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Accueil Tous les numéros Volume 33 (Novembre 2017) Hors série n°1 Med Sci (Paris), 33 (2017) 39-45 Références
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Numéro
Med Sci (Paris)
Volume 33, Novembre 2017
Les Cahiers de Myologie
Page(s) 39 - 45
Section Mise au point
DOI https://doi.org/10.1051/medsci/201733s108
Publié en ligne 15 novembre 2017
  1. Medicine USNLo. What is precision medicine? April 4, 2017. [Google Scholar]
  2. Hawgood S, Hook-Barnard IG, O’Brien TC, Yamamoto KR. Precision medicine: Beyond the inflection point. Sci Transl Med 2015; 7 : 300ps17. [CrossRef] [PubMed] [Google Scholar]
  3. Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. Nat Biotechnol 2012 ; 29 : 613–624. [Google Scholar]
  4. Tie J, Wang Y, Tomasetti C, et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci Transl Med 2016; 8 : 346ra92. [CrossRef] [PubMed] [Google Scholar]
  5. Ghasemi M, Nabipour I, Omrani A, et al. Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. Am J Nucl Med Mol Imaging 2016 ; 6 : 310–327. [PubMed] [Google Scholar]
  6. Martin SD, Coukos G, Holt RA, Nelson BH. Targeting the undruggable: immunotherapy meets personalized oncology in the genomic era. Ann Oncol 2015 ; 26 : 2367–2374. [PubMed] [Google Scholar]
  7. Mohebian MR, Marateb HR, Mansourian M, et al. A hybrid computer-aided-diagnosis system for prediction of breast cancer recurrence (HPBCR) using optimized ensemble learning. Comput Struct Biotechnol J 2017 ; 15 : 75–85. [CrossRef] [PubMed] [Google Scholar]
  8. Beckmann E, Vignaux JJ. Les objets connectés peuvent-ils aider les patients atteints de pathologies neuromusculaires ?. Med Sci (Paris) 2016 ; 32 (hs2) : 22–26. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  9. Hood L, Auffray C. Participatory medicine: a driving force for revolutionizing healthcare. Genome Med 2013 ; 5 : 110. [CrossRef] [PubMed] [Google Scholar]
  10. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2014 ; 2 : 3. [CrossRef] [PubMed] [Google Scholar]
  11. Tual M. Comment le « deep learning » révolutionne l’intelligence artificielle. Le Monde 24.07.2015. [Google Scholar]
  12. Nasr-Esfahani E, Samavi S, Karimi N, et al. Melanoma detection by analysis of clinical images using convolutional neural network. Conf Proc IEEE Eng Med Biol Soc 2016 ; 2016 : 1373–1376. [PubMed] [Google Scholar]
  13. Cruz-Roa A, Gilmore H, Basavanhally A, et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci Rep 2017 ; 7 : 46450. [CrossRef] [PubMed] [Google Scholar]
  14. Meyer S, Mueller K, Stuke K, et al. Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data. Neuroimage Clin 2017 ; 14 : 656–662. [Google Scholar]
  15. Saman Sarraf DDD, John A.E. Anderson, Ghassem Tofighi. DeepAD: Alzheimer’s disease classification viadeep convolutional neural networks using MRI and fMRI. Alzheimer’s Disease Neuroimaging Initiativ 2016. [Google Scholar]
  16. Vandenberghe ME, Scott ML, Scorer PW, et al. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep 2017 ; 7 : 45938. [CrossRef] [PubMed] [Google Scholar]
  17. Masood A, Al-Jumaily A. Semi-advised learning model for skin cancer diagnosis based on histopathalogical images. Conf Proc IEEE Eng Med Biol Soc 2016 ; 2016 : 631–634. [PubMed] [Google Scholar]
  18. Wang J. Deep learning based diagnostics: unlocking a $16 billion market. Ark Inves, December 2016 (available from: https://ark-invest.com/research/deep-learning-based-diagnostics). [Google Scholar]
  19. Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 2016 ; 6 : 26094. [CrossRef] [PubMed] [Google Scholar]
  20. Densen P. Challenges and opportunities facing medical education. Trans Am Clin Climatol Assoc 2011 ; 122 : 48–58. [PubMed] [Google Scholar]
  21. Zhao L, Chen J, Chen F, et al. Social media nested epidemic simulation via online semi-supervised deep learning. Proc IEEE Int Conf Data Min 2015 ; 2015 : 639–648. [PubMed] [Google Scholar]
  22. Wicks P. Could digital patient communities be the launch pad for patient-centric trial design?. Trials 2014 ; 15 : 172. [CrossRef] [PubMed] [Google Scholar]
  23. Cocos A, Fiks AG, Masino AJ. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. J Am Med Inform Assoc 2017 ; 24 : 813–821. [CrossRef] [PubMed] [Google Scholar]
  24. Picton G. Study shows promise in automated reasoning, hypothesis generation over complete medical literature. Baylor College of Medicine, 2014, Aug 25 (Available from: https://www.bcm.edu/news/research/automated-reasoning-hypothesis-generation?cm_mc_uid=46272480081214888855617&cm_mc_sid_50200000=1493398142). [Google Scholar]
  25. BewellConnect. Santé - Mieux connaître les risques pour mieux les prévenir (Available from: https://bewell-connect.com/sante/). [Google Scholar]
  26. HIMSS. What is Interoperability? 2013 (available from: http://www.himss.org/library/interoperability-standards/what-is-interoperability). [Google Scholar]
  27. OMIM Gene Map Statistics [Internet]. Updated September 20th, 2017 (available from: https://www.omim.org/statistics/geneMap). [Google Scholar]
  28. OMIM Update List [Internet]. 2017 (available from : https://www.omim.org/statistics/update). [Google Scholar]
  29. AFM-Téléthon. Maladies neuromusculaires 2016 (available from: http://www.afm-telethon.fr/maladies-neuromusculaires-6676). [Google Scholar]
  30. Donnart A. L’errance de diagnostic-Erradiag, résultats de l’enquête sur l’errance diagnostique: alliances maladies rares (available from : http://www.alliance-maladies-rares.org/lerrance-de-diagnostic/). [Google Scholar]
  31. Travers Ching DSHA. Opportunities and obstacles for deep learning in biology and medicine. BioRxiv 2017, May 28. [Google Scholar]
  32. Ingo Schnabel MP. Goal-driven software development. In: Society IC, ed. Proceedings of the 30th Annual IEEE/NASA Software Engineering Workshop, April 24–28, 2006. [Google Scholar]

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