F. Bagattini, I. Karlsson, J. Rebane, and P. Papapetrou, A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records, BMC Medical Informatics and Decision Making, vol.19, issue.1, 2019.

D. W. Bates, E. B. Miller, D. J. Cullen, L. Burdick, L. Williams et al., for the ADE Prevention Study Group: Patient Risk Factors for Adverse Drug Events in Hospitalized Patients, Archives of Internal Medicine, vol.159, issue.21, pp.2553-2560, 1999.

, Detecting ADEs from Heterogeneous Medical Sources 11

B. K. Beaulieu-jones, D. R. Lavage, J. W. Snyder, J. H. Moore, S. A. Pendergrass et al., Characterizing and managing missing structured data in electronic health records: Data analysis, JMIR Med Inform, vol.6, issue.1, p.11, 2018.

H. Cao, M. Markatou, G. B. Melton, M. F. Chiang, and G. Hripcsak, Handling temporality of clinical events for drug safety surveillance, AMIA Proceedings, pp.106-110, 2005.

A. Daveluy, C. Raignoux, G. Miremont-salam, P. Girodet, N. Moore et al., Drug interactions between inhaled corticosteroids and enzymatic inhibitors, Eur J Clin Pharmacol, vol.65, issue.7, pp.743-745, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00534957

T. Desautels, J. Calvert, J. Hoffman, Q. Mao, M. Jay et al., Using transfer learning for improved mortality prediction in a data-scarce hospital setting, Biomedical Informatics Insights, vol.9, 2017.

D. Fitzmaurice, A. Blann, and G. Lip, Bleeding risks of antithrombotic therapy, British Medical Journal, vol.325, issue.7368, pp.828-831, 2002.

R. Freeman, L. Moore, L. Garca-lvarez, A. Charlett, and A. Holmes, Advances in electronic surveillance for healthcare-associated infections in the 21st century: a systematic review, J Hosp Infect, vol.84, issue.2, pp.106-119, 2013.

T. Gentimis, A. J. Alnaser, A. Durante, K. Cook, and R. Steele, Predicting hospital length of stay using neural networks on mimic iii data, 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, pp.1194-1201, 2017.

R. Harpaz, K. Haerian, H. S. Chase, and C. Friedman, Mining electronic health records for adverse drug effects using regression based methods, the 1st ACM International Health Informatics Symposium, pp.100-107, 2010.

A. Henriksson, M. Kvist, H. Dalianis, and M. Duneld, Identifying adverse drug event information in clinical notes with distributional semantic representations of context, Journal of Biomedical Informatics, vol.57, pp.333-349, 2015.

A. Henriksson, J. Zhao, H. Boström, and H. Dalianis, Modeling heterogeneous clinical sequence data in semantic space for adverse drug event detection, IEEE International Conference on Data Science and Advanced Analytics, pp.1-8, 2015.

W. R. Hersh, Adding value to the electronic health record through secondary use of data for quality assurance, research, and surveillance, Clin Pharmacol Ther, vol.81, pp.126-128, 2007.

T. Hielscher, M. Spiliopoulou, H. Völzke, and J. Kühn, Mining longitudinal epidemiological data to understand a reversible disorder, International Symposium on Intelligent Data Analysis, pp.120-130, 2014.

B. Honigman, J. Lee, J. Rothschild, P. Light, R. Pulling et al., Using computerized data to identify adverse drug events in outpatients, Journal of the American Medical Informatics Association, vol.8, issue.3, pp.254-266, 2001.

R. Howard, A. Avery, S. Slavenburg, S. Royal, G. Pipe et al., Which drugs cause preventable admissions to hospital? a systematic review, British journal of clinical pharmacology, vol.63, issue.2, pp.136-147, 2007.

P. B. Jensen, L. J. Jensen, and S. Brunak, Mining electronic health records: towards better research applications and clinical care, Nature Reviews Genetics, vol.13, issue.6, pp.395-405, 2012.

M. Kursa and W. Rudnicki, Feature selection with the boruta package, Journal of Statistical Software, vol.36, issue.11, pp.1-13, 2010.

C. Allaart,

M. B. Kursa, A. Jankowski, and W. R. Rudnicki, Boruta -a system for feature selection, Fundam. Inf, vol.101, issue.4, pp.271-285, 2010.

F. Kury and O. Bodenreider, Desiderata for drug classification systems for their use in analyzing large drug prescription datasets, Proceedings of the 3rd Workshop on Data Mining for Medical Informatics, 2016.

J. R. Nebeker, P. Barach, and M. H. Samore, Clarifying adverse drug events: a clinician's guide to terminology, documentation, and reporting, Annals of internal medicine, vol.140, issue.10, pp.795-801, 2004.

G. N. Norén, T. Bergvall, P. B. Ryan, K. Juhlin, M. J. Schuemie et al., Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: Lessons for developing a risk identification and analysis system, Drug Safety, vol.36, issue.1, pp.107-121, 2013.

K. Ouchi, C. Lindvall, P. R. Chai, and E. W. Boyer, Machine learning to predict, detect, and intervene older adults vulnerable for adverse drug events in the emergency department, Journal of Medical Toxicology, vol.14, issue.3, pp.248-252, 2018.

S. V. Pakhomov, J. Buntrock, and C. G. Chute, Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier, Journal of Biomedical Informatics, vol.38, issue.2, pp.145-153, 2005.

M. Y. Park, D. Yoon, K. Lee, S. Y. Kang, I. Park et al., A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database, Pharmacoepidemiology and Drug Safety, vol.20, issue.6, pp.598-607, 2011.

E. P. Van-puijenbroek, A. Bate, H. G. Leufkens, M. Lindquist, R. Orre et al., A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions, Pharmacoepidemiology and Drug Safety, vol.11, issue.1, pp.3-10, 2002.

P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, pp.53-65, 1987.

A. Sarker, R. Ginn, A. Nikfarjam, K. Oconnor, K. Smith et al., Utilizing social media data for pharmacovigilance: a review, Journal of Biomedical Informatics, vol.54, pp.202-212, 2015.

M. J. Schuemie, P. M. Coloma, H. Straatman, R. M. Herings, G. Trifirò et al., Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods, Medical Care, vol.50, issue.10, pp.890-897, 2012.

D. J. Scott, J. Lee, I. Silva, S. Park, G. B. Moody et al., Accessing the public mimic-ii intensive care relational database for clinical research, BMC Medical Informatics and Decision Making, vol.13, issue.1, 2013.

Ö. Uzuner, I. Goldstein, Y. Luo, and I. Kohane, Identifying patient smoking status from medical discharge records, Journal of the American Medical Informatics Association, vol.15, issue.1, pp.14-24, 2008.

N. G. Weiskopf, G. Hripcsak, S. Swaminathan, and C. Weng, Defining and measuring completeness of electronic health records for secondary use, Journal of Biomedical Informatics, vol.46, issue.5, pp.830-836, 2013.

J. Zhao, A. Henriksson, L. Asker, and H. Boström, Predictive modeling of structured electronic health records for adverse drug event detection, BMC Medical Informatics and Decision Making, vol.15, p.1, 2015.