Prototyping a precision oncology 3.0 rapid learning platform
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 plat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2374-0 |
| _version_ | 1819076570093977600 |
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| author | Connor Sweetnam Simone Mocellin Michael Krauthammer Nathaniel Knopf Robert Baertsch Jeff Shrager |
| author_facet | Connor Sweetnam Simone Mocellin Michael Krauthammer Nathaniel Knopf Robert Baertsch Jeff Shrager |
| author_sort | Connor Sweetnam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T18:43:24Z |
| format | Article |
| id | doaj.art-a67d90bfb6cc4e8cb9fcb013b7ddc593 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T18:43:24Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a67d90bfb6cc4e8cb9fcb013b7ddc5932022-12-21T18:53:58ZengBMCBMC Bioinformatics1471-21052018-09-0119111910.1186/s12859-018-2374-0Prototyping a precision oncology 3.0 rapid learning platformConnor Sweetnam0Simone Mocellin1Michael Krauthammer2Nathaniel Knopf3Robert Baertsch4Jeff Shrager5Cancer CommonsIstituto Oncologico Veneto, IOV-IRCSS; and Department of Surgery Oncology and Gastroenterology, University of PadovaProgram for Computational Biology and Bioinformatics, Yale UniversityCancer CommonsCancer CommonsCancer CommonsAbstract 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.http://link.springer.com/article/10.1186/s12859-018-2374-0Natural language processingPrecision oncologyControlled natural languageNanopublicationTreatment reasoningRapid learning |
| spellingShingle | Connor Sweetnam Simone Mocellin Michael Krauthammer Nathaniel Knopf Robert Baertsch Jeff Shrager Prototyping a precision oncology 3.0 rapid learning platform BMC Bioinformatics Natural language processing Precision oncology Controlled natural language Nanopublication Treatment reasoning Rapid learning |
| title | Prototyping a precision oncology 3.0 rapid learning platform |
| title_full | Prototyping a precision oncology 3.0 rapid learning platform |
| title_fullStr | Prototyping a precision oncology 3.0 rapid learning platform |
| title_full_unstemmed | Prototyping a precision oncology 3.0 rapid learning platform |
| title_short | Prototyping a precision oncology 3.0 rapid learning platform |
| title_sort | prototyping a precision oncology 3 0 rapid learning platform |
| topic | Natural language processing Precision oncology Controlled natural language Nanopublication Treatment reasoning Rapid learning |
| url | http://link.springer.com/article/10.1186/s12859-018-2374-0 |
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