Literature DB >> 25689646

Watson will see you now: a supercomputer to help clinicians make informed treatment decisions.

Susan Doyle-Lindrud.   

Abstract

IBM has collaborated with several cancer care providers to develop and train the IBM supercomputer Watson to help clinicians make informed treatment decisions. When a patient is seen in clinic, the oncologist can input all of the clinical information into the computer system. Watson will then review all of the data and recommend treatment options based on the latest evidence and guidelines. Once the oncologist makes the treatment decision, this information can be sent directly to the insurance company for approval. Watson has the ability to standardize care and accelerate the approval process, a benefit to the healthcare provider and the patient.

Entities:  

Keywords:  cancer care

Mesh:

Year:  2015        PMID: 25689646     DOI: 10.1188/15.CJON.31-32

Source DB:  PubMed          Journal:  Clin J Oncol Nurs        ISSN: 1092-1095            Impact factor:   1.027


  12 in total

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3.  Digital imaging biomarkers feed machine learning for melanoma screening.

Authors:  Daniel S Gareau; Joel Correa da Rosa; Sarah Yagerman; John A Carucci; Nicholas Gulati; Ferran Hueto; Jennifer L DeFazio; Mayte Suárez-Fariñas; Ashfaq Marghoob; James G Krueger
Journal:  Exp Dermatol       Date:  2016-12-19       Impact factor: 3.960

4.  Use of a cognitive computing system for treatment of cervical cancer.

Authors:  Soyi Lim; Kwang Beom Lee
Journal:  J Gynecol Oncol       Date:  2017-05-29       Impact factor: 4.401

5.  Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services.

Authors:  Longxiang Shi; Shijian Li; Xiaoran Yang; Jiaheng Qi; Gang Pan; Binbin Zhou
Journal:  Biomed Res Int       Date:  2017-02-12       Impact factor: 3.411

6.  Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer.

Authors:  Yun Yeong Kim; Se Jeong Oh; Yong Soon Chun; Woon Kee Lee; Heung Kyu Park
Journal:  PLoS One       Date:  2018-07-06       Impact factor: 3.240

7.  An empirical meta-analysis of the life sciences linked open data on the web.

Authors:  Maulik R Kamdar; Mark A Musen
Journal:  Sci Data       Date:  2021-01-21       Impact factor: 6.444

8.  Making sense of big data in health research: Towards an EU action plan.

Authors:  Charles Auffray; Rudi Balling; Inês Barroso; László Bencze; Mikael Benson; Jay Bergeron; Enrique Bernal-Delgado; Niklas Blomberg; Christoph Bock; Ana Conesa; Susanna Del Signore; Christophe Delogne; Peter Devilee; Alberto Di Meglio; Marinus Eijkemans; Paul Flicek; Norbert Graf; Vera Grimm; Henk-Jan Guchelaar; Yi-Ke Guo; Ivo Glynne Gut; Allan Hanbury; Shahid Hanif; Ralf-Dieter Hilgers; Ángel Honrado; D Rod Hose; Jeanine Houwing-Duistermaat; Tim Hubbard; Sophie Helen Janacek; Haralampos Karanikas; Tim Kievits; Manfred Kohler; Andreas Kremer; Jerry Lanfear; Thomas Lengauer; Edith Maes; Theo Meert; Werner Müller; Dörthe Nickel; Peter Oledzki; Bertrand Pedersen; Milan Petkovic; Konstantinos Pliakos; Magnus Rattray; Josep Redón I Màs; Reinhard Schneider; Thierry Sengstag; Xavier Serra-Picamal; Wouter Spek; Lea A I Vaas; Okker van Batenburg; Marc Vandelaer; Peter Varnai; Pablo Villoslada; Juan Antonio Vizcaíno; John Peter Mary Wubbe; Gianluigi Zanetti
Journal:  Genome Med       Date:  2016-06-23       Impact factor: 11.117

9.  Finding relevant free-text radiology reports at scale with IBM Watson Content Analytics: a feasibility study in the UK NHS.

Authors:  Alicja Piotrkowicz; Owen Johnson; Geoff Hall
Journal:  J Biomed Semantics       Date:  2019-11-12

10.  Concordance of Treatment Recommendations for Metastatic Non-Small-Cell Lung Cancer Between Watson for Oncology System and Medical Team.

Authors:  Hai-Sheng You; Chun-Xia Gao; Hai-Bin Wang; Sai-Sai Luo; Si-Ying Chen; Ya-Lin Dong; Jun Lyu; Tao Tian
Journal:  Cancer Manag Res       Date:  2020-03-16       Impact factor: 3.989

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