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BMC Bioinformatics. 2018 Sep 26;19(1):341. doi: 10.1186/s12859-018-2374-0.

Prototyping a precision oncology 3.0 rapid learning platform.

Author information

1
Cancer Commons, Los Altos, CA, USA.
2
Istituto Oncologico Veneto, IOV-IRCSS; and Department of Surgery Oncology and Gastroenterology, University of Padova, Padova, Italy.
3
Program for Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
4
Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
5
EECS Department, MIT, Cambridge, MA, USA.
6
Cancer Commons, Los Altos, CA, USA. jshrager@stanford.edu.
7
Symbolic Systems Program, Stanford University (Adjunct), Stanford, CA, USA. jshrager@stanford.edu.

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:

Controlled natural language; Nanopublication; Natural language processing; Precision oncology; Rapid learning; Targeted therapies; Treatment reasoning; Tumor boards

PMID:
30257653
PMCID:
PMC6158802
DOI:
10.1186/s12859-018-2374-0
[Indexed for MEDLINE]
Free PMC Article

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