Predicting adverse drug reaction outcomes with machine learning

Abstract

Background: Adverse drug reactions are a drug safety issue affecting more than two million people in the U.S. annually. The Food and Drug Administration (FDA) maintains a comprehensive database of adverse drug reactions reported known as FAERS (FDA adverse event reporting system), providing a valuable resource for studying factors associated with ADRs. The goal of the project is to build predictive models to predict the outcome given patient characteristics and drug usage. The results can be valuable for health care practitioners by offering new knowledge on adverse drug reactions which can be used to improve decision making related to drug prescriptions.

Methods: In this paper I present and discuss results from machine learning models used to predict outcomes of ADRs. Machine learning models are a popular set of models for prediction. They have gained attention recently and have been used in a variety of fields. They can be trained on existing data and retrained when new data become available. The trained models are then used to make predictions.

Results: I find that the supervised learning models are work similarly within groups, with accuracy between 65% and 75% for predicting deaths and 70% to 75% for predicting hospitalizations. Across groups the models predict hospitalizations better than deaths.

Conclusions: The predictive models I built achieve good accuracy. The results can potentially be improved when more data become available in the future.