S. Timmermans and A. Mauck, The Promises And Pitfalls Of Evidence-Based Medicine, Health Affairs, vol.24, issue.1, pp.18-28, 2005.
DOI : 10.1377/hlthaff.24.1.18

C. V. Powell, G. Maskell, M. Marks, M. South, and C. Robertson, Successful implementation of spacer treatment guideline for acute asthma, Archives of Disease in Childhood, vol.84, issue.2, pp.142-148, 2001.
DOI : 10.1136/adc.84.2.142

P. Gross, S. Greenfield, S. Cretin, J. Ferguson, J. Grimshaw et al., Optimal Methods for Guideline Implementation, Medical Care, vol.39, issue.8 2, pp.85-92, 2001.
DOI : 10.1097/00005650-200108002-00006

L. Keune, V. De-vogel, and H. Van-marle, Methodological development of the Hoeven Outcome Monitor (HOM): A first step towards a more evidence based medicine in forensic mental health, International Journal of Law and Psychiatry, vol.45, pp.43-51, 2016.
DOI : 10.1016/j.ijlp.2016.02.009

W. Raghupathi and V. Raghupathi, Big data analytics in healthcare: promise and potential, Health Information Science and Systems, vol.19, issue.1, pp.10-1186
DOI : 10.1002/9781119205005

URL : https://link.springer.com/content/pdf/10.1186%2F2047-2501-2-3.pdf

I. Website, Available from: https://www.i2b2.org, 2016.

A. Martinez-millana, C. Fernandez-llatas, L. Sacchi, D. Segagni, S. Guillen et al., From data to the decision: A software architecture to integrate predictive modelling in clinical settings, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.2015-8161, 2015.
DOI : 10.1109/EMBC.2015.7320288

. Spe3dlab-website, Available from: www.speelab.io, 2016.

R. Project-website, Available from: https://www.r-project.org Shiny R framework website. Available from: http://shiny.rstudio.com, 2016.

. Rserve-website, Available from: https://rforge 12. dplyr R package web page Available from: https://cran, 2006.

M. Wickham, Package " ggplot2 " 2014. 14. ggrepel R package web page Available from: https://cran.rproject .org/web/packages/ggrepel/index.html, 2016.
DOI : 10.1002/wics.147

K. Hornik, A. Weingessel, and F. Leisch, Davidmeyerr-projectorg MDM. Package " e1071, 2015.

E. Polley, E. Ledell, M. Van-der-laan, and . Package, SuperLearner " : Super Learner Prediction, p.32, 2016.

F. Husson, J. Josse, S. Le, and J. Mazet, Package " FactoMineR, Top Doc, vol.2015, pp.1-95
URL : https://hal.archives-ouvertes.fr/hal-00359835

R. Stats-package-web-page, Available from: https://stat.ethz.ch/R-manual/R- devel/library/stats/html/00Index.html. Accessed June 1, 2016. 21. French Penal Code - Article 222-13. Available from: https

T. Lefèvre, A. Lepresle, and P. Chariot, Detangling complex relationships in forensic data: principles and use of causal networks and their application to clinical forensic science, International Journal of Legal Medicine, vol.340, issue.Suppl 1
DOI : 10.1126/science.1236536

C. Alston, K. Mengersen, and A. Pettitt, Case Studies in Bayesian Statistical Modelling and Analysis, 2012.
DOI : 10.1002/9781118394472

F. Taroni, C. Aitken, and P. Garbolino, B iedermann a. Bayesian Networks and Probabilistic Inference in Forensic Science Statistics in Practice, 2006.

J. Gámez, J. Mateo, and J. Puerta, Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood, Data Mining and Knowledge Discovery, vol.8, issue.4, pp.106-154, 2011.
DOI : 10.1091/mbc.9.12.3273

Y. Chiu, Machine Learning with R Cookbook, 2015.

S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective, 2015.

L. Fiore and P. Lavori, Integrating Randomized Comparative Effectiveness Research with Patient Care, New England Journal of Medicine, vol.374, issue.22, pp.2152-2160, 2016.
DOI : 10.1056/NEJMra1510057

URL : http://www.opm.co.uk/wp-content/uploads/2015/07/hra-sciencewise-dialogue-report-final-july-2015-recruiting-participants.pdf

M. Scutari, Learning Bayesian Networks with the bnlearn R Package, J Stat Softw, vol.35, issue.3, pp.1-22, 2010.

F. Santos, P. Guyomarc-'h, and J. Bruzek, Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines, Forensic Science International, vol.245, 2014.
DOI : 10.1016/j.forsciint.2014.10.010

C. Dang, T. Phuong, M. Beddag, A. Vega, and C. Denis, A data model for clinical legal medicine practice and the development of a dedicated software for both practitioners and researchers, Journal of Forensic and Legal Medicine
DOI : 10.1016/j.jflm.2016.11.002

S. Liu and S. Young, A survey of social media data analysis for physical activity surveillance, Journal of Forensic and Legal Medicine
DOI : 10.1016/j.jflm.2016.10.019

M. Jaulent, D. Leprovost, J. Charlet, and R. Choquet, Semantic interoperability challenges to process large amount of data perspectives in forensic and legal medicine, Journal of Forensic and Legal Medicine
DOI : 10.1016/j.jflm.2016.10.002

URL : https://hal.archives-ouvertes.fr/hal-01431816