Aggregate-Eliminate-Predict: Detecting Adverse Drug Events from Heterogeneous Electronic Health Records
This work addresses the challenge of adverse drug event detection for healthcare applications, but it is incremental as it builds upon a prior framework with additional features.
The authors tackled the problem of detecting adverse drug events from heterogeneous electronic health records by extending an existing framework to include diagnosis and drug prescription codes alongside lab measurements, resulting in substantial and statistically significant improvements in AUC across five medical datasets.
We study the problem of detecting adverse drug events in electronic healthcare records. The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements. An earlier framework proposed for the same problem demonstrated promising predictive performance for the random forest classifier by using only lab measurements as data features. We extend this framework, by additionally including diagnosis and drug prescription codes, concurrently. In addition, we employ a recursive feature selection mechanism on top, that extracts the top-k most important features. Our experimental evaluation on five medical datasets of adverse drug events and six different classifiers, suggests that the integration of these additional features provides substantial and statistically significant improvements in terms of AUC, while employing medically relevant features.