Knowledge Discovery from Thyroid Disease Research Using Transformer-Based Natural Language Processing

Thyroid disorders are a diverse group of
endocrine diseases that continue to represent important
clinical and public health issues globally. The constantly
growing volume of scientific literature makes it
increasingly difficult to detect emergent research issues
and knowledge gaps through traditional review
approaches. The purpose of this work was to identify and
synthesize important research themes in thyroid illness
literature using a transformer-based natural language
processing (NLP) approach. 3,000 scientific publications
were extracted from the PubMed database using
Biopython. Hierarchical clustering revealed obvious
semantic links between the themes, demonstrating that
the transformer-based approach accurately reflected the
underlying structure of thyroid research. The analysis
identified 49 distinct research topics that were divided
into five major thematic domains: (i) molecular biology,
thyroid cancer, and immune mechanisms; (ii) clinical
management, treatment, and public health; (iii)
autoimmune diseases, metabolism, and environmental
influences; (iv) rare endocrine disorders and genetic
mutations; and (v) diagnosis, prediction models, and
surgical outcomes. The identified topics ranged from
thyroid nodules to thyroid cancer, autoimmune thyroid
diseases, thyroid eye disease, pregnancy-related thyroid
disorders, environmental risk factors, molecular
biomarkers, genetic mutations, radioactive iodine
therapy, minimally invasive surgery, pediatric thyroid
diseases, cardiovascular complications, reproductive
health, gut microbiota, COVID-19-related thyroid
dysfunction, and emerging targeted therapies. This study
provides a comprehensive synthesis of current thyroid
disease research and demonstrates that transformer-
based NLP provides an efficient framework for
organizing large amounts of biomedical literature,
identifying research trends, and guiding future clinical
and scientific investigations.