Detecting adverse drug reaction with data mining and predicting its severity with machine learning

Abstract

Adverse Drug Reaction (ADR) is one of the many uncertainties that are considered a fatal threat to the pharmacy industry and the field of medical diagnosis. Utmost care is taken to test a new drug thoroughly before it is introduced and made available to the public. However, these pre-clinical trials are not enough on their own to ensure safety. The increasing concern to the ADRs has motivated the development of statistical, data mining and machine learning methods to detect the Adverse Drug Reactions. With the availability of Electronic Health Records (EHRs), it has become possible to detect ADRs with the mentioned technologies. In this work, we have proposed a hybrid model of data mining and machine learning to identify different Adverse Reactions and predict the intensity of the outcome. We have used the Proportionality Reporting Ratio (PRR) along with the precision point estimator test called the Chi-Square test to find out the different relationships between drug and symptoms called the drug-ADR association. This output from the data mining technique is used as an input to the machine learning algorithms such as Random Forest and Support Vector Machine (SVM) to predict the intensity of the outcome of ADR, depending on a patient's demographic data such as gender, weight, age, etc. In this work, we have achieved an accuracy of 91% to predict 'death' as the outcome from an ADR.