Automated Suggestion of Tests for Identifying Likelihood of Adverse Drug Events

Adverse drug events (ADE) caused by use, misuse or sudden discontinuation of medications trigger hospital emergency room visits. Information about a wide range of drugs and associated ADEs is provided in online drug databases in the form of narrative texts. Even though some ADEs can be detected by observable symptoms, several others can only be confirmed by laboratory tests. In this paper, we present a system that provides automated suggestion of tests to identify the likelihood of ADEs. Given a patient's medications and an optional list of signs and symptoms, our system automatically produces the laboratory tests needed to confirm possible ADEs associated with these drugs. The basis of our application is to map clinical symptoms to medical problems and laboratory tests. Towards that, we use template-based extraction and shallow parsing techniques from natural language processing to extract information from narrative texts. We employ relevance ranking measures to establish correspondence between the tests and ADEs. Our evaluation based on a sample set of 40 drugs shows that this system achieves relatively high sensitivity.