USA – FDA Seeks Help Using Algorithms to Detect Adverse Event Anomalies

As it becomes more difficult for the US Food and Drug Administration (FDA) to decipher when a series of adverse events could actually be a sign of a more significant problem, the agency is calling on the public to develop computational algorithms for the automatic detection of adverse event anomalies using publicly available data.

The current system of tracking adverse events is passive, in that patients, patient guardians, health care providers and manufacturers submit voluntary reports of adverse events associated with products, which FDA then analyzes.

“FDA regulators use a variety of data mining methods and tools to analyze the volumes of adverse event reports and identify possible safety signals. Disproportionality methods, which identify unexpectedly high statistical associations between products and adverse events, serve as a primary method for identifying safety signals. Change-point analysis identifies changes in longitudinal adverse event patterns for products,” the agency explains…