Patient Centered Identification, Attribution, and Ranking of Adverse Drug Events

Adverse drug events (ADEs) trigger a high number of hospital emergency room (ER) visits. Information about ADEs is often available in online drug databases in the form of narrative texts, and serves as the physician's primary reference point for ADE attribution and diagnosis. Manually reviewing these narratives, however, is an error prone and time consuming process, especially due to the prevalence of polypharmacy. So ER health care providers, especially given the heavy volume of traffic in ERs, often either skip this step or at best do it rather perfunctorily. This causes ADEs to be missed or misdiagnosed, often leading to extensive and unnecessary testing and treatment, including hospitalization. In this paper, we present a system that automates the detection of ADEs and provides a list of suspect drugs, ranked by their likelihood of causing the patient's complaints and symptoms. The input data, i.e., Medications and complaints, are obtained from triage notes that often contain descriptive language. Our application utilizes heterogeneous information sources (including drug databases) to refine and transform these descriptions as well as the online database narratives using a natural language processing (NLP) pipeline. We then employ ranking measures to establish correspondence between the complaints and the medications. Our preliminary evaluation based on actual ER cases demonstrates that this system achieves high precision and recall.