Linking Symptom Inventories using Semantic Textual Similarity

TL;DR

This paper introduces an AI-based semantic textual similarity method to link and compare diverse symptom inventories, improving reproducibility and clinical assessment across different studies and settings.

Contribution

The study demonstrates that pre-trained STS models can effectively link symptom descriptions across inventories, outperforming other models and aiding clinical decision-making.

Findings

Achieved 74.8% accuracy in linking symptom inventories

Outperformed other models in semantic similarity tasks

Enabled comparison of symptom scores across diverse datasets

Abstract

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.