Artificial Intelligence and Machine Learning in Clinical Research

2018 Archived Content

OVERVIEW | DOWNLOAD BROCHURE | SPEAKERS | KEYNOTES

Cambridge Healthtech Institute’s Inaugural

Artificial Intelligence and Machine Learning in Clinical Research:

AI, ML, ROBOTICS, ADVANCED ANALYTICS, BIG DATA
February 14-15, 2018 | Hyatt Regency Orlando | Orlando, FL

Artificial intelligence (AI) and machine learning (ML) have propelled many industries toward a new highly functional and powerful state. Now they are starting to make their way into the clinical research realm. Many pharmaceutical companies and larger CROs are starting projects involving some elements of AI, ML and robotic process automation in clinical trials. To facilitate the discussion and to accelerate the adoption of these approaches in clinical trials, Cambridge Healthtech Institute presents the Inaugural Artificial Intelligence and Machine Learning in Clinical Research conference, part of 9th Annual SCOPE Summit.

Wednesday, February 14

11:30 am Registration Open

12:10 pm BRIDGING LUNCHEON CO-PRESENTATION: Centralizing Data to Address Imperatives in Clinical Development

Karim Damji, Senior Vice President, Products, Solutions & Marketing, Saama Technologies

Amit Gulwadi, Executive Director, Analytics-Patient Engagement/Recruitment, Celgene

With the deluge of structured, unstructured, and syndicated data, the use of varied data for targeted outcomes remains difficult, despite increased industry efforts to address the issue. New technologies are federating the ability to leverage analytic-ready data for innovations in clinical development and drug commercialization. With the application of clinical data-as-a-service and meta-data core, centralized clinical data lakes have the power to improve data quality, evidence generation, and time-to-insights.

12:50 Coffee and Dessert Break in the Exhibit Hall

1:30 Plenary Keynotes

3:00 Valentine’s Day Celebration in the Exhibit Hall, Last Chance for Exhibit Viewing

BLOCKCHAIN, ML, AI

4:00 Chairperson’s Remarks

Balazs Flink, M.D., Clinical Trial Analytics Lead, R&D Business Insights and Analytics, Bristol-Myers Squibb

4:05 Blockchain Disruption: How Blockchain Will Change Our Industry

Munther_Baara Munther Baara, Senior Director, Development Business Technology, Pfizer

Imagine a solution that makes it easy to aggregate health data in a secure, trusted, automated, and error-free way, a solution which enforces rules, privacy, and regulations in a mutually agreed upon manner, resulting in a smart-contract between patient and healthcare stakeholders. This enables patients to aggregate their data from diverse health sources and share what they choose to with their physicians and researchers. All this puts the patient in control of their health and well-being, rather than being along for the ride: How it works, key benefits, empowering the patients with control over their data.

4:30 Exploration of Where Machine Learning Will Help in the Product Development Process in the Pharmaceutical Industry

Francis_Kendall Francis Kendall, Technology Evaluation and Implementation Leader, Product Development, Roche

The talk will explore how Machine Learning is and will change how Product Development is carried out in the industry from improving efficiencies, gaining more insights on products, improved surveillance of products, especially safety, and its use in IoT devices.

4:55 CO-PRESENTATION: Leveraging Digital Transformation to Unify Process, Connect Data, and Turbocharge Innovation in Clinical Operations

Evi_Cohen Evi Cohen, Vice President, Global Pharma & Life Sciences, Appian

Mike Montello, Vice President, Global Head, R&D Solutions Information Technology, IQVIA

Conducting and managing a successful, safe clinical trial is complicated. With massive data and complex processes at the core, it’s no surprise innovation in Business Process Management (BPM) is behind many successful trials.

5:25 Intelligent Clinical Trial Design, Planning and Conduct

Balazs_Fink Balazs Flink, M.D., Clinical Trial Analytics Lead, R&D Business Insights and Analytics, Bristol-Myers Squibb

As technology evolves and AI solutions become more sophisticated, there is a natural demand to test and apply them in areas that were traditionally expert opinion-guided. This presentation is about early experiences and challenges of pharma companies - including BMS - that are starting to apply AI in the R&D space to promote precision oncology, identify targets, design and plan trials and translate strategy to efficient operational execution.

5:50 Reception Hosted by Cognizant Technology Solutions

Thursday, February 15

7:15 am Registration Open

7:45 Breakfast Presentation: Clinical Trial Operations Insights Evolved via Accenture Life Sciences Cloud

Chandi_Kodthiwada Chandi Kodthiwada, Product Manager, Accenture Life Sciences Cloud (ALSC), Accenture

Chances are you have spent a meaningful amount of time on one of the below questions: How do you assess your Clinical Trial operational efficiency? How do I stay on top of Site behavior? How do you maintain Vendor-Oversight? In the presentation, we will go through Accenture Life Sciences Cloud(ALSC) - Clinical Operations Insights Platform(COIP) and our work with Metrics Champion Consortium(MCC) to answer the above questions.

AI AND ML IN CLINICAL TRIALS

8:30 Chairperson’s Remarks

Vikram Gupta, Technology Innovation Senior Manager, Amgen

8:35 CO-PRESENTATION: AI and Machine Learning for Clinical Trials

Vikram Gupta, Technology Innovation Senior Manager, Amgen

William Wong, Technology Strategy, Innovation Senior Manager, Amgen

While technology will probably never completely replace HCPs, machine intelligence (Machine Learning, Natural Language Processing (NLP), and Artificial Intelligence (AI)) is transforming healthcare by improving outcomes and changing the way healthcare professionals think about providing care and manage clinical trials.

9:35 AI Guided Patient Selection to Elevate the Clinical Trial Efficiency and Accelerate Positive Outcomes

Slava Akmaev, PhD, Senior Vice President & Chief Analytics Officer, BERG

Drug development is well positioned to benefit from data driven approaches integrating disparate data types and delivering actionable insight with a commercial impact. The BERG AI driven platform, Interrogative Biology® identifies novel biochemical markers in phase I/II clinical studies for patient selection in registrational trials increasing the likelihood of success.

9:55 Applying Machine Learning Frameworks to Improve Trial Outcomes

Rishi_Karvir Hrishikesh (Rishi) Karvir, Senior Manager, Medidata

Data is the cornerstone of modern AI/machine learning technologies, yet data integration and standardization remains a problem--especially in clinical trials. Discover how access to the right data can transform all aspects of a clinical trial, such as using predictive modeling for site performance or anomaly detection for data errors.

10:15 Networking Coffee Break

NATURAL LANGUAGE PROCESSING AND SEMI AUTOMATED CSR NARRATIVES

10:30 Chairperson’s Remarks

Xia Wang, Director, Health Informatics, Global Medicines Development Unit, R&D, AstraZeneca

10:35 CO-PRESENTATION: The Application of Natural Language Processing (NLP) to Explore the Understanding of Patient Treatment Journey in Diabetes

Xia_Wang Xia Wang, Director, Health Informatics, Global Medicines Development Unit, R&D, AstraZeneca

Gerry_Petratos Gerry Petratos, CEO, Hiteks Solutions, Inc.

This talk relates a pilot work at AstraZeneca in utilizing Natural Language Processing (NLP) technology to explore the understanding of treatment journeys of newly diagnosed type 2 diabetes patients, by retrieving structured data from Diabetes Practice Guidelines & clinical documentation to identify events of interest and compare cohorts. This pilot provided benchmarking understanding of NLP technology to retrieve meaningful information from unstructured Electrical Health Record (EHR) data sources. The outcomes of discreet treatment pathways from SoC guidelines revealed important insights of the complexity involved in treating diabetes patients.

11:00 CO-PRESENTATION: Semi Automated CSR Narratives

Avanti_Karandikar Avanti Karandikar, Senior Manager, Clinical Business & System Analysis RDIS, MedImmune (AstraZeneca Biologics)

Dorian_Zoumplis Dorian Zoumplis, M.S. Biotechnology, Senior Innovation Project Manager, Technology Innovation & Delivery Excellence, AstraZeneca

This presentation will include topics such as: Create quality CSR narratives that are consistent across a therapeutic area and/or compound, scope, save time and effort on behalf of the author, reduction in cost associated with (e.g. costs associated with service providers) writing narratives from scratch, create narratives based on a template with specifics to protocol and/or compound, allow for collected data points to be pre-populated to avoid mistakes in study day calculations, event onset/resolution dates, etc.

11:25 Brief Session Break

11:35 CO-PRESENTATION: AI - Machine Learning for Clinical Data Management, a Pilot Case Study

Abhay_Jha Abhay Jha, Principal, Business Technology Lead, R&D Excellence Practice, ZS Associates

Venkat_Sethuraman Venkat Sethuraman, MBA, Ph.D., Associate Principal, ZS Associates

Machine learning can aid Clinical Data Management with early detection of anomalies in patient data from clinical sites, thereby reducing the need to unlock databases frozen for submission, saving precious time. To determine a best approach with machine learning, ZS worked with a pharmaceutical client, using univariate and multivariate analytics and fraud detection techniques to identify anomalies that previously slipped through standard data quality checks. In this session, we will share our case study results, including lessons learned.

12:00 pm PANEL DISCUSSION: How to Make All the Data Machine Learnable?

Moderator: Munther Baara, Senior Director, Development Business Technology, Pfizer

Panelists:

Jaydev Thakkar, Product Innovation Lead, Amgen Digital Health

Francis Kendall, Technology Evaluation and Implementation Leader, Product Development, Roche

Balazs Flink, M.D., Clinical Trial Analytics Lead, R&D Business Insights and Analytics, Bristol-Myers Squibb

Hrishikesh Karvir, PhD, Senior Manager Predictive Modeling, Medidata Solutions

Leveraging data for machine learning projects
Implementing robust data standards
Analyzing big data using machine learning algorithms

12:50 PANEL DISCUSSION: Why Are There Barriers to the Adoption of Innovative Processes and Technologies at Sites?

Moderator: Jim Kremidas, Executive Director, Association of Clinical Research Professionals (ACRP)

Panelists: David Vulcano, Assistant Vice President & Responsible Executive for Clinical Research, Hospital Corporation of America (HCA)

Sean Walsh, MBA, CDO, Raleigh Neurology Associates

Beth Harper, MBA, Workforce Innovation Officer, Association of Clinical Research Professionals (ACRP)

Many innovative technologies and process improvement initiatives are coming out at a rapid pace, whether from TransCelerate and other industry consortia, or from technology companies themselves. Which of these improvements actually work? How can sites implement these more effectively? Why are there barriers to adoption and how can the innovators better understand sites’ needs?

Share sites’ perspective on the evolving clinical research landscape
Discuss the reasons sites struggle with new processes and technology tools
Determine ways to facilitate adoption

1:15 Closing Remarks

1:20 SCOPE Summit 2018 Adjourns

Group Discounts Are Available! Special rates are available for multiple attendees from the same organization. For more information on group discounts, contact Melissa Dolen at 781-972-5418 or mdolen@healthtech.com.

For questions or suggestions about the meeting, contact:

Marina_62x62 Marina Filshtinsky, M.D.
Senior Director, Conferences
Cambridge Healthtech Institute (CHI)
T: (+1) 781.972.5496
E: mfilshtinsky@healthtech.com