TY - GEN
T1 - Word Embedding Neural Networks to Advance Knee Osteoarthritis Research
AU - Amirian, Soheyla
AU - Ghazaleh, Husam
AU - Assefi, Mehdi
AU - Kremers, Hilal Maradit
AU - Arabnia, Hamid R.
AU - Plate, Johannes F.
AU - Tafti, Ahmad P.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.
AB - Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.
KW - Artificial Intelligence
KW - Knee Osteoarthritis
KW - Word Embedding
KW - Word2vec
UR - http://www.scopus.com/inward/record.url?scp=85171983214&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171983214&partnerID=8YFLogxK
U2 - 10.1109/CSCI58124.2022.00055
DO - 10.1109/CSCI58124.2022.00055
M3 - Conference contribution
AN - SCOPUS:85171983214
T3 - Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
SP - 289
EP - 292
BT - Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
Y2 - 14 December 2022 through 16 December 2022
ER -