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8:00 am Coffee & Registration

8:30 am Chair’s Opening Remarks

DATA DRIVEN CLINICAL DECISIONS: MAXIMIZING THE VALUE OF DATA FOR MORE EFFICIENT & SIGNIFICANT RESEARCH

Undoubtedly we need to be making data-driven decisions. At the core of hindering clinical development is messy and biased data. To revolutionize our clinical development, we need data-driven decisions that start with clean and valuable data. These sessions look at current efforts involved in generating methodical and comprehensive data sets and the challenge of validating ML generated results. They also look at integrating various data types to develop biomarker approaches for clinical studies.

8:40 am Harnessing Machine Learning & Artificial Intelligence for Biomedical Research

Synopsis

  • Challenges in integrating machine learning and artificial intelligence forbiomedical research. Semi-automating the process of features engineering and discovery of ML models in life sciences
  • Using ML algorithms to illuminate estrogen signaling in breast tumorigenesis and guide novel drug development

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9:10 am Data Integration & Machine Learning for Gene Therapy Translational Research

Synopsis

  • Analysis of clinical trial derived data to identify correlates of improved outcomes
  • Characterization of gene therapy drug products to compare manufacturing processes

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9:40 am How Machine Learning Derived Drug-Sensitivity Models Can Help Clinical Proof-Of-Concept Studies

  • Bin Li Director of Computational Biology, Takeda

Synopsis

• Build machine learning models for drug-sensitivity as early as possible, to help infer a drug’s MOA, stratifying patients, and select the right indications
• Model validation using independent testing datasets is critical

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10:10 am Translating Machine Learning Solutions in the Clinical Practice

  • Mark Michalski Executive Director, MGH & BWH Center for Clinical Data Science

Synopsis

  • Overview of advances in machine learning and their potential impact on healthcare

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10:40 am Morning Refreshments

AI FOR SITE SELECTION, DECENTRALISED TRIALS & CONNECTED CLINICAL TRIALS

Increasingly, the role of wearables are becoming more and more critical in streamlining clinical development, this section of the agenda looks at the benefits and challenges of leveraging data from wearable devices and how we are using the power of AI in the joint digitization and optimization of clinical trials.

11:10 am Case Study: How AI Is Being Harnessed in Designing Decentralised Trials

Synopsis

• The digitalization of Clinical trials
• Exploring telemedicine in Clinical trials

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11:40 am Implementing Digital & Wearables Devices in Clinical Trials: A Collaborative Process

Synopsis

• Benefits and challenges of leveraging data from wearable devices: are we finally getting “real-world” data?
• Implementing digital devices in clinical trials require a strong collaboration

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12:10 pm The Road to Digital Therapeutics: Using AI to Improve Patient Engagement to Advance Mental Healthcare

  • Jennifer Gentile Senior Vice President, US Clinical Operations, Ieso Digital Health

Synopsis

  • Measure things well and you can train learning models that improve care
  • Natural Language Processing allows the identification of clinician and patient behaviors that predict engagement and clinical outcomes

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12:40 pm Networking Lunch

AI-ML FOR STREAMLINING PHARMACOVIGILENCE PROCESSES

As global regulatory requirements evolve, and the volume and variety of pharmacovigilance (PV) data increases exponentially, PV operational teams are faced with a collective pressure to improve quality and patient safety with minimal costs. Here we look at how the industry is pushing towards new technologies to obtain a faster, more accurate and secure approach to PV processes. Automation in this field promises to drastically cut costs. These sessions look to explore how the use of AI can be used to support execution of PV activities and drive down costs, whilst staying compliant.

1:40 pm Exploring the Emerging Pharmacovigilance Applications of AI & Machine Learning

Synopsis

  • Priorities of Pharmacovigilance at different stages of the product life-cycle
  • Challenges and opportunities in PV lifecycle management and how to implement AI

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2:10 pm Is it Possible to have AI-ML Circumvent the Bottlenecks in Clinical Trial Management?

Synopsis

• Exploring Patient enrollment challenges: Consolidate and structure EHRs, AI for automated clinical trial matching
• Pharmacovigilance via virtual monitoring, AI could streamline by using virtual assistant that enforces behavioural changes in patients; facial recognition to track medication adherence

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2:40 pm Using Artificial Intelligence to Optimize PV workload

  • Omar Aimer Pharmacovigilance and Drug Safety Specialist, Brunel-Sanofi

Synopsis

• AI to improve speed and security of adverse event case processing
• How AI will allow early detection of potential drug-related side effects

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3:10 pm Chair’s Closing Remarks

3:20 pm End of Day Two & Close of AI-ML Clinical Development Summit 2019