Linking Symptom Inventories using Semantic Textual Similarity
This work addresses reproducibility issues in clinical symptom assessment by enabling comparisons across different inventories, though it is incremental as it applies existing STS methods to a new domain.
The paper tackled the problem of incomparability across diverse symptom inventories by using semantic textual similarity (STS) models to link symptoms and scores, achieving 74.8% accuracy in predicting symptom severity across multiple tasks.
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.