Key result
A quantitative text-mining approach of PubMed literature identified targeted and novel areas of investigation for renal disease in cats and dogs by comparing trends with human CKD publications.
Why the study?
Chronic kidney disease is a leading cause of mortality and morbidity in cats and dogs, but an enhanced understanding of the knowledge base regarding its pathophysiology was needed.
Automated text-mining of biomedical literature can identify novel areas of investigation for renal disease in companion animals by leveraging human data.
Chronic kidney disease (CKD) is characterised by progressive kidney damage and encompasses a broad range of renal pathologies and aetiologies. In humans, CKD is an increasing global health problem, in particular in the western world, while in cats and dogs, CKD is one of the leading causes of mortality and morbidity. Here, we aimed to develop an enhanced understanding of the knowledge base related to the pathophysiology of renal disease and CKD in cats and dogs. To achieve this, we leveraged a text-mining approach for reviewing trends in the literature and compared the findings to evidence collected from publications related to CKD in humans. Applying a quantitative text-mining technique, we examined data on clinical signs, diseases, clinical and lab methods, cell types, cytokine, and tissue associations (co-occurrences) captured in PubMed biomedical literature. Further, we examined different types of pain within human CKD-related publications, as publications on this topic are sparser in companion animals, but with the growing importance of animal welfare and quality of life, it is an area of interest. Our findings could serve as substance for future research studies. The systematic automated review of relevant literature, along with comparative analysis, has the potential to summarise scientific evidence and trends in a quick, easy, and cost-effective way. Using this approach, we identified targeted and novel areas of investigation for renal disease in cats and dogs.
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Dadousis et al. (2024) conducted a review in Chronic kidney disease (CKD). Text-mining approach was evaluated on Trends in the literature related to pathophysiology of renal disease and CKD. A quantitative text-mining approach of PubMed literature identified targeted and novel areas of investigation for renal disease in cats and dogs by comparing trends with human CKD publications.
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