  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.
