  An extensive library of symptom inventories has been developed over time to
measure clinical symptoms, but this variety has led to several long standing
issues. Most notably, results drawn from different settings and studies are not
comparable, which limits reproducibility. Here, we present an artificial
intelligence (AI) approach using semantic textual similarity (STS) to link
symptoms and scores across previously incongruous symptom inventories. We
tested the ability of four pre-trained STS models to screen thousands of
symptom description pairs for related content - a challenging task typically
requiring expert panels. Models were tasked to predict symptom severity across
four different inventories for 6,607 participants drawn from 16 international
data sources. The STS approach achieved 74.8% accuracy across five tasks,
outperforming other models tested. This work suggests that incorporating
contextual, semantic information can assist expert decision-making processes,
yielding gains for both general and disease-specific clinical assessment.
