PURPOSE: This study analyzed the trends and characteristics of shoulder rehabilitation research through keyword analysis, and their relationships were modeled using text mining techniques.
METHODS: Abstract data of 10,121 articles in which abstracts were registered on the MEDLINE of PubMed with 'shoulder' and 'rehabilitation' as keywords were collected using python. By analyzing the frequency of words, 10 keywords were selected in the order of the highest frequency.
Word-embedding was performed using the word2vec technique to analyze the similarity of words. In addition, the groups were classified and analyzed based on the distance (cosine similarity) through the t-SNE technique.
RESULTS: The number of studies related to shoulder rehabilitation is increasing year after year, keywords most frequently used in relation to shoulder rehabilitation studies are ‘patient’, ‘pain’, and ‘treatment’. The word2vec results showed that the words were highly correlated with 12 keywords from studies related to shoulder rehabilitation.
Furthermore, through t-SNE, the keywords of the studies were divided into 5 groups.
CONCLUSION: This study was the first study to model the keywords and their relationships that make up the abstracts of research in the MEDLINE of Pub Med related to ‘shoulder’ and ‘rehabilitation’ using text-mining techniques.
The results of this study will help increase the diversifying research topics of shoulder rehabilitation studies to be conducted in the future.