Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm
The management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with limited bibliometric analysis in this field. The ris...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/6/312 |
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| author | Ilie Cirstea Andrei-Flavius Radu Delia Mirela Tit Ada Radu Gabriela Bungau Paul Andrei Negru |
| author_facet | Ilie Cirstea Andrei-Flavius Radu Delia Mirela Tit Ada Radu Gabriela Bungau Paul Andrei Negru |
| author_sort | Ilie Cirstea |
| collection | DOAJ |
| description | The management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with limited bibliometric analysis in this field. The rising volume of MW, exacerbated by global health crises, strains existing systems. This study uses bibliometric analysis of 3025 publications from 1975 to 2024, employing Web of Science data with specific Boolean operators and keywords for efficient searching algorithms. Data visualization and analysis were carried out with software such as VOSviewer version 1.6.20 and Bibliometrix 5.0.0, along with custom Python 3.12.3 thesaurus files to standardize terminology. The results reveal a significant rise in publications post-2000, particularly during the COVID-19 pandemic, with China, India, and the US as major contributors. South Korea stands out for high citation rates. Network analysis identified collaboration patterns, while trend mapping highlighted a shift toward sustainable waste management practices. The evaluation insights revealed a clear transition from incineration-based methods toward sustainable and innovative solutions such as autoclaving, plasma pyrolysis, and advanced oxidation processes, driven by environmental concerns and regulatory frameworks. This study underscores the implications of MW and the importance of analyzing publication trends over time to understand the ongoing need for development, grounded in a legislative policy framework, which is essential for advancing sustainable practices in MW management. |
| format | Article |
| id | doaj-art-8d9d9505a71a478e88e37a395c3eb6bb |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-8d9d9505a71a478e88e37a395c3eb6bb2025-08-20T02:24:34ZengMDPI AGAlgorithms1999-48932025-05-0118631210.3390/a18060312Bibliometric Analysis of Medical Waste Research Using Python-Driven AlgorithmIlie Cirstea0Andrei-Flavius Radu1Delia Mirela Tit2Ada Radu3Gabriela Bungau4Paul Andrei Negru5Doctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, RomaniaDoctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, RomaniaDoctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, RomaniaDoctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, RomaniaDoctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, RomaniaDoctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, RomaniaThe management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with limited bibliometric analysis in this field. The rising volume of MW, exacerbated by global health crises, strains existing systems. This study uses bibliometric analysis of 3025 publications from 1975 to 2024, employing Web of Science data with specific Boolean operators and keywords for efficient searching algorithms. Data visualization and analysis were carried out with software such as VOSviewer version 1.6.20 and Bibliometrix 5.0.0, along with custom Python 3.12.3 thesaurus files to standardize terminology. The results reveal a significant rise in publications post-2000, particularly during the COVID-19 pandemic, with China, India, and the US as major contributors. South Korea stands out for high citation rates. Network analysis identified collaboration patterns, while trend mapping highlighted a shift toward sustainable waste management practices. The evaluation insights revealed a clear transition from incineration-based methods toward sustainable and innovative solutions such as autoclaving, plasma pyrolysis, and advanced oxidation processes, driven by environmental concerns and regulatory frameworks. This study underscores the implications of MW and the importance of analyzing publication trends over time to understand the ongoing need for development, grounded in a legislative policy framework, which is essential for advancing sustainable practices in MW management.https://www.mdpi.com/1999-4893/18/6/312medical wastesearch algorithmswaste managementsustainable developmentbibliometric analysisVOSviewer |
| spellingShingle | Ilie Cirstea Andrei-Flavius Radu Delia Mirela Tit Ada Radu Gabriela Bungau Paul Andrei Negru Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm Algorithms medical waste search algorithms waste management sustainable development bibliometric analysis VOSviewer |
| title | Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm |
| title_full | Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm |
| title_fullStr | Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm |
| title_full_unstemmed | Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm |
| title_short | Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm |
| title_sort | bibliometric analysis of medical waste research using python driven algorithm |
| topic | medical waste search algorithms waste management sustainable development bibliometric analysis VOSviewer |
| url | https://www.mdpi.com/1999-4893/18/6/312 |
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