# Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm
This requires a structured approach involving waste collection, segregation, transportation, and the implementation of systematic disposal methods [21]. Reusable equipment, leadership deficiencies, and customized interventions are critical elements in mitigating medical waste in operating theaters across various global healthcare systems [22]. To transition toward a circular economy, various waste-to-energy technologies, including thermochemical, biochemical, and chemical conversion processes, have been methodically explored for MW [23]. Although this topic is critical for public health, environmental sustainability, and economic considerations, research into MW remains underdeveloped. Continuous investigation and publication in this domain are necessary to improve waste management practices. However, bibliometric studies and science mapping analyses in this area are sparse and target only small areas of this vast issue, highlighting the need for further research. The objective of this paper was to develop an optimized framework regarding the implications and publication trends in MW through a specific bibliometric analysis and science mapping research utilizing a custom Python-based thesaurus tool [24]. The deficiencies in the regulatory framework, persistent inefficiencies in MW management systems, and inadequate awareness and professional education have highlighted the necessity of optimizing, categorizing, and assessing publication trends in this complex domain. In recent years, the scientific landscape concerning MW has significantly expanded, especially following the COVID-19 pandemic. Among the key efforts to map the evolution of MW research are the studies conducted by [PERSON] and [PERSON] [25], [PERSON] et al. [6], and [PERSON] et al. [26]. Nevertheless, these analyses are constrained by narrower temporal coverage, limited datasets, or the absence of in-depth thematic trend evaluation. In contrast, our study extends over a nearly 50-year period (i.e., 1975-2024), analyzes one of the largest and most refined datasets to date in this field (i.e., 3025 papers), and introduces one of the most complex and targeted search algorithms, incorporating 41 specific terms. It also integrates a Python-enhanced thesaurus generator for keyword standardization and applies comprehensive visual and co-word analysis tools. This allows for a better understanding of the field's thematic and methodological shifts. Moreover, recent work on MW sustainability (e.g., [PERSON] et al., 2022 [27]) reinforces the need for advanced tools to assess and contextualize research directions, a gap our approach addresses. This bibliometric analysis aims to address the following questions: how has the literature in the field of medical waste management evolved during the period 1975-2024 in terms of publication volume and impact; which countries, institutions, and journals have been the most productive and influential contributors to research in medical waste management; what are the main collaboration patterns between researchers and countries in this field; how have research themes and topics in the studied field evolved over time, particularly in response to global health crises, such as the COVID-19 pandemic. Furthermore, it facilitates access to extensive literature, so improving the capacity to delineate research objectives and attain a comprehensive understanding of the scientific advancements in MW sustainable management. The present study is structured as follows: Section 1 is dedicated to introducing the subject under evaluation, while Section 2 details the materials and methods used, describing the data collection process from the Web of Science database, the development of the search algorithm, and the analytical tools employed, including a custom Python-based thesaurus generator. Section 3 presents the results in two main parts: first, an overview of the literature and scientific production metrics at country, institutional, and publication levels; second, a visualization of scientific networks and trends.