TEDAR: Temporal dynamic signal detection of adverse reactions

Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.

Automated summary

Computational approaches for detecting signals of adverse drug reactions are powerful tools in monitoring unattended effects reported by users, aiming to prevent serious injuries and deaths by early detection of adverse reactions. The methodology allows for a greater number of true signals to be detected without significantly increasing false positives.