A. R. Bath-p, Identification of risk factors for 15-year mortality among community-dwelling older people using Cox regression and a genetic algorithm, Journal of Gerontology, issue.8, pp.60-1052, 2005.

A. S. Horn-j, H. O. Zhu-q, S. J. Tatonetti-n, . Vilar-s, . Brochhausen-m et al., Toward a complete dataset of drug-drug interaction information from publicly available sources, J Biomed Inform, vol.55, pp.206-223, 2015.

B. D. Cullen-d, L. N. Petersen-l, S. D. Small-s, . Laffel-g, S. B. Sweitzer-b et al., Incidence of adverse drug events and potential adverse drug events, JAMA, vol.274, pp.29-34, 1995.

B. J. Keyes-m, Technology and patient safety: a two-edged sword, Biomed Instrum Technol, vol.36, pp.84-92, 2002.

B. A. Bousquet-c, C. I. Sabatier-b, and . Degoulet-p, Specification of business rules for the development of hospital alarm system: application to the pharmaceutical validation, In Stud Health Technol Inform, pp.145-50, 2008.

B. L. Pasquier-n and H. C. Collard-m, Hasar: mining sequential association rules for atherosclerosis risk factor analysis, PKDD, pp.14-25, 2004.

D. L. Brodzinski-h, Z. H. , L. Q. Lingren-t, A. E. Kirkendall-e, and . Solti-i, Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department, J Am Med Inform Assoc, issue.e2, pp.20-212, 2014.

D. F. Squizzato-a and . Ageno-w, The metabolic syndrome as a risk factor for venous and arterial thrombosis, Semin Thromb Hemost, vol.35, issue.5, pp.451-458, 2009.

D. J. Stalhandske-e and B. J. Nudell-t, Using health care failure mode and effect analysis: the va national center for patient safety's prospective risk analysis system, 2002.

D. S. , A. C. Miller-r, and S. A. Johnson-k, Extracting drug-drug interaction articles from Medline to improve the content of drug databases, AMIA Symp, pp.216-236, 2005.

D. Med and H. News, Grapefruit: enemy of many medications. in some patients, the interaction of fruit and drug may put their life and health at risk, Duke Med Health News, vol.19, issue.2, pp.1-2, 2013.

D. M. Grabar-n, Semantic distance-based creation of clusters of pharmacovigilance terms and their evaluation, J Biomed Inform, vol.54, pp.174-185, 2015.

F. N. Lawrentschuk-n, Risk of developing prostate cancer in the future: overview of prognostic biomarkers, Urology, vol.73, issue.5, pp.21-28, 2009.

G. R. Field-t, R. B. , H. P. , and T. J. Hennekens-c, Evidence for a positive linear relation between blood pressure and mortality in elderly people, Lancet, issue.8953, pp.345-825, 1995.

G. D. Derendorf-h, Grapefruit-medication interactions, CMAJ, vol.185, issue.6, p.507, 2013.

H. T. Graña-m, R. V. , and G. N. Naya-h, Identification of relations between risk factors and their pathologies or health conditions by mining scientific literature, Medinfo, pp.964-972, 2010.

H. S. and R. M. Parvizi-j, Minimising the risk of infection: a peri-operative checklist, Bone Joint J, vol.1, pp.98-116, 2016.

H. M. Szolovits-p and M. L. Numans-m, Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer, Artif Intell Med, 2016.

H. J. Kaufman-d and . Patel-v, Computer-based drug ordering: evaluation of interaction with a decision-support system, Stud Health Technol Inform, vol.107, issue.2, pp.1063-1070, 2004.

J. E. Lees-n and M. G. Dixon-woods-m, How well is quality improvement described in the perioperative care literature ? a systematic review, Jt Comm J Qual Patient Saf, vol.42, issue.5, pp.196-216, 2016.

K. I. Van-rooyen-m, Text mining for insurance claim cost prediction, Date Mining LNAI 3755, pp.192-202, 2006.

L. S. Tang-b and C. Q. Wang-x, Drug-drug interaction extraction via convolutional neural networks, 2016.

M. P. Galan-p, H. D. Bertrais-s, and H. S. Ménard-j, High plasma aldosterone and low renin predict blood pressure increase and hypertension in middle-aged caucasian populations, J Hum Hypertens, issue.8, pp.22-550, 2008.

M. R. Lindquist-m and E. A. Edwards-i, Signal selection and follow-up in pharmacovigilance, Drug Saf, vol.25, issue.6, pp.459-465, 2002.

P. C. and J. L. Benetos-a, Heart rate as a risk factor for cardiovascular disease, Prog Cardiovasc Dis, vol.52, issue.1, pp.6-10, 2009.

P. L. Fernandes-l and . J. Alvarez-leite, Host cholesterol and inflammation as common key regulators of toxoplasmosis and artherosclerosis development, Expert Rev Anti Infect Ther, vol.7, issue.7, pp.807-826, 2009.

R. S. Booth-s and . C. Et-al, Prescription errors in uk critical care units, Anaesthesia, vol.59, pp.1193-200, 2004.

T. V. Stoicu-tivadar, Patient empowerment by increasing the understanding of medical language for lay users, Methods Inf Med, vol.52, issue.5, pp.454-62, 2013.

T. G. , P. A. , C. P. , K. J. Polimeni-g, C. G. Miremont-salamé et al., Eu-adr group. data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor ?, Pharmacoepidemiol Drug Saf, issue.12, pp.18-1176, 2009.

T. C. Hodgson-s, Genetic predisposition to cancer, Clin Med, vol.5, issue.5, pp.491-499, 2005.

V. C. Taylor- and A. S. Stanhope-n, Framework for analysing risk and safety in clinical medicine, BMJ, issue.7138, pp.316-1154, 1998.

W. Y. , L. J. Hao-s, . Xu-h, J. A. Shin, . Liu-r et al., Nlp based congestive heart failure case finding: A prospective analysis on statewide electronic medical records, Int J Med Inform, vol.84, issue.12, pp.1039-1086, 2015.

W. S. , W. R. , and G. R. Harrison-b, Epidemiology of medical errors, BMJ, vol.320, pp.774-781, 2000.

W. S. and N. A. Rascher-w, The safety of drug therapy in children, Dtsch Arztebl Int, vol.112, issue.45, pp.781-788, 2015.