Artículo

Decision Support Techniques for determination of the causal relationship between health problems of workers and their work activities

Resumen

Reflecting on the complexity and impacts of determination of the causal relationship between health problems of workers and the exercise of their work activities, there is a need to learn about scientific articles that expose techniques to determine this type of causal relationship. There is also a need to reveal whether any article exposes multicriteria decision analysis technique. The aim is to quantify the techniques used to determine the causal relationship between health problems of workers and the exercise of their work activities. Bibliometric analysis was performed, searching for articles in Portuguese, Spanish and English. An advanced search was performed on the website of the ministerial journals portal and then on the Gale Academic OneFile, SciVerse Scopus, Scientific Electronic Library Online (SciELO) and PubMed Central collections. In summary, 38 articles were selected from portal, 50 from Gale Academic OneFile, 20 from SciVerse Scopus, 37 from SciELO and 5 from PubMed Central, totaling 150 articles of interest for analysis of their contents. Among these 150 articles, 33.33% addressed the causal relationship between illness and work, 3.33% described some process related to occupational diagnostic investigation and 0.66%, which represents only one article, exhibited a technique to determine this type of causal relationship: the probability of causality in neoplastic diseases. No article described multicriteria decision analysis method as a technique for determine this type of causal relationship. Therefore, there is a need to carry out and disseminate scientific research on methods to help determine a causal relationship between illness and work.
Hidayat, Erwin Yudi (57205439013); Hastuti, Khafiizh (56485990500); Muda, Azah Kamilah (23390362900)
Artificial intelligence in digital image processing: A bibliometric analysis
2025
10.1016/j.iswa.2024.200466
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212984349&doi=10.1016%2fj.iswa.2024.200466&partnerID=40&md5=14c2a82f6cb46e9efa80c050fc79c24f
Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Dinus Research Group for AI in Medical Science (DREAMS), Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
All Open Access; Gold Open Access
Scopus
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