Machine Learning-Based Modeling of Big Clinical Trials Data for Adverse Outcome Prediction: A Case Study of Death Events

摘要

It is known that clinical trials have potential risks for participants, which could result in unexpected adverse events. To quantify and predict the risk of adverse outcomes, we leverage a large amount of clinical reports to build machine learning models to predict adverse outcomes. We focused on death events as the predicting target in this study. From Clinicaltrial.gov, we collected 28,340 reports and transformed the data into vectorized machine learning features. These features were harmonized across studies using semantic mapping and feature selection techniques. The resulting selected clinical trial features were used to build five machine learning models for prediction. We evaluated and compared relative model performances for the prediction task. Results show that the logistic regression algorithm achieved the best overall receiver operating characteristic score at 0.7344. This exploratory study showed that it is feasible to use clinical trial factors to predict adverse outcomes. We demonstrated the approach by focusing on building machine learning models to predict death outcomes. Predicting adverse outcomes could help clinical trials estimate harmful risks and design better mechanisms to protect participants. We hope by using our models, a clinical trial expert will be able to assess whether serious adverse events are likely to occur in a clinical trial at the early stage and to estimate what potential trial factors could contribute to the potential serious adverse events.