Word Embedding Neural Networks to Advance Knee Osteoarthritis Research

TL;DR

This paper explores the use of neural network-based word embeddings to automatically extract relevant terminology from scientific literature on knee osteoarthritis, aiming to enhance understanding and diagnosis.

Contribution

It introduces a novel application of word embedding neural networks for autonomous keyword extraction in knee OA research literature.

Findings

Feasibility demonstrated for neural network-based keyword extraction.

Potential to improve terminology standardization in knee OA research.

Supports enhanced literature mining for better disease understanding.

Abstract

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.