J. Bergstra and Y. Bengio, Random search for hyper-parameter optimization, Journal of Machine Learning Research, vol.13, pp.281-305, 2012.

L. Breiman, Random forests, Machine learning, vol.45, issue.1, pp.5-32, 2001.

G. C. Cawley and N. L. Talbot, On over-fitting in model selection and subsequent selection bias in performance evaluation, Journal of Machine Learning Research, vol.11, pp.2079-2107, 2010.

C. C. Chang and C. J. Lin, Libsvm: a library for support vector machines, ACM transactions on intelligent systems and technology (TIST), vol.2, issue.3, p.27, 2011.

. Choi, Gram: graph-based attention model for healthcare representation learning, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.787-795, 2017.

O. Corby and C. F. Zucker, The kgram abstract machine for knowledge graph querying, Web Intelligence and Intelligent Agent Technology (WI-IAT), vol.1, pp.338-341, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01529737

J. Daiber, M. Jakob, C. Hokamp, and P. N. Mendes, Improving efficiency and accuracy in multilingual entity extraction, Proceedings of the 9th International Conference on Semantic Systems, 2013.

G. Forman and M. Scholz, Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement, ACM SIGKDD Explorations Newsletter, vol.12, issue.1, pp.49-57, 2010.

B. A. Goldstein, A. M. Navar, M. J. Pencina, and J. Ioannidis, Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review, Journal of the American Medical Informatics Association, vol.24, issue.1, pp.198-208, 2017.

V. Lacroix-hugues, D. Darmon, C. Pradier, and P. Staccini, Creation of the first french database in primary care using the icpc2: Feasibility study, Studies in health technology and informatics, vol.245, pp.462-466, 2017.

P. Mccullagh and J. A. Nelder, Generalized linear models, vol.37, 1989.

H. Min, H. Mobahi, K. Irvin, S. Avramovic, and J. Wojtusiak, Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology, Journal of biomedical semantics, vol.8, issue.1, p.39, 2017.

F. J. Ordónez, P. De-toledo, and A. Sanchis, Activity recognition using hybrid generative/discriminative models on home environments using binary sensors, Sensors, vol.13, issue.5, pp.5460-5477, 2013.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

J. Pennington, R. Socher, and C. Manning, Glove: Global vectors for word representation, Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp.1532-1543, 2014.

A. G. Salguero, M. Espinilla, P. Delatorre, and J. Medina, Using ontologies for the online recognition of activities of daily living, Sensors, vol.18, issue.4, p.1202, 2018.