Abstract
The growing healthcare industry generates a large amount of data on patient health conditions, demographic plans, and drugs required for such conditions. These attract the attention of the medical professionals and the data scientists alike. In this paper, we propose a drug recommendation assistant built using machine learning techniques and natural language processing, which draws its accuracy from several major datasets. The proposed system makes it possible to manifest the contrasting effects, reviews, ratings, and then recommend the most “effective” drug for a given individual. The results of the predictive analysis were that from 2005–2015, between the ages 55 and 80, the death rates of the top deadliest diseases in the U.S. all increased drastically. Based on the current trends, with some level of accuracy, it is possible to predict the next top five medical conditions (Birth Control, Depression, Pain, Anxiety, Acne) which will be prevalent in the near future and the top five drugs for used to treat them.
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This research is partially supported by a grant from Amazon Web Services.
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Doma, V. et al. (2020). Automated Drug Suggestion Using Machine Learning. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_42
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