Fuzzy Rule-Based Approach for Detecting Adverse Drug Reaction Signal Pairs

Detecting Adverse Drug Reactions (ADR) signal pairs is technically a complex problem. This is the case if we realistically assume that there does not exist a set of rules that are readily acceptable to all human experts (e. g., physicians, epidemiologists and pharmacists). The parameters used in identifying the signal pairs are really a vague, subjective measure rather than an objective measure. Furthermore, human experts often disagree one another owing to their knowledge and experiences and there is no "ground truth" to indicate which physician is right or wrong. Because of this and other limitations, current surveillance systems are not ideal for rapidly identifying rare unknown ADRs. A more effective system is needed as the electronic patient records become more and more easily accessible in various health organizations such as hospitals, medical centers and insurance companies. These data provide a new source of information that has great potentials to detect ADR signals much earlier. In this paper we have designed and developed a fuzzy inference engine for finding the causal relationship between a drug and an adverse reaction. The reasoning is based on a fuzzy inference system implemented using the freeware FuzzyJess. Fuzzy logic is used to represent, interpret, and compute vague and/or subjective information which is very common in medicine. The Detector is a fuzzy rule-based system. Using clinical information of more than 10,000 patients treated at the Detroit Veterans Affairs Medical Center, we have generated preliminary simulated detection results.