Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations.

Background: Outcomes are variables monitored during a clinical trial to assess the impact of an intervention on humans' health.Automatic assessment of semantic similarity of trial outcomes is required for a number of tasks, such as detection of outcome switching (unjustified changes of pre-defined outcomes of a trial) and implementation of Core Outcome Sets (minimal sets of outcomes that should be reported in a particular medical domain).

Objective: We aimed at building an algorithm for assessing semantic similarity of pairs of primary and reported outcomes.