title: A distributed, collaborative intelligent agent system approach for proactive postmarketing drug safety surveillance.
abstract: Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275,000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.
authors.affiliation: Department of Electrical and Computer Engineering Gonzaga University, Spokane, WA 99258, USA. ji@gonzaga.edu
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Ji
authors.forename: Hao
authors.initials: H
authors.lastname: Ying
authors.forename: Margo S
authors.initials: MS
authors.lastname: Farber
authors.forename: John
authors.initials: J
authors.lastname: Yen
authors.forename: Peter
authors.initials: P
authors.lastname: Dews
authors.forename: Richard E
authors.initials: RE
authors.lastname: Miller
authors.forename: R Michael
authors.initials: RM
authors.lastname: Massanari
mesh_terms: D020117:Cisapride / Q000009:adverse effects; D003195:Computer Communication Networks; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D064420:Drug-Related Side Effects and Adverse Reactions; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated; D011358:Product Surveillance, Postmarketing / Q000379:methods*; D012984:Software
chemical_list: D020117:Cisapride