chain
  • Review how ML and NLP are used to improve target identification, candidate selections and formulation development
  • Improve treatment adherence and increase patient recruitment efficiency in clinical trial design
  • Apply AI augmented reality systems to the manufacturing process/up-scaling and post-market surveillance
Ronald Dorenbos, Associate Director Materials and Innovation, TAKEDA
Identify and Overcome Innovation Adoption Hurdles in Healthcare to Utilize Mass Quantities of Data
  • Implement a cutting-edge health data strategy while maintaining compliance with complex privacy and security regulatory frameworks
  • Identify the compliance challenges arising from the use of big data sets, the development of analytics, and the implementation of machine learning
  • Understand the value of achieving positive public perception and patient buy-in concerning health data use
Justin Smith, Ph.D., Sr. Director Advanced Analytics, ST. LUKE’S HEALTH SYSTEM
3:15PM
Predictive Analytics in Drug Development: How New Approaches Can Improve Clinical Design and Operations
  • Discuss areas of Clinical Development amenable to predictive modeling that increase speed, efficiency, and reduce risk
  • Learn about a model used to streamline clinical trial design through smarter site selection and enrollment criteria
Michael Sachs, Ph.D., Principal Manager, gRED Pipeline and Portfolio Planning, GENENTECH
Innovating Healthcare With AI, AutoML and Ethics at HealthFirst
  • Hear how DataScience@Healthfirst is developing data management best practices, risk scores, and productionized ML pipelines to scale an innovative analytics practice at a non-profit insurance company
  • Talk about how we monitor and track our machine learning models – and how we’re using AutoML to redefine the role of a data scientist in the healthcare space
  • Come hear about our past, current and future plans to ensure that machine learning in healthcare reaches underserved populations and enables the best outcomes with community-centered approaches
John Frame, Sr. Data Scientist, HEALTHFIRST CORPORATION
3:45PM
Understanding the Social Determinants of Health
  • Hear how Healthfirst has developed a strategy to identify members who do not have adequate access to care and furthermore rectify their needs
  • Talk about how the social determinants of health impact outcomes and contribute to large disparities between members — and how machine learning is powering our approach to better serve people
  • Hear about the initiatives we are working on to combat these problems with the power of DataRobot
Scott Ogden, Lead Data Scientist, HEALTHFIRST CORPORATION
4:00PM
Utilize Machine Learning and Artificial Intelligence to Identify Different Types of Risk During the Clinical Trial
  • Understand how machine learning and artificial intelligence can aid in identifying different types of risk during the clinical trial
  • Adapt machine learning and artificial intelligence to better allocate resources and use insights to determine high-risk factors
  • Use both objective and subjective performance measures to determine the proactive actions to ensure the success of clinical trials
Alex (Wen-Yaw) Hsieh, Director — Clinical Development Quality, PFIZER
Better Health for New Yorkers With Data (Science)
  • Improving healthcare for New Yorkers is our passion. Data science is our medium
  • Come hear how Healthfirst has developed a data science strategy and implemented a cloud-based machine learning pipeline to get to know our members better and drive better health outcomes while improving operating results using machine learning, natural language processing, and ethical AI
  • We’ll talk about how to sell in a data science strategy, how to build a cloud-based analytics and ML pipeline and, how to drive results with data science
Steven Prewitt, Vice President of Clinical Analytics and Informatics, HEALTHFIRST CORPORATION
4:45PM
Conference Concludes

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