TY - GEN
T1 - Comparing data-mining algorithms developed for longitudinal observational databases
AU - Reps, Jenna
AU - Garibaldi, Jonathan M.
AU - Aickelin, Uwe
AU - Soria, Daniele
AU - Gibson, Jack E.
AU - Hubbard, Richard B.
PY - 2012
Y1 - 2012
N2 - Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases. In this paper we investigate the robustness of four recently developed algorithms that mine longitudinal observational databases by applying them to The Health Improvement Network (THIN) for six drugs with well document known negative side effects. Our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior.
AB - Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases. In this paper we investigate the robustness of four recently developed algorithms that mine longitudinal observational databases by applying them to The Health Improvement Network (THIN) for six drugs with well document known negative side effects. Our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior.
UR - http://www.scopus.com/inward/record.url?scp=84870344929&partnerID=8YFLogxK
U2 - 10.1109/UKCI.2012.6335771
DO - 10.1109/UKCI.2012.6335771
M3 - Conference contribution
AN - SCOPUS:84870344929
SN - 9781467343923
T3 - 2012 12th UK Workshop on Computational Intelligence, UKCI 2012
BT - 2012 12th UK Workshop on Computational Intelligence, UKCI 2012
T2 - 2012 12th UK Workshop on Computational Intelligence, UKCI 2012
Y2 - 5 September 2012 through 7 September 2012
ER -