Journal of neurotrauma · 2025

Linking Symptom Inventories Using Semantic Textual Similarity.

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Kennedy Eamonn, Vadlamani Shashank, Lindsey Hannah M, Peterson Kelly S, Dams O'Connor Kristen, Agarwal Ronak, Amiri Houshang H, Andersen Raeda K, Babikian Talin, Baron David A, Bigler Erin D, Caeyenberghs Karen, Delano-Wood Lisa, Disner Seth G, Dobryakova Ekaterina, Eapen Blessen C, Edelstein Rachel M, Esopenko Carrie, Genova Helen M, Geuze Elbert, Goodrich-Hunsaker Naomi J, Grafman Jordan, Håberg Asta K, Hodges Cooper B, Hoskinson Kristen R, Hovenden Elizabeth S, Irimia Andrei, Jahanshad Neda, Jha Ruchira M, Keleher Finian, Kenney Kimbra, Koerte Inga K, Liebel Spencer W, Livny Abigail, Løvstad Marianne, Martindale Sarah L, Max Jeffrey E, Mayer Andrew R, Meier Timothy B, Menefee Deleene S, Mohamed Abdalla Z, Mondello Stefania, Monti Martin M, Morey Rajendra A, Newcombe Virginia, Newsome Mary R, Olsen Alexander, Pastorek Nicholas J, Pugh Mary Jo, Razi Adeel, Resch Jacob E, Rowland Jared A, Russell Kelly, Ryan Nicholas P, Scheibel Randall S, Schmidt Adam T, Spitz Gershon, Stephens Jaclyn A, Tal Assaf, Talbert Leah D, Tartaglia Maria Carmela, Taylor Brian A, Thomopoulos Sophia I, Troyanskaya Maya, Valera Eve M, van der Horn Harm Jan, Van Horn John D, Verma Ragini, Wade Benjamin S C, Walker Willian C, Ware Ashley L, Werner J Kent, Yeates Keith Owen, Zafonte Ross D, Zeineh Michael M, Zielinski Brandon, Thompson Paul M, Hillary Frank G, Tate David F, Wilde Elisabeth A, Dennis Emily L

Аннотация

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.

Предметные рубрики MeSH

Humans Brain Injuries, Traumatic Semantics Deep Learning Male Female Adult

Linking Symptom Inventories Using Semantic Textual Similarity.

Ошибки и ограничения

Аннотация

Предметные рубрики MeSH