Machine learning drugs side effects prediction
Date Issued
2023-07
Author(s)
Gavrilov, Zoran
Madevska Bogdanova, Ana
Abstract
Adverse drug reactions can be the cause of
hospitalization, increased morbidity and mortality, withdrawal
of drugs from the market and consequently increased costs of the
healthcare system. Current methods for predicting and assessing
potential side effects are challenging in terms of costs and
efficiency. Machine learning could be implemented for
predicting the side effects of drugs. Therefore, we present
machine learning classifier for predicting drugs side effects
using different supervised learning models on a dataset consisted
of chemical, biological and phenotypic features. Compared to
other machine learning models for prediction of side effects of
drugs, our model has similar and comparable performance.
Machine learning probably wouldn't be able to predict all side
effects, but it could help scientists to notice potential problems
early and develop safer drugs in the future.
hospitalization, increased morbidity and mortality, withdrawal
of drugs from the market and consequently increased costs of the
healthcare system. Current methods for predicting and assessing
potential side effects are challenging in terms of costs and
efficiency. Machine learning could be implemented for
predicting the side effects of drugs. Therefore, we present
machine learning classifier for predicting drugs side effects
using different supervised learning models on a dataset consisted
of chemical, biological and phenotypic features. Compared to
other machine learning models for prediction of side effects of
drugs, our model has similar and comparable performance.
Machine learning probably wouldn't be able to predict all side
effects, but it could help scientists to notice potential problems
early and develop safer drugs in the future.
Subjects
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