  The electronic healthcare databases are starting to become more readily
available and are thought to have excellent potential for generating adverse
drug reaction signals. The Health Improvement Network (THIN) database is an
electronic healthcare database containing medical information on over 11
million patients that has excellent potential for detecting ADRs. In this paper
we apply four existing electronic healthcare database signal detecting
algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the
THIN database for a selection of drugs from six chosen drug families. This is
the first comparison of ADR signalling algorithms that includes MUTARA and HUNT
and enabled us to set a benchmark for the adverse drug reaction signalling
ability of the THIN database. The drugs were selectively chosen to enable a
comparison with previous work and for variety. It was found that no algorithm
was generally superior and the algorithms' natural thresholds act at variable
stringencies. Furthermore, none of the algorithms perform well at detecting
rare ADRs.
