Linking Symptom Inventories Using Semantic Textual Similarity

By sjmartinez • April 10, 2025

Kennedy E, Vadlamani S, Lindsey HM, Peterson KS, Dams O'Connor K, Agarwal R, Amiri HH, Andersen RK, Babikian T, Baron DA, Bigler ED, Caeyenberghs K, Delano-Wood L, Disner SG, Dobryakova E, Eapen BC, Edelstein RM, Esopenko C, Genova HM, Geuze E, Goodrich-Hunsaker NJ, Grafman J, Håberg AK, Hodges CB, Hoskinson KR, Hovenden ES, Irimia A, Jahanshad N, Jha RM, Keleher F, Kenney K, Koerte IK, Liebel SW, Livny A, Løvstad M, Martindale SL, Max JE, Mayer AR, Meier TB, Menefee DS, Mohamed AZ, Mondello S, Monti MM, Morey RA, Newcombe V, Newsome MR, Olsen A, Pastorek NJ, Pugh MJ, Razi A, Resch JE, Rowland JA, Russell K, Ryan NP, Scheibel RS, Schmidt AT, Spitz G, Stephens JA, Tal A, Talbert LD, Tartaglia MC, Taylor BA, Thomopoulos SI, Troyanskaya M, Valera EM, van der Horn HJ, Van Horn JD, Verma R, Wade BSC, Walker WC, Ware AL, Werner JK Jr, Yeates KO, Zafonte RD, Zeineh MM, Zielinski B, Thompson PM, Hillary FG, Tate DF, Wilde EA, Dennis EL. Linking Symptom Inventories Using Semantic Textual Similarity. J Neurotrauma. 2025 Apr 9. doi: 10.1089/neu.2024.0301. Epub ahead of print. PMID: 40200899.

Tags: Artificial intelligence, General Internal Medicine, Harmonization, Physical Medicine & Rehabilitation, PM&R, Publications, semantic textual similarity, symptom inventories, Traumatic brain injury, Veterans Administration

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