Linking Symptom Inventories using Semantic Textual Similarity
Eamonn Kennedy Shashank Vadlamani Hannah M Lindsey Kelly S Peterson Kristen Dams OConnor Kenton Murray Ronak Agarwal Houshang H Amiri Raeda K Andersen Talin Babikian David A Baron Erin D Bigler Karen Caeyenberghs Lisa Delano-Wood Seth G Disner Ekaterina Dobryakova Blessen C Eapen Rachel M Edelstein Carrie Esopenko Helen M Genova Elbert Geuze Naomi J Goodrich-Hunsaker Jordan Grafman Asta K Haberg Cooper B Hodges Kristen R Hoskinson Elizabeth S Hovenden Andrei Irimia Neda Jahanshad Ruchira M Jha Finian Keleher Kimbra Kenney Inga K Koerte Spencer W Liebel Abigail Livny Marianne Lovstad Sarah L Martindale Jeffrey E Max Andrew R Mayer Timothy B Meier Deleene S Menefee Abdalla Z Mohamed Stefania Mondello Martin M Monti Rajendra A Morey Virginia Newcombe Mary R Newsome Alexander Olsen Nicholas J Pastorek Mary Jo Pugh Adeel Razi Jacob E Resch Jared A Rowland Kelly Russell Nicholas P Ryan Randall S Scheibel Adam T Schmidt Gershon Spitz Jaclyn A Stephens Assaf Tal Leah D Talbert Maria Carmela Tartaglia Brian A Taylor Sophia I Thomopoulos Maya Troyanskaya Eve M Valera Harm Jan van der Horn John D Van Horn Ragini Verma Benjamin SC Wade Willian SC Walker Ashley L Ware J Kent Werner Jr Keith Owen Yeates Ross D Zafonte Michael M Zeineh Brandon Zielinski Paul M Thompson Frank G Hillary David F Tate Elisabeth A Wilde Emily L Dennis
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.