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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-99626-4_33
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