Symptomatology of hypoglycemia in diabetes: a bibliometric analysis (2000-2022) of Bayesian approaches - Sharmin, Afsana Al; Zulkafli, Hani Syahida; Mohamed Ali, Nazihah; Al Mamun, Md. Abdullah; Shafrin, Rubaiya (Malque Publishing, 2025)
Hypoglycemia poses a critical challenge in managing diabetes. Existing literature, while extensive, lacks a holistic perspective. This study aims to bridge this gap by combining bibliometric analysis and a comprehensive review of Bayesian analysis-related hypoglycemic issues. This study employed data from the SCI-EXPANDED database for bibliometric analysis. The keywords "symptom" or "symptoms," "hypoglycemic" or "hypoglycemia," or "hypoglycaemia" or "hypoglycaemic," and "Diabetes" or "Diabetic" or "Diabetics" were used to locate 1,596 documents from 2000 to 2022. Document types, authorship patterns, and citation metrics were examined. Bayesian methodologies were systematically reviewed across various diabetes types and evaluated using specific assessment tools. Most of the articles published in "Endocrinology & Metabolism" contributed 37.2% of total articles, with a notable CPP2022 (Citations Per Publication (CPP)) of 35, and the main publication type were articles with an average of about six authors and over 32,000 citations in 2022. The United States (US) consistently leads in the number of published articles, followed by China, Japan, and India. Novo Nordisk led institutions with 36 publications and a substantial CPP2022 of 60.9. The comprehensive review emphasized that Bayesian statistical modeling is widely used for adult Type 1 and Type 2 diabetes but is limited in child Type 1 and absent in Gestational Diabetes (GAD) research. In contrast, Bayesian Networks (BNs) are mainly applied to adult Type 2 diabetes, with gaps in other types. Furthermore, Bayesian Neural Networks (BNNs) are prevalent in adult and child Type 1 studies but not applied to Type 2 or GAD. Since 2010, Total Publications (TP) have increased rapidly, indicating increased interest in researching hypoglycemia. Outlining potential research directions and emphasizing the transformative impact of Bayesian methodologies provides valuable insights for clinicians, researchers, and healthcare stakeholders.