Language

English

Publication Date

6-1-2025

Journal

Journal of Neurotrauma

DOI

10.1089/neu.2024.0301

PMID

40200899

PMCID

PMC12169902

PubMedCentral® Posted Date

6-4-2025

PubMedCentral® Full Text Version

Post-print

Abstract

An extensive library of symptom inventories has been developed over time to measure clinical symptoms of traumatic brain injury (TBI), but this variety has led to several long-standing issues. Most notably, results drawn from different settings and studies are not comparable. This creates a fundamental problem in TBI diagnostics and outcome prediction, namely that it is not possible to equate results drawn from distinct tools and symptom inventories. Here, we present an approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories by ranking item text similarities according to their conceptual likeness. We tested the ability of four pretrained deep learning 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. Correlation and factor analysis found the properties of the scales were broadly preserved under conversion. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding broad gains for the harmonization of TBI assessment.

Keywords

Humans, Brain Injuries, Traumatic, Semantics, Deep Learning, Male, Female, Adult, artificial intelligence, harmonization, traumatic brain injury, semantic textual similarity, symptom inventories

Published Open-Access

yes

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