Abstract
Adverse Drug Events (ADEs) are a major health problem, and developing accurate prediction methods may have a significant impact in public health. Ideally, we would like to have predictive methods, that could pinpoint possible ADRs during the drug development process. Unfortunately, most relevant information on possible ADRs is only available after the drug is commercially available. As a first step, we propose using prior information on existing interactions through recommendation systems algorithms. We have evaluated our proposal using data from the ADReCS database with promising results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Also referred to as Adverse Drug Reactions (ADR).
- 2.
References
Cai, M.-C., Xu, Q., Pan, Y.-J., Pan, W., Ji, N., Li, Y.-B., Jin, H.-J., Liu, K., Ji, Z.-L.: ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms. Nucleic Acids Res. 43, 907–913 (2015)
Gower, S.: Netflix Prize and SVD, pp. 1–10 (2014)
Juntti-Patinen, L., Neuvonen, P.: Drug-related deaths in a university central hospital. Eur. J. Clin. Pharmacol. 58(7), 479–482 (2002)
Lazarou, J., Pomeranz, B.H., Corey, P.N.: Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. Jama 279(15), 1200–1205 (1998)
Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, UK (2014)
Martin, K., Bégaud, B., Latry, P., Miremont-Salamé, G., Fourrier, A., Moore, N.: Differences between clinical trials and postmarketing use. Br. J. Clin. Pharmacol. 57(1), 86–92 (2004)
Page, D., Costa, V.S., Natarajan, S., Barnard, A., Peissig, P., Caldwell, M.: Identifying adverse drug events by relational learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 2012, p. 790. NIH Public Access (2012)
Patel, P., Zed, P.J.: Drug-related visits to the emergency department: How big is the problem? Pharmacother. J. Hum. Pharmacol. Drug Ther. 22(7), 915–923 (2002)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2011)
Smolensky, P.: Information processing in dynamical systems: Foundations of harmony theory (1986)
Xu, R., Wang, Q.: Large-scale combining signals from both biomedical literature and the fda adverse event reporting system (faers) to improve post-marketing drug safety signal detection. BMC Bioinform. 15(1), 17 (2014)
Youden, W.J.: Index for rating diagnostic tests. Cancer 3(1), 32–35 (1950)
Acknowledgements
The authors gratefully acknowledge the financial support of Fundação para a Ciência e Tecnologia (FCT), through the research project “ADE - Adverse Drug Effects Detection” (PTDC/EIA-EIA/121686/2010), as well as the Master in Informatics and Computing Engineering (MIEIC) at FEUP.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pinto, D., Costa, P., Camacho, R., Costa, V.S. (2015). Predicting Drugs Adverse Side-Effects Using a Recommender-System. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-24282-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24281-1
Online ISBN: 978-3-319-24282-8
eBook Packages: Computer ScienceComputer Science (R0)