Linking Symptom Inventories Using Semantic Textual Similarity

Authors

• Eamonn Kennedy, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Shashank Vadlamani, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Hannah M. Lindsey, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Kelly S. Peterson, Division of Epidemiology, University of Utah, Salt Lake City, Utah, USA.
• Kristen Dams O'Connor, Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
• Ronak Agarwal, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Houshang H. Amiri, Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran.
• Raeda K. Andersen, Crawford Research Institute, Shepherd Center, Atlanta, Georgia, USA.
• Talin Babikian, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, USA.
• David A. Baron, Department of Psychiatry, Center for Behavioral Health and Sport, Western University of Health Sciences, Lebanon, Pomona, California, USA.
• Erin D. Bigler, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Karen Caeyenberghs, Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
• Lisa Delano-Wood, VA San Diego Healthcare System; Center of Stress and Mental Health, and Department of Psychiatry, UC San Diego School of Medicine, San Diego, California, USA.
• Seth G. Disner, Minneapolis VA Health Care System, Minneapolis, Minnesota, USA.
• Ekaterina Dobryakova, Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, New Jersey, USA.
• Blessen C. Eapen, VA Greater Los Angeles Health Care System, Los Angeles, California, USA.
• Rachel M. Edelstein, Department of Psychology, University of Virginia, Charlottesville, Virginia, USA.
• Carrie Esopenko, Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
• Helen M. Genova, Center for Autism Research, Kessler Foundation, East Hanover, New Jersey, USA.
• Elbert Geuze, Ministry of Defence, Brain Research and Innovation Centre, Utrecht, The Netherlands.
• Naomi J. Goodrich-Hunsaker, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Jordan Grafman, Shirley Ryan Ability Lab, Chicago, Illinois, USA.
• Asta K. Håberg, Faculty of Medicine and Health Sciences, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
• Cooper B. Hodges, Department of Psychology, Brigham Young University, Provo, Utah, USA.
• Kristen R. Hoskinson, Center for Biobehavioral Health, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA.
• Elizabeth S. Hovenden, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Andrei Irimia, Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA.
• Neda Jahanshad, Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA.
• Ruchira M. Jha, Departments of Neurology, Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, USA.
• Finian Keleher, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
• Kimbra Kenney, Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.

Document Type

Article

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.

Medical Subject Headings

Humans; Brain Injuries, Traumatic (diagnosis); Semantics; Deep Learning; Male; Female; Adult

Publication Date

6-1-2025

Publication Title

Journal of neurotrauma

E-ISSN

1557-9042

Volume

42

Issue

2025-11-12

First Page

1008

Last Page

1020

PubMed ID

40200899

Digital Object Identifier (DOI)

10.1089/neu.2024.0301

Recommended Citation

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; and Kenney, Kimbra, "Linking Symptom Inventories Using Semantic Textual Similarity" (2025). Neurology. 2032.
https://scholar.barrowneuro.org/neurology/2032