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

页数：19

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