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Literature DB >> 26181906

Artificial Intelligence, Big Data, and Cancer.

Hagop Kantarjian¹, Peter Paul Yu².

Abstract

Entities: Disease

Mesh：

Year: 2015 PMID： 26181906 DOI： 10.1001/jamaoncol.2015.1203

Source DB: PubMed Journal: JAMA Oncol ISSN： 2374-2437 Impact factor: 31.777

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Cited

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23 in total

1. Prospects and challenges for clinical decision support in the era of big data.

Authors: Issam El Naqa; Michael R Kosorok; Judy Jin; Michelle Mierzwa; Randall K Ten Haken
Journal: JCO Clin Cancer Inform Date: 2018-11-09

Review 2. New Perspectives on Pheochromocytoma and Paraganglioma: Toward a Molecular Classification.

Authors: Joakim Crona; David Taïeb; Karel Pacak
Journal: Endocr Rev Date: 2017-12-01 Impact factor: 19.871

3. Comprehensive Evaluation of Medical Conditions Associated with Risk of Non-Hodgkin Lymphoma using Medicare Claims ("MedWAS").

Authors: Eric A Engels; Ruth Parsons; Caroline Besson; Lindsay M Morton; Lindsey Enewold; Winnie Ricker; Elizabeth L Yanik; Hannah Arem; April A Austin; Ruth M Pfeiffer
Journal: Cancer Epidemiol Biomarkers Prev Date: 2016-04-26 Impact factor: 4.254

4. Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.

Authors: Andrew E Grothen; Bethany Tennant; Catherine Wang; Andrea Torres; Bonny Bloodgood Sheppard; Glenn Abastillas; Marina Matatova; Jeremy L Warner; Donna R Rivera
Journal: JCO Clin Cancer Inform Date: 2020-11

Artificial Intelligence, Big Data, and Cancer.

1. Prospects and challenges for clinical decision support in the era of big data.

Review 2. New Perspectives on Pheochromocytoma and Paraganglioma: Toward a Molecular Classification.

3. Comprehensive Evaluation of Medical Conditions Associated with Risk of Non-Hodgkin Lymphoma using Medicare Claims ("MedWAS").

4. Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.

5. Restricted working hours in Austrian residency programs : Survey results.

Review 6. Machine learning in oncology: a review.

7. CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment.

Review 8. Machine learning applications to enhance patient specific care for urologic surgery.

9. Learning from big data: are we undertreating older women with high-risk breast cancer?

10. Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits.