Day One | Tuesday, November 6, 2018
Continental Breakfast and Registration Opens
Digital Therapeutics: Software As Treatment
  • Discuss how machine learning can be leveraged to personalize treatment and engage patients
  • Hear how digital therapeutics can produce clinical outcomes comparable to or better than drugs
  • Understand how one digital therapeutics platform can be leveraged to target multiple indications (Click’s platform targets depression, insomnia, chronic pain, and more)
  • Review the regulatory pathway that is open for these apps to obtain drug-like labels
Christopher Jordan, Chief Technology Officer, CLICK THERAPEUTICS
Utilize Predictive Analytics to Manage Healthcare Outcomes
  • Hear about a patient monitoring platform that detects sepsis and fluid overload in ICU patients via a urinary catheter
  • Learn how Potrero Medical has found the balance between innovative technology adoption and meeting business goals
  • Understand how Potrero Medical has utilized predictive models to address patients needs
Kelly Stanton, Data Scientist, POTRERO MEDICAL
Saheel Sutaria, Chief Technology Officer, POTRERO MEDICAL
Address Pharma’s Big Data Problem and Even Bigger Text Problem
  • Provide a broad perspective on different Natural Language Processing (NLP) problems in Pharma and how that compares with other industries
  • Review different Deep Learning techniques that can be used to solve Pharma NLP problems
  • Outline how Saama went about solving Adverse Drug Event (ADE) extraction problems
Karim Damji, Senior Vice President of Product Marketing, SAAMA TECHNOLOGIES INC.
Networking Break
Create an Internal Culture of Innovation and Openness to AI adoption
  • Discuss strategies to present digestible and cohesive information regarding an AI platform’s capabilities and services for internal review
  • Develop implementation planning committees to oversee the AI integration
  • Impact of implanting AI in an organization and proposed success and ROI
Ryan Billings, Senior Director, Digital Innovation, AMAG PHARMACEUTICALS
Machine Learning in Drug Development: Apply Artificial Intelligence to Develop Novel Therapeutics for Neurodegenerative Diseases
  • Examine 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
Networking Lunch
Transform Healthcare Delivery Through Artificial Intelligence
  • Review specific examples of utilizing AI to craft and parse the largest clinical database representing clinical information from 63 million patients collected over 16 years through electronic medical record systems
  • Explore OSU Center for Health Systems Innovation’s efforts to apply AI approaches to the transformation of healthcare delivery, emphasizing on rural healthcare
William D. Paiva, Ph.D., Executive Director, Center for Health Systems Innovation (CHSI), OKLAHOMA STATE UNIVERSITY
Utilize AI Applications to Ensure Pharmacovigilance at Every Step of the Drug Development Process
  • Elevate pharmacovigilance operations through technological innovations and internal collaborations
  • Identify and limit side effects of marketed drugs through AI systems
  • Enhance PV performance and reporting by utilizing developing technologies
Networking Break
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/upscaling and postmarket surveillance
Ronald Dorenbos, Associate Director Materials and Innovation, TAKEDA
Supply Chain Innovations: Integrating Blockchain to Drive Transparency and Efficiency Across Supply Chain Networks
  • Understand how life sciences and biopharma organizations are adopting and implementing disruptive technologies to manage product diversion, countering illicit drug supplies while conforming to the mandates of the Drug Supply Chain Security Act (DSCSA)
  • Consider the impact of blockchain-based systems to support supply chain security and the core functions of manufacturing and distribution
Day One Concludes
Day Two | Wednesday, November 7, 2018
Continental Breakfast and Registration Opens
Bridging the Gap Between Machine Learning and Wearable Technologies in Clinical Trials
  • Use exploratory digital health endpoints to better identify patients who will most benefit from therapies
  • Apply wearable technologies combined with AI algorithms to monitor and prevent adverse events and ensure treatment adherence
  • Hear about MyoKardia’s cross-functional team of translational, clinical, regulatory, and marketing experts collaborated to rapidly develop and commercialized digital health tools
Charles Wolfus, Vice President, Digital Health and Business Operations, MYOKARDIA
Networking Break
Real-World AI and Drug Repositioning: A Perfect Match for Drug Development
  • Not all AI platforms are the same: Understand how to find the right one
  • How AI-driven repositioning can impact key aspects of healthcare relevant to the discovery and development of drugs and in the context of the right patient subpopulations, all the way from lab bench to the point of care
  • Review examples and case studies, including Biovista’s Project Prodigy AI
Aris Persidis, President, BIOVISTA
Define Blockchain Healthcare Needs and Strategies for Adoption
  • Define appropriate blockchain architecture for regulatory compliance
  • Engage healthcare’s various stakeholders effectively for implementation and adoption
  • Review successful blockchain in healthcare uses cases as well examples of pitfalls to date
Rishi Madhok, M.D., Chief Executive Officer and Co-Founder, BITMED
Networking Lunch
Leveraging Machine Learning and AI to Drive Process Improvement at Bayer
  • Understand the implications of modeling of clinical trials in outcomes with machine learning to improve trial efficiencies
  • Hear how risk-based scheduling and planning is being used for clinical trial process optimization
  • Learn about novel deep learning models for subject enrollment and sales forecasting
Kevin Hua, Senior Manager A.I./Machine Learning Development, BAYER
Artificial Intelligence Impact on Healthcare From Drug Discovery to Digital Health
  • Identify strategies for alliance management with technologies providers that understand the current medical needs and the future platforms of operations
  • Develop strategies to ensure data management and analysis converts to increased patient care or improved healthcare outcomes
  • Outline potential downfalls of direct-to-consumer healthcare devices and how to avoid pitfalls
Georgia Mitsi, MBA, Ph.D., Senior Director, Head of Digital Health Care Initiatives, SUNOVION PHARMACEUTICALS INC.
Smart and Precise Network-Based Drug Discovery Using Artificial Intelligence Tools
  • Use AI tools to decode the fundamental principles that allow eukaryotic cells to do their business, i.e., iteratively sense, decide, act and learn/adapt.
  • Review cell’s communication networks similar to the layered communication systems on the internet, spurring the concept of the Intranet of Cells (IoC)
  • Rules of the IoC will enable us to complete and “clean up” the existing incomplete and messy biological networks, predict cell behavior, and usher a new era in network-based diagnostic tools and network-resetting therapeutics for major diseases
Pradipta Ghosh, M.D., Professor, Departments of Medicine and Cell and Molecular Medicine, UC SAN DIEGO
Digitally Detecting Developing Diseases
  • Hear how BioTrillion is developing a health technology platform called BioEngine4D by applying computational learning and AI to clinical health data and novel LIFEdata, now accessible through the ubiquity of multi-modality sensors present in everyday consumer smart devices
  • Consider how ML models can examine physiologic, non-molecular biomarkers to predict disease presence
  • Review how developing diseases exhibit a dynamic pattern of progression that can be identified through a uniquely identifiable signature and are continually measured and tracked by existing sensors
  • Shift disease detection earlier in the health-time continuum — pre-symptomatically — from developed to developing using predictive analytics
Savan Devani, Founder and CEO, BIOTRILLION
Conference Concludes

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