M. Segal, E. Rollins, K. Hodges, and M. Roozeboom, Medicare-Medicaid eligible beneficiaries and potentially avoidable hospitalizations, Medicare Medicaid Res Rev, vol.4, issue.1, 2014.

T. Freund, S. Campbell, S. Geissler, C. Kunz, C. Mahler et al., Strategies for reducing potentially avoidable hospitalizations for ambulatory caresensitive conditions, Ann Fam Med, vol.11, issue.4, pp.363-370, 2013.

G. Mercier, V. Georgescu, and J. Bousquet, Geographic variation in potentially avoidable hospitalizations in France, vol.34, pp.836-843, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01997130

T. Ngo, V. Georgescu, T. Libourel, A. Laurent, and G. Mercier, Spatial gradual patterns: Application to the measurement of potentially avoidable hospitalizations, Proc. of the SOFSEM Int. Conf., Austria, pp.596-608, 2018.

J. Gao, E. Moran, Y. Li, and P. Almenoff, Predicting potentially avoidable hospitalizations, Med Care, vol.52, issue.2, pp.164-71, 2014.

A. B. Bindman, K. Grumbach, D. Osmond, M. Komaromy, K. Vranizan et al., Preventable hospitalizations and access to health care, JAMA, vol.274, issue.4, pp.305-316, 1995.

R. Bourret, G. Mercier, J. Mercier, O. Jonquet, J. E. De-la-coussaye et al., Comparison of two methods to report potentially avoidable hospitalizations in France in 2012: a cross-sectional study, BMC Health Serv Res, vol.15, p.4, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01756614

V. Vapnik and A. , Lerner: Pattern recognition using generalized portrait method. Automation and Remote Control, vol.24, pp.774-780, 1963.

T. B. Trafalis and H. Ince, Support vector machine for regression and applications to financial forecasting, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN, vol.6, pp.348-353, 2000.

C. Cortes and V. Vapnik, Support vector networks. M Learning, vol.20, pp.273-297, 1995.

A. J. Smola and B. Schlkopf, A Tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.

T. Chai and R. R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature, Geosci. Model Dev, vol.7, pp.1247-1250, 2014.