English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Comparison of algorithms that detect drug side effects using electronic healthcare databases