A rule-engine-based application for over-the-counter medication safety

Abstract

Fatal Accidents involving prescription and over the counter medications have risen at a startling rate over the last 15 years. In the United States, statistics from the Centers for Disease Control and Prevention (CDC) indicate that there were 38,329 fatal drug overdoses in the year 2010, more than double the number observed in 1999. In this thesis, we explore a rule-engine based approach for personalized over-the-counter (OTC) medication safety advisory application. Our application is one of the first attempts towards providing timely advice to a patient on whether it is safe to ingest a particular medication at a given time. This application, which is driven by a rule-engine, takes into account multiple factors such as patient’s health conditions, demographical characteristics and recent drug consumption histories. We present several experiments for studying the effectiveness and efficiency of our system.