학술논문
Linking Symptom Inventories using Semantic Textual Similarity
Document Type
Working Paper
Author
Kennedy, Eamonn; Vadlamani, Shashank; Lindsey, Hannah M; Peterson, Kelly S; OConnor, Kristen Dams; Murray, Kenton; 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; Haberg, 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; Lovstad, 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 SC; Walker, Willian SC; Ware, Ashley L; Werner Jr, 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
Source
Subject
Language
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.