Prototyping a precision oncology 3.0 rapid learning platform

被引：5
|
作者：
Sweetnam, Connor [1 ]
Mocellin, Simone [2 ,3 ]
Krauthammer, Michael [4 ,5 ]
Knopf, Nathaniel [1 ,6 ]
Baertsch, Robert [1 ]
Shrager, Jeff [1 ,7 ]
机构：
[1] Canc Commons, Los Altos, CA 94023 USA
[2] Univ Padua, IOV IRCSS, Inst Oncol Veneto, Padua, Italy
[3] Univ Padua, Dept Surg Oncol & Gastroenterol, Padua, Italy
[4] Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA
[5] Yale Univ, Dept Pathol, Sch Med, New Haven, CT 06520 USA
[6] MIT, EECS Dept, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[7] Stanford Univ Adjunct, Symbol Syst Program, Stanford, CA 94305 USA
来源：
BMC BIOINFORMATICS | 2018年 / 19卷
关键词：
Natural language processing; Precision oncology; Controlled natural language; Nanopublication; Treatment reasoning; Rapid learning; Tumor boards; Targeted therapies;
D O I：
10.1186/s12859-018-2374-0
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
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.
引用
收藏
页数：19
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