From Web to RheumaLpack: Creating a Linguistic Corpus for Exploitation and Knowledge Discovery in Rheumatology

Abstract

This study introduces RheumaLinguisticpack (RheumaLpack), the first specialised linguistic web corpus designed for the field of musculoskeletal disorders. By combining web mining (i.e., web scraping) and natural language processing (NLP) techniques, as well as clinical expertise, RheumaLpack systematically captures and curates structured and unstructured data across a spectrum of web sources including clinical trials registers (i.e., ClinicalTrials.gov), bibliographic databases (i.e., PubMed), medical agencies (i.e. European Medicines Agency), social media (i.e., Reddit), and accredited health websites (i.e., MedlinePlus, Harvard Health Publishing, and Cleveland Clinic). Given the complexity of rheumatic and musculoskeletal diseases (RMDs) and their significant impact on quality of life, this resource can be proposed as a useful tool to train algorithms that could mitigate the diseases' effects. Therefore, the corpus aims to improve the training of artificial intelligence (AI) algorithms and facilitate knowledge discovery in RMDs. The development of RheumaLpack involved a systematic six-step methodology covering data identification, characterisation, selection, collection, processing, and corpus description. The result is a non-annotated, monolingual, and dynamic corpus, featuring almost 3 million records spanning from 2000 to 2023. RheumaLpack represents a pioneering contribution to rheumatology research, providing a useful resource for the development of advanced AI and NLP applications. This corpus highlights the value of web data to address the challenges posed by musculoskeletal diseases, illustrating the corpus's potential to improve research and treatment paradigms in rheumatology. Finally, the methodology shown can be replicated to obtain data from other medical specialities. The code and details on how to build RheumaLpack are also provided to facilitate the dissemination of such resource.

Keywords: Artificial intelligence; Natural language processing; Rheumatology; Web corpus.