Machine Learning Applications in Nephrology: A Bibliometric Analysis Comparing Kidney Studies to Other Medicine SubspecialitiesPlain-Language Summary

• Main Authors: Ashish Verma, Vipul C. Chitalia, Sushrut S. Waikar, Vijaya B. Kolachalama
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: Kidney Medicine
• Subjects: Bibliometric analysis; kidney; machine learning; NIH funding; research methods
• Online Access: http://www.sciencedirect.com/science/article/pii/S2590059521001163

Rationale & Objectives: Artificial intelligence driven by machine learning algorithms is being increasingly employed for early detection, disease diagnosis, and clinical management. We explored the use of machine learning–driven advancements in kidney research compared with other organ-specific fields. Study Design: Cross-sectional bibliometric analysis. Setting & Participants: ISI Web of Science database was queried using specific Medical Subject Headings (MeSH) terms about the organ system, journal International Standard Serial Number, and research methodology. In parallel, we screened the National Institutes of Health (NIH) RePORTER website to explore funded grants that proposed the use of machine learning as a methodology. Predictors: Number of publications using machine learning as a research method. Outcome: Articles were characterized by research methodology among 5 organ systems (brain, heart, kidney, liver, and lung). Grants funded by NIH for machine learning were characterized by study sections. Analytical Approach: Percentages of articles using machine learning and other research methodologies were compared among 5 organ systems. Results: Machine learning-based articles that are focused on the kidney accounted for 3.2% of the total relevant articles from the 5 organ systems. Specifically, brain research published over 19-fold higher number of articles than kidney research. As compared with machine learning, conventional statistical approaches such as the Cox proportional hazard model were used 9-fold higher in articles related to kidney research. In general, a lower utilization of machine learning–based approaches was observed in organ-specific specialty journals than the broad interdisciplinary journals. The digestive disease, kidney, and urology study sections funded 122 applications proposing machine learning–based approaches compared to 265 applications from the neurology, neuropsychology, and neuropathology study sections. Limitations: Observational study. Conclusions: Our analysis suggests lowest use of machine learning as a research tool among kidney researchers compared with other organ-specific researchers, underscoring a need to better inform the kidney research community about this emerging data analytic tool.