Data supporting precision oncology fda wakibbe
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Machine Learning and validation for the FDA.
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Data supporting precision oncology fda wakibbe
- 1. Data Supporting Precision Oncology Warren A. Kibbe, Ph.D. Professor, Biostats & Bioinformatics Director of Biomedical Informtics, Duke CTSI Chief Data Officer, Duke Cancer Institute warren.kibbe@duke.edu @wakibbe #DataSharing #LearningHealthSystem #DataHarmonization
- 2. The World is Changing Pace of Commercialization Reach of Markets Role of Data Change in Healthcare Change in Computing Societal Changes
- 3. Understanding Cancer Precision medicine will lead to fundamental understanding of the complex interplay between genetics, epigenetics, nutrition, environment and clinical presentation and direct effective, evidence- based prevention and treatment. Ramifications across many aspects of health care
- 4. Changes in Oncology • Cancer is a grand challenge • Anatomic vs molecular classification • Health vs Disease
- 5. Our ability to generate biomedical data continues to grow in terms of variety and volume Current sources of data molecular genome pathology imaging labs notes sensors icons by the Noun Project
- 6. Multi-scalar, multi-modal, temporal data analysis is in its infancy icons by the Noun Project molecular genome pathology imaging EHR behavior population
- 7. AI is changing our ability to go both deep and broad Trustworthy AI Provenance Reusable Reproducible
- 8. AI and Machine Learning will help us manage these data
- 9. Data Science
- 10. Changes in Computing • Converged devices • Converged IT • Ubiquity of devices, data, mHealth
- 12. Pace of Technology Adoption
- 14. Convergence Machine Learning and Deep Learning approaches are enabled by changes in instrumentation, digitalization, computation, technology.
- 15. Best Practices • For software, behavior driven development, true DevOps (security first), fully traceable software development (tests, and deployment, testing and validation harness), open APIs, and validation of algorithms including learning models are not just feasible but critical to overall validation.
- 16. Boeing 737 MAX Example of where good software practices were undermined by changing the behavior of the software without changing the scope of testing
- 17. Interpretable ML
- 20. NAM Workshops
- 21. “Science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience.” —Institute of Medicine LEARNING HEALTH SYSTEMS
- 22. Another imperative is that such systems do their work: • Transparently (how does one learn without well documented processes?) • Reproducibly (good practices must always be repeatable at scale and scientifically reproducible) • Only with the above can the science in “data science” be done with sufficient rigor LEARNING HEALTH SYSTEMS
- 24. Learning Health Systems in NEJM
- 25. Goals • Contain rising cost of healthcare • Maximize the value of care • Increase public discourse and marketplace for healthcare
- 26. Drivers • Decision Making is too complex • Clinical decisions are based on practice, not evidence • Inefficiency and waste in healthcare
- 27. Human cognitive capacity is constant
- 28. Lack of Evidence
- 29. EHRs and the Learning Health System
- 30. LHS definition
- 31. Sebastian Thrun
- 32. 32 Blue Ribbon Panel Report Cancer Moonshot℠ Blue Ribbon Panel “The Cancer Moonshot Task Force was directed to consult with external experts from relevant scientific sectors, including the presidentially appointed National Cancer Advisory Board(NCAB). A Blue Ribbon Panel of scientific experts was created to advise the NCAB.”
- 33. Vision: Enable the creation of a Learning Healthcare System for Cancer, where as a nation we learn from the contributed knowledge and experience of every cancer patient. As part of the Cancer Moonshot, we want to unleash the power of data to enhance, improve, and inform the journey of every cancer patient from the point of diagnosis through survivorship.
- 34. Data Sharing and the FAIR Principles FAIR – Making data Findable, Accessible, Attributable, Interoperable, Reusable, and provide Recognition Force11 white paper https://www.force11.org/group/fairgroup/fairprinciples
- 35. 35 NCI Cancer Research Data Commons (CRDC) - Concept NCI Scope: “Create a data science infrastructure necessary to connect repositories, analytical tools, and knowledge bases” Data commons co-locate data, storage and computing infrastructure with commonly used services, tools & apps for analyzing and sharing data to create an interoperable resource for the research community.* *Robert L. Grossman, Allison Heath, Mark Murphy, Maria Patterson and Walt Wells, A Case for Data Commons Towards Data Science as a Service, IEEE Computing in Science and Engineer, 2016. Source of image: The CDIS, GDC, & OCC data commons infrastructure at the University of Chicago Kenwood Data Center.
- 36. 36 Data Commons Framework Clinical Proteomics ImagingGenomics Immuno- oncology Animal Models Cancer Biomarkers NCI Cancer Research Data Commons SBG CGC Broad FireCloud ISB CGC Elastic Compute Query Visualization Clinical Proteomics Tumor Analysis Consortium* Tool Deployment The Cancer Imaging Archive* TCIA Web Interface APIs Data Submission Authentication & Authorization Authentication & Authorization Data Models & Dictionaries Computational Workspaces Data Contributors and Consumers Tool Repositories Metadata Validation & Tools Analysis Courtesy NCI-CBIIT
- 39. Data Harmonization • The process of semantic and syntactic mapping of data to a set of definitions, predefined data elements, data model. • Validation and Harmonization of primary and secondary data is crucial to enable analysis and reuse
- 40. Spanning the Semantic Chasm of Despair Building a Translational Bridge CD2H Thanks to Melissa Haendel
- 41. Project Highlight: Harmonizing clinical data models Sentinel I2b2/ACT OMOP PCORNET ▪ Different countries use different “outlets”. ▪ There is a need for travel adapters. The Solution: ▪ Use a converter between various adapters. ▪ Allow researchers to ask a question once and receive results from many different sources
- 42. Project Highlight: LOINC2HPO ◆ Develop a software tool to map LOINC codes to HPO terms ◆ Develop software to convert EHR observations into HPO terms for use in clinical research Steps Develop a tool for converting LOINC laboratory codes and values into more phenotypically meaningful language (Human Phenotype Ontology) to allow for translational interoperability and new analytics 2657-5 “Nitrite [Mass/volume] in Urine” Numeric 20407-3 “Nitrite [Mass/volume] in Urine by Test strip” Numeric 32710-6 “Nitrite [Presence] in Urine” Positive/Negati ve 5802-4 “Nitrite [Presence] in Urine by Test strip” Positive/Negati ve 50558-6 “Nitrite [Presence] in Urine by Automated test strip Positive/Negati ve LOINC Outcome HPO: Nitrituria
- 43. INSERT CDE Browser Screenshot? CIBMTR Center for Cancer Research Over 35 NCI Programs, Plus Cancer Centers and Consortia GDC
- 44. Data Sharing Index • We need metrics for data, software, algorithm use, usability, conformance • Data sharing stimulates science, innovation, commercialization • Providing recognition and attribution to data providers and software & algorithm builders is critical for a robust data sharing ecosystem • Support and measure FAIRness!