R. Bender and U. Grouven, Logistic regression models used in medical research are poorly presented, BMJ Br Med J, vol.313, issue.7057, p.628, 1996.
DOI : 10.1136/bmj.313.7057.628

URL : http://europepmc.org/articles/pmc2352066?pdf=render

J. Bergstra and Y. Bengio, Random search for hyper-parameter optimization, J Mach Learn Res, vol.13, pp.281-305, 2012.

C. E. Bonferroni, Il calcolo delle assicurazioni su gruppi di teste. Studi in onore del professore salvatore ortu carboni, pp.13-60, 1935.

W. Boulding, S. W. Glickman, M. P. Manary, K. A. Schulman, and R. Staelin, Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days, Am J Manage Care, vol.17, issue.1, pp.41-49, 2011.

A. Boulesteix and C. Strobl, Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction, BMC Med Res Methodol, vol.9, issue.1, p.85, 2009.
DOI : 10.1186/1471-2288-9-85

URL : https://bmcmedresmethodol.biomedcentral.com/track/pdf/10.1186/1471-2288-9-85

A. P. Bradley, The use of the area under the roc curve in the evaluation of machine learning algorithms, Pattern Recogn, vol.30, issue.7, pp.1145-59, 1997.

L. Breiman, Random forests, Mach Learn, vol.45, issue.1, pp.5-32, 2001.

D. C. Brousseau, P. L. Owens, A. L. Mosso, J. A. Panepinto, and C. A. Steiner, Acute care utilization and rehospitalizations for sickle cell disease, Jama, vol.303, issue.13, pp.1288-94, 2010.
DOI : 10.1001/jama.2010.378

F. H. Bunn, Pathogenesis and treatment of sickle cell disease, N Engl J Med, vol.337, issue.11, pp.762-771, 1997.

S. Bussy, A. Guilloux, S. Gaïffas, and A. Jannot, C-mix: A high-dimensional mixture model for censored durations, with applications to genetic data, vol.0, p.0962280218766389, 2018.
DOI : 10.1177/0962280218766389

URL : https://hal.archives-ouvertes.fr/hal-01648389

H. Chen, R. L. Kodell, K. F. Cheng, and J. J. Chen, Assessment of performance of survival prediction models for cancer prognosis, BMC Med Res Methodol, vol.12, issue.1, p.102, 2012.

D. R. Cox, Regression models and life-tables, J R Stat Soc Ser B Methodol, vol.34, issue.2, pp.187-220, 1972.
DOI : 10.1111/j.2517-6161.1972.tb00899.x

J. J. Dai, L. Lieu, and D. Rocke, Dimension reduction for classification with gene expression microarray data, Stat Appl Genet Mol Biol, vol.5, issue.1, 2006.

J. Escudié, A. Jannot, E. Zapletal, S. Cohen, G. Malamut et al., Reviewing 741 patients records in two hours with fastvisu, AMIA Annual Symposium Proceedings, vol.2015, p.553, 2015.

V. T. Farewell, The use of mixture models for the analysis of sureval data with long-term survivors, Biometrics, vol.38, issue.4, pp.1041-1047, 1982.

T. R. Fleming and D. P. Harrington, Counting processes and survival analysis, vol.169, 2011.
DOI : 10.1002/9781118150672

URL : https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118150672.fmatter

M. J. Frei-jones, J. J. Field, and M. R. Debaun, Risk factors for hospital readmission within 30 days: a new quality measure for children with sickle cell disease, Pediatr Blood Cancer, vol.52, issue.4, pp.481-486, 2009.

B. Friedman and J. Basu, The rate and cost of hospital readmissions for preventable conditions, Med Care Res Rev, vol.61, issue.2, pp.225-265, 2004.

J. H. Friedman, Stochastic gradient boosting, Comput Stat Data Anal, vol.38, issue.4, pp.367-78, 2002.

X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp.315-338, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J Mach Learn Res, vol.3, pp.1157-82, 2003.

D. P. Harrington and T. R. Fleming, A class of rank test procedures for censored survival data, Biometrika, vol.69, issue.3, pp.553-66, 1982.

D. M. Hawkins, The problem of overfitting, J Chem Inf Comput Sci, vol.44, issue.1, pp.1-12, 2004.

P. J. Heagerty and Y. Zheng, Survival model predictive accuracy and roc curves, Biometrics, vol.61, issue.1, pp.92-105, 2005.

D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression, vol.398, 2013.

A. Kalousis, J. Prados, and M. Hilario, Stability of feature selection algorithms: a study on high-dimensional spaces, Knowl Inf Syst, vol.12, issue.1, pp.95-116, 2007.

D. G. Kleinbaum and M. Klein, , vol.3, 2010.

R. P. Kocher and E. Y. Adashi, Hospital readmissions and the affordable care act: paying for coordinated quality care, Jama, vol.306, issue.16, pp.1794-1799, 2011.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, vol.14, pp.1137-1182, 1995.

A. Kuk and C. Chen, A mixture model combining logistic regression with proportional hazards regression, Biometrika, vol.79, issue.3, pp.531-572, 1992.

, Les 131 centres de référencebanque nationale de données maladies rares, p.30, 2014.

J. Little, J. Higgins, J. Ioannidis, D. Moher, F. Gagnon et al., Strengthening the reporting of genetic association studies (strega): an extension of the strobe statement, Hum Genet, vol.125, issue.2, pp.131-51, 2009.

B. H. Menze, B. M. Kelm, R. Masuch, U. Himmelreich, P. Bachert et al., A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data, BMC Bioinforma, vol.10, issue.1, p.213, 2009.

R. T. Mikolajczyk, A. Disilvesto, and J. Zhang, Evaluation of logistic regression reporting in current obstetrics and gynecology literature, Obstet Gynecol, vol.111, issue.2, pp.413-422, 2008.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in python, J Mach Learn Res, vol.12, pp.2825-2855, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, Modelling patient time-series data from electronic health records using gaussian processes, Adv Neural Inf Process Syst Workshop Mach Learn Clin Data Anal, pp.1-4, 2013.

J. Pittman, E. Huang, H. Dressman, C. Horng, S. H. Cheng et al., Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes, Proc Natl Acad Sci U S A, vol.101, issue.22, pp.8431-8437, 2004.

O. S. Platt, B. D. Thorington, D. J. Brambilla, P. F. Milner, W. F. Rosse et al., Pain in sickle cell disease: rates and risk factors, N Engl J Med, vol.325, issue.1, pp.11-17, 1991.

P. E. Puddu and A. Menotti, Artificial neural networks versus proportional hazards cox models to predict 45-year all-cause mortality in the italian rural areas of the seven countries study, BMC Med Res Methodol, vol.12, issue.1, p.100, 2012.

D. C. Rees, A. D. Olujohungbe, N. E. Parker, A. D. Stephens, P. Telfer et al., Guidelines for the management of the acute painful crisis in sickle cell disease, Br J Haematol, vol.120, issue.5, pp.744-52, 2003.

M. W. Rich, V. Beckham, C. Wittenberg, C. L. Leven, K. E. Freedland et al., A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure, N Engl J Med, vol.333, issue.18, pp.1190-1195, 1995.

B. Schölkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, 2002.

N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for cox's proportional hazards model via coordinate descent, J Stat Softw, vol.39, issue.5, p.1, 2011.

L. Tong, C. Erdmann, M. Daldalian, J. Li, and T. Esposito, Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk, BMC Med Res Methodol, vol.16, issue.1, p.26, 2016.

B. Trombert-paviot, A. Rector, R. Baud, P. Zanstra, C. Martin et al., The development of ccam: the new french coding system of clinical procedures, Health Inf Manag, vol.31, issue.1, pp.2-11, 2003.

G. Upton, Fisher's exact test, J R Stat Soc Ser A Stat Soc, pp.395-402, 1992.

J. M. Vinson, M. W. Rich, J. C. Sperry, A. S. Shah, and T. Mcnamara, Early readmission of elderly patients with congestive heart failure, J Am Geriatr Soc, vol.38, issue.12, pp.1290-1295, 1990.

F. Wilcoxon, Individual comparisons by ranking methods, Biom Bull, vol.1, issue.6, pp.80-83, 1945.
DOI : 10.1007/978-1-4612-4380-9_16

, World Health Organization. International statistical classification of diseases and related health problems, World Health Organization, vol.1, 2004.

B. Yegnanarayana, Artificial neural networks: PHI Learning Pvt. Ltd, 2009.

E. Zapletal, N. Rodon, N. Grabar, and P. Degoulet, Methodology of integration of a clinical data warehouse with a clinical information system: the hegp case, MedInfo, pp.193-200, 2010.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J R Stat Soc Ser B Stat Methodol, vol.67, issue.2, pp.301-321, 2005.