J. Adler-milstein, A. J. Holmgren, P. Kralovec, C. Worzala, T. Searcy et al., Electronic health record adoption in u.s. hospitals: The emergence of a digital 'advanced use' divide, Journal of the American Medical Informatics Association, vol.24, issue.6, pp.1142-1148, 2017.

G. Andrew and J. Gao, Scalable training of l 1-regularized loglinear models, Machine Learning, Proceedings of the International Conference (ICML), pp.33-40, 2007.

A. Avati, K. Jung, S. Harman, L. Downing, A. Y. Ng et al., Improving palliative care with deep learning, Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp.311-316, 2017.

R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm et al., Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1721-1730, 2015.

Y. Choi, C. Y. Chiu, and D. Sontag, Learning low-dimensional representations of medical concepts, AMIA Summits on Translational Science Proceedings, pp.41-50, 2016.

J. Davis and M. Goadrich, The relationship between precision-recall and ROC curves, Machine Learning, Proceedings of the International Conference (ICML), pp.233-240, 2006.

T. G. Dietterich, Ensemble methods in machine learning, Intl workshop on multiple classifier systems, pp.1-15, 2000.

T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett, vol.27, issue.8, pp.861-874, 2006.

P. Genevès, T. Calmant, N. Layaïda, M. Lepelley, S. Artemova et al., Scalable machine learning for predicting at-risk profiles upon hospital admission, Big Data Research, issue.12, pp.23-34, 2018.

J. George, Y. Phun, M. J. Bailey, D. C. Kong, and K. Stewart, Development and validation of the medication regimen complexity index, Annals of Pharmacotherapy, vol.38, issue.9, pp.1369-1376, 2004.

R. Guidotti, A. Monreale, F. Turini, D. Pedreschi, and F. Giannotti, A survey of methods for explaining black box models, 2018.

J. Henry, Y. Pylypchuk, T. Searcy, and V. Patel, Adoption of electronic health record systems among u.s. non-federal acute care hospitals, 2016.

G. King and L. Zeng, Logistic regression in rare events data, Political Analysis, vol.9, issue.2, pp.137-163, 2001.

M. Lepelley, C. Genty, A. Lecoanet, B. Allenet, P. Bedouch et al., Electronic medication regimen complexity index at admission and complications during hospitalization in medical wards: a tool to improve quality of care?, International Journal for Quality in Health Care, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02007687

R. Makadia and P. B. Ryan, Transforming the premier perspective®hospital database into the observational medical outcomes partnership (omop) common data model. eGEMs, vol.2, p.1110, 2014.

C. Me, P. P. , A. Kl, and M. Cr, A new method of classifying prognostic comorbidity in longitudinal studies: development and validation, J Chronic Dis, vol.40, issue.5, pp.373-83, 1987.

X. Meng, J. Bradley, B. Yavuz, E. Sparks, S. Venkataraman et al., Machine learning in Apache Spark. CoRR, 2015.

R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, Deep patient: An unsupervised representation to predict the future of patients from the electronic health records, Scientific Reports, vol.6, 2016.

. Premier, Healthcare database, 2018.

A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj et al., Scalable and accurate deep learning with electronic health records, npj Digital Medicine, vol.1, issue.1, p.18, 2018.

D. Roosan, M. Samore, M. Jones, Y. Livnat, and J. Clutter, Bigdata based decision-support systems to improve clinicians' cognition, Proceedings of the IEEE International Conference on Healthcare Informatics, pp.285-288, 2016.
DOI : 10.1109/ichi.2016.39

URL : http://europepmc.org/articles/pmc5161104?pdf=render

B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi, Deep ehr: A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis, IEEE Journal of Biomedical and Health Informatics, 2017.