Agenda
Agenda is subject to change. All times are in Eastern Time.
| DAY 1 11:00 AM – 4:30 PM ET |
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| 11:00 – 11:10 AM |
Welcome and Workshop Overview Katrina Goddard, Ph.D.
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11:10 – 11:55 AM |
Keynote Presentation: Driving Health Equity through Representative AI and Inclusive Innovation Abby Sears, M.H.A.
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11:55 – 12:40 PM |
Session 1: Landscape of NCI-funded Research The inclusion of machine learning methods in healthcare delivery research has rapidly increased, resulting in increased grant NCI grant application. This session will review extramural trends in NCI research grant applications over the last five years proposing machine learning methods, an electronic health record-based data structure, and cancer healthcare delivery research focus. It will highlight emerging trends in research topics, methods, populations, and outcomes. Moderator: Panelist: Roxanne E. Jensen, Ph.D.
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12:40 – 1:10 PM |
Break |
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1:10 – 2:40 PM |
Session 2: Applying Machine Learning to Identify High-Risk Patients & Outcomes Machine Learning holds great promise to leverage rich digital data collected during routine clinical practice to identify high-risk patients and predict diverse health outcomes. In particular, prediction models are of great interest in clinical settings, where preventing adverse outcomes, such as severe treatment side effects or early detection of aggressive cancers, may allow for more informed, early, and effective clinical intervention. Despite the potential of machine learning to advance cancer research and clinical practice, there are challenges limiting its use. This panel will include presentations from experts in the application of “real world” machine learning examples including the identification of clinical patients at high risk for lethal cancer and the development of algorithms to predict severe treatment-related adverse events among cancer patients. Moderator: Panelists:
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2:40 – 4:10 PM |
Session 3: Machine Learning Approaches to Improve Cancer Patient Symptom Monitoring & Health Outcomes Innovations in machine learning, especially when coupled with novel or underutilized data sources, have the potential to transform symptom monitoring of cancer patients and to improve health outcomes. Despite the enormous potential of machine learning, there are challenges limiting its use and adoption within the cancer research and clinical communities. This panel will include presentations from experts in the application of machine learning related to the capture and evaluation of patient-generated health data, including patient-reported outcomes. Panelists will also discuss opportunities and challenges with utilizing machine learning for symptom monitoring and health outcome prediction. Moderators: Dana L. Wolff-Hughes, Ph.D. Panelists:
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4:10 – 4:25 PM |
Day 1 Wrap Up Roxanne E. Jensen, Ph.D.
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4:30 PM |
Adjourn
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| DAY 2 11:00 AM – 4:30 PM ET |
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| 11:00 – 11:05 AM |
Welcome Back |
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11:05 – 12:40 PM |
Session 4: Algorithmic Bias & Ethics This session will explore algorithmic bias and other ethical issues raised by the use of machine learning in cancer and health outcomes research. The speakers will discuss the sources and implications of algorithmic bias and the meaning of algorithmic fairness; identify other potential harms of applying machine learning algorithms in outcomes research and clinical care; and explore ways of preventing and mitigating these harms. The overall goal of this session is to identify key knowledge gaps that need to be addressed in future research. Moderator: Patient Perspective: Kimberly Richardson Panelists:
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12:40 – 2:15 PM |
Session 5: Unstructured Data: Drawing Meaning Across Domains to Inform Healthcare As a patient moves through their cancer care journey, they create a wealth of information, much of it in the form of unstructured data. The ability to create meaning from this data represents endless possibilities in better understanding a patient’s journey, both inside and outside the formal healthcare system, and potential to improve care and outcomes for these patients. This panel will explore the variety of domains and the machine learning methodologies used to add meaning to unstructured information that may impact cancer care delivery and health outcome. Moderators: Nicole Senft Everson, Ph.D. Patient Perspective: Kimberly Richardson Panelists:
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2:15 – 2:45 PM |
Break |
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2:45 – 4:20 PM |
Session 6: Machine Learning-Based Decision Support at Point of Care In this session, we gathered a group of leaders in machine learning-based clinical decision support and cancer care delivery. This panel will provide valuable insights on improving the use of machine learning with clinical decision support tools and how it may be incorporated in clinical workflow to improve cancer care. The panel will provide examples of projects and studies reflecting on what principles and frameworks can be used in research and practice.
Patient Perspective: Kimberly Richardson
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| 4:20 – 4:25 PM |
Day 2 Wrap Up Roxanne E. Jensen, Ph.D.
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4:30 PM |
Adjourn
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