Abstract
This paper addresses the problem of polypharmacy management in older patients with dementia. We propose a technique that combines semantic technologies with big data machine learning techniques to detect deviations in daily activities which may signal the side effects of a drug-drug interaction. A polypharmacy management knowledge base was developed and used to semantically define drug-drug interactions and to annotate with the help of doctors significant registered deviations from the elders’ routines. The Random Forest Classifier is used to detect the days with significant deviations, while the k-means clustering algorithm is used to automate the deviations annotation process. The results are promising showing that such an approach can be successfully applied for assisting doctors in identifying the effects of polypharmacy in the case of patients with dementia.
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
Similar content being viewed by others
References
Zazo, M.H., et al.: An ontology for drug-drug interactions. In: Proceedings of the 6th International Workshop on Semantic Web Applications and Tools for Life Sciences (2014)
Ujager, F.S.: Evaluation of wellness detection techniques using complex activities association for smart home ambient. Int. J. Adv. Comput. Sci. Appl. 7(8), 243–253 (2016)
Ordonez, F.J.: Sensor-based bayesian detection of anomalous living patterns in a home setting. Pers. Ubiquitous Comput. J. 19(2), 259–270 (2015)
Hoque, E., et al.: Holmes: a comprehensive anomaly detection system for daily in-home activities. In: Proceedings of the 2015 International Conference on Distributed Computing in Sensor Systems, pp. 40-51 (2015)
Civitarese, G., Bettini, C.: Monitoring objects manipulations to detect abnormal behaviors. In: Proceedings of the International Conference on Pervasive Computing and Communications Workshops (2017)
Noy, N.F., McGuinness, D.L.: Ontology development 101: a guide to creating your first ontology (2001). https://protegewiki.stanford.edu/wiki/Ontology101
https://betterhealthwhileaging.net/medications-to-treat-difficult-alzheimers-behaviors/
https://www.alz.org/alzheimers_disease_standard_prescriptions.asp
Acknowledgements
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI UEFISCDI and of the AAL Programme with co-funding from the European Union’s Horizon 2020 research and innovation programme project number AAL 44 / 2017 within PNCDI III.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chifu, V., Pop, C., Cioara, T., Anghel, I., Moldovan, D., Salomie, I. (2018). Identifying the Polypharmacy Side-Effects in Daily Life Activities of Elders with Dementia. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-99626-4_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99625-7
Online ISBN: 978-3-319-99626-4
eBook Packages: EngineeringEngineering (R0)