L. Asker, H. Boström, I. Karlsson, P. Papapetrou, and J. Zhao, Mining candidates for adverse drug interactions in electronic patient records, Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, 2014.

P. Aspden, J. Wolcott, B. J. Lr, and C. , Generalized random shapelet forests. Committee on Identifying and Preventing Medication Errors, 2007.

H. Dalianis, M. Hassel, A. Henriksson, and M. Skeppstedt, Stockholm epr corpus: A clinical database used to improve health care, Proceedings of the Fourth Swedish Language Technology Conference, 2009.

D. Hand, H. Mannila, and P. Smyth, Principles of Data Mining. Adaptive Computation and Machine Learning Series, 2001.

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.

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.

J. Hollmén, J. K. Seppänen, and H. Mannila, Mixture models and frequent sets: combining global and local methods for 0-1 data, Proceedings of the Third SIAM International Conference on Data Mining, pp.289-293, 2003.

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.

O. P. Rinta-koski, Machine learning in neonatal intensive care, 2018.

O. P. Rinta-koski, S. Sarkka, J. Hollmén, M. Leskinen, and S. Andersson, Gaussian process classification for prediction of in-hospital mortality among preterm infants, Neurocomputing, vol.298, pp.134-141, 2018.

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.

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.