Prototyping a precision oncology 3.0 rapid learning platform
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12859_2018_Article_2374.pdf
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2.84 MB
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Author(s)
Sweetnam, Connor
Mocellin, Simone
Krauthammer, Michael
Baertsch, Robert
Shrager, Jeff
Knopf, Nathaniel D.
Date Issued
September 2018
Journal
BMC Bioinformatics
Publisher
BioMed Central
Citation
Sweetnam, Connor, et al. “Prototyping a Precision Oncology 3.0 Rapid Learning Platform.” BMC Bioinformatics, vol. 19, no. 1, Dec. 2018. © 2018 The Authors
Version
Final published version
Abstract
Background: We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system.
Results: We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform.
Conclusions: The design choices made in this implementation rest upon ten constitutive hypotheses, which, taken together, define a particular view of how a rapid learning medical platform might be defined, organized, and implemented. Keywords: Natural language processing, Precision oncology, Controlled natural language, Nanopublication, Treatment reasoning, Rapid learning, Tumor boards, Targeted therapies
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s12859-018-2374-0
