Occupational health knowledge discovery based on association rules applied to workers’ body parts protection: a case study in the automotive industry
Rattachement africain : pt. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Occupational Health Protection (OHP) is mandatory by law and can be accomplished by considering the participation of others besides occupational physicians. The data shared can originate knowledge that might influence other processes related to occupational risk prevention. In this study, we used Artificial Intelligence (AI) methods to extract patterns among records shared under these circumstances over two years in the automotive industry. Records featuring OHP data against physical working conditions were selected, and a database of 383 profiles was designed. As Occupational Health Protection profiles under study are associated with work functional ability reduction, the body part(s) (n = 14) where it occurred were identified. Association Rules (ARs) coupled with Natural Language Processing techniques were applied to find meaningful hidden relationships and to identify the occurrence of protection profiles being assigned to at least two body parts simultaneously. After filtering ARs using three metrics (support, confidence, and lift), 54 ARs were found. The distribution of simultaneous body parts is presented as being higher in Special projects (n = 5). The results can use in: (i) design a multi-site body parts functional work ability (loss) model; (ii) model the capacity of organizations to retain workers in their working settings and (iii) prevent work-related musculoskeletal symptoms.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Occupational health knowledge discovery based on association rules applied to workers’ body parts protection: a case study in the automotive industry
- Date Crossref
- 08/12/2022
- Éditeur
- Informa UK Limited
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Lisbon pays non établi dans la noticeUniversité ou école supérieure
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Laboratório de Instrumentação Engenharia Biomédica e Física da Radiação pays non établi dans la noticeUniversité ou école supérieure
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Universidade Nova de Lisboa pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Sciences and Technology Physics Department pays non établi dans la noticeUniversité ou école supérieure
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Volkswagen Autoeuropa pays non établi dans la noticeInstitution
University of Lisbon, Laboratório de Instrumentação Engenharia Biomédica e Física da Radiação et Universidade Nova de Lisboa, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.