Prototyping a precision oncology 3.0 rapid learning platform

BMC Bioinformatics

Michael Krauthammer¹  Connor Sweetnam²  Robert Baertsch²  Jeff Shrager²  Simone Mocellin³  Nathaniel Knopf⁴ 

[1] and Department of Surgery Oncology and Gastroenterology, University of Padova;Cancer Commons;Istituto Oncologico Veneto, IOV-IRCSS;Program for Computational Biology and Bioinformatics, Yale University;

关键词: Natural language processing;    Precision oncology;    Controlled natural language;    Nanopublication;    Treatment reasoning;    Rapid learning;   

DOI  :  10.1186/s12859-018-2374-0

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown