Accelerating precision oncology with artificial intelligence

Precision oncology is a revolution in cancer care where the right treatments are matched to the right patients. However, to date, precision oncology treatments have only been developed for some cancers. Even when the treatments are available, not all patients receive them. Applying artificial intelligence to clinical, genomic, and social determinants of health data may help in developing targeted prevention strategies and new treatments and help identify eligible patient so that care can be delivered efficiently and equitably.

Register Free: https://www.pharmexec.com/pe_w/precision-oncology

Event Overview:

Precision oncology is a revolution in cancer care where the right treatments are matched to the right patients. Precision oncology can help identify patients at high risk for cancer and help lower risk, find cancers earlier, improve diagnosis of type of cancer, choose the best treatments, and monitor how well treatments work. Precision oncology can improve quality of life, helping patients avoid unnecessary treatments and high-risk invasive procedures.

To date precision oncology treatments have only been developed for some cancers. Even when the treatments are available, not all patients receive them. Barriers to access include the lack of availability of digital tools and IT infrastructure and access to genetic testing. Applying artificial intelligence and machine learning to clinical, genomic, and social determinants of health data may help in developing targeted prevention strategies and new treatments and help identify eligible patients so that care can be delivered efficiently and equitably.

Key Learning Objectives:

• Learn about new innovations to use clinical, genomic, and other data to develop machine learning algorithms and deliver insights to improve patient care.
• Understand challenges of implementation at the point of care. How can these technologies be brought to the bedside?
• Discuss what the future holds in terms of developing models to help both the development of new therapies and patient treatment at the point of care. What is needed to make this a reality?

Speakers:

ELISE BERLINER, PhD
Global Senior Principal of Real-World Evidence Strategy
Oracle

Elise Berliner, PhD is the Global Senior Principal for Real World Evidence Strategy at Cerner Enviza. Before joining Cerner Enviza, Dr. Berliner was the Director of the Technology Assessment Program at the Agency for Healthcare Research and Quality (AHRQ), providing systematic reviews and other scientific analyses to the Centers for Medicare & Medicaid Services (CMS) to inform Medicare coverage decisions and other policy issues. Dr. Berliner has several years of experience in research and development at innovative medical technology companies, was a Fellow at the Office of Technology Assessment in the United States Congress, and received her Ph.D. in biophysics from Brandeis University.

KATHRYN LANG
Senior Vice President RWD and Analytics
Freenome

Kathryn Lang, MBBS, MRCP (UK), FRCPath, is a Senior Vice President and leads the real-world data and analytics group at Freenome.
Kathryn joined Freenome from Guardant Health, where she served as Vice President of Outcomes and Evidence. In this role, she was responsible for primary and secondary research in early cancer detection as well as health economics modeling and real-world data. Kathryn also served as the Global Head of Oncology Real-World Evidence for Pfizer.
Kathryn is an experienced hematologist-oncologist with training in epidemiology and data science from Newcastle University and the Erasmus Medical Center, Rotterdam. Kathryn spent ten years in academic medicine at King’s College Hospital, London, specializing in hematological malignancy and real-world, observational evidence generation.

ERIC STAHLBERG
Director of Bioinformatics and Data Science
Frederick National Library for Cancer Research

Dr. Eric Stahlberg was named director of Biomedical Informatics and Data Science (BIDS) at the Frederick National Laboratory for Cancer Research in September 2018. He has been instrumental in establishing the Frederick National Laboratory’s high-performance computing initiative and in assembling scientific teams across multiple, complex organizations to advance predictive oncology. Stahlberg has played a leadership role in many key partnerships, including a major collaboration between the NCI and the Department of Energy (DOE). Under the Joint Design of Advanced Computing Systems for Cancer (JDACS4C), NCI and DOE are accelerating progress in precision oncology and computing. The collaboration is rooted in three major national initiatives; the Precision Medicine Initiative, the National Strategic Computing Initiative, and the Cancer Moonshot. Stahlberg has spearheaded the Frederick National Laboratory’s contributions to a number of JDACS4C projects, including ATOM and CANDLE. Dr. Stahlberg holds a Ph.D. in computational chemistry from The Ohio State University.

CHRISTINE SWISHER, PhD
Chief Scientific Officer
Project Ronin

Christine Swisher is Chief Scientific Officer at Project Ronin leading multidisciplinary teams of data scientists, statisticians, informaticists, and machine learning experts to build technologies that solve challenging clinical problems. Together, they have delivered safe and ethical artificial intelligence-based that personalize key clinical decisions, NLP and generative AI innovations, and demonstrated causal impact on clinical and institutional outcomes with the use of the Ronin platform. Before joining Ronin, she spearheaded several FDA-cleared AI-based products, CLIA LDTs, and other clinical decision support/AI capabilities across the ML lifespan and led analytics functions. She is an advocate and champion for Responsible AI and holds over 20 patents in machine learning and AI. She completed her PhD in biomedical engineering with an emphasis on computer science jointly at Berkeley and UCSF and continued her postdoctoral work at Harvard Medical School and MGH.

Register Free: https://www.pharmexec.com/pe_w/precision-oncology