Abstract
At the end of 2014 the Caisse nationale d’Assurance maladie (CNAM) and the École polytechnique signed a three-year research and development partnership agreement. In 2018, it has been renewed for 3 years.
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Notes
- 1.
This partnership is above all a team effort. The results are down to the collaboration of highly multidisciplinary teams. We would like to thank the doctors, developers, researchers, and business experts of CNAM (in particular, Aurélie Bannay, Hélène Caillol, Joël Coste, Claude Gissot, Fanny Leroy, Anke Neumann, Jérémie Rudant, and Alain Weill) and the teams of developers, data scientists, and researchers at the École Polytechnique (in particular, Prosper Burq, Philip Deegan, Nguyen Dinh Phong, Xristos Giastidis, Agathe Guilloux, Daniel de Paula da Silva, Youcef Sebiat, and Dian Sun).
- 2.
See the chapter titled “Medical and Administrative Data on Health Insurance,” p. 75 in this book.
- 3.
Coloma P., “Mining Electronic Healthcare Record Databases to Augment Drug Safety Surveillance,” PhD Manuscript, University Medical Center, Rotterdam, 2012; Morel M., Bacry E., Gaïffas S. et al. “ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection,” ArXiv preprint, 2017; Rajkomar A., Oren E., Chen K. et al. “Scalable and accurate deep learning for electronic health records,” ArXiv preprint, 2018; Shickel B., Tighe P., Bihorac A. et al. “Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis,” ArXiv e-prints, 2018.
- 4.
Cox D., “Regression Models and Life-Tables,” Journal of the Royal Statistical Society, Series B. 1972.
- 5.
Schuemie M., Trifirò G., Coloma P. et al., “Detecting adverse drug reactions following long-term exposure in longitudinal observational data: The exposure- adjusted self-controlled case series,” Statistical Methods in Medical Research, 2014; Whitaker H., Paddy Farrington C., Spiessens B. et al., “Tutorial in biostatistics: The self-controlled case series method,” Statistics in Medicine, 2006.
- 6.
Morel M., Bacry E., Gaïffas S., Gaiffas, A.., Leroy, F. “ConvSCCS: convolutional self-controlled case-series model for lagged adverse event detection”. Biostatistics, kxz003, https://doi.org/10.1093/biostatistics/kxz003 (2019).
- 7.
Neumann A., Weill A., Ricordeau P. et al., “Pioglitazone and risk of bladder cancer among diabetic patients in France: a population-based cohort study,” Diabetologia, 2012.
- 8.
Ibid.
- 9.
Lewis J., Habel L., Quesenberry C. et al., “Pioglitazone use and risk of bladder cancer and other common cancers in persons with diabetes,” JAMA, 2015.
- 10.
Bacry E., Bompaire M., Gaïffas S. et al., “Tick: A Python library for statistical learning, with a particular emphasis on time-dependent modeling,” Journal of Machine Learning Research, 2018.
- 11.
Morel M., Bacry E., Gaïffas S. et al., 2017, art. cit. 12 “SCALPEL3 : a scalable open-source library for healthcare claims databases”, Arxiv preprint (arXiv:1910.07045), 2019.
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Bacry, E., Gaïffas, S. (2020). Machine Learning and Massive Health Data. In: Nordlinger, B., Villani, C., Rus, D. (eds) Healthcare and Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-32161-1_4
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