TY - GEN
T1 - A multi-agent system for detecting adverse drug reactions
AU - Mansour, Ayman
AU - Ying, Hao
AU - Dews, Peter
AU - Ji, Yanqing
AU - Farber, Margo S.
AU - Yen, John
AU - Miller, Richard E.
AU - Massanari, R. Michael
PY - 2010
Y1 - 2010
N2 - Discovering unknown adverse drug reactions (ADRs) as early as possible is highly desirable. Current methods largely rely on passive spontaneous reports, which suffer from serious underreporting, latency, and inconsistent reporting. They are not ideal for early identification of ADRs [5]. In this paper, we propose a multi-agent system approach for ADR detection. A multi-agent system is formed by a community of agents that exchange information and proactively help one another to achieve the goals set by the system designer. We show how agents, equipped with decision rules developed by the physicians on the team, can collaborate to detect signal pairs of potential ADRs. Using the popular agent language JADE [8, 10] and clinical information on 1,000 patients treated at the Detroit Veterans Affairs Medical Center, we have constructed a small group of agents and generated preliminary simulated detection results.
AB - Discovering unknown adverse drug reactions (ADRs) as early as possible is highly desirable. Current methods largely rely on passive spontaneous reports, which suffer from serious underreporting, latency, and inconsistent reporting. They are not ideal for early identification of ADRs [5]. In this paper, we propose a multi-agent system approach for ADR detection. A multi-agent system is formed by a community of agents that exchange information and proactively help one another to achieve the goals set by the system designer. We show how agents, equipped with decision rules developed by the physicians on the team, can collaborate to detect signal pairs of potential ADRs. Using the popular agent language JADE [8, 10] and clinical information on 1,000 patients treated at the Detroit Veterans Affairs Medical Center, we have constructed a small group of agents and generated preliminary simulated detection results.
UR - https://www.scopus.com/pages/publications/77956585947
UR - https://www.scopus.com/pages/publications/77956585947#tab=citedBy
U2 - 10.1109/NAFIPS.2010.5548293
DO - 10.1109/NAFIPS.2010.5548293
M3 - Conference contribution
AN - SCOPUS:77956585947
SN - 9781424478576
T3 - Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
BT - 2010 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'2010
T2 - 2010 Annual North American Fuzzy Information Processing Society Conference, NAFIPS'2010
Y2 - 12 July 2010 through 14 July 2010
ER -