Human-Centered Explainable Artificial Intelligence
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/145840 |
Resumo: | In automotive and industrial settings, occupational physicians are responsible for monitoring workers' health protection profiles. Workers' Functional Work Ability (FWA) status is used to create Occupational Health Protection Profiles (OHPP). This is a novel longitudinal study in comparison with previous research that has predominantly relied on the causality and explainability of human-understandable models for industrial technical teams like ergonomists. The application of artificial intelligence can support the decision-making to go from a worker's Functional Work Ability to explanations by integrating explainability into medical (restriction) and support in contexts of individual, work-related, and organizational risk conditions. A sample of 7857 for the prognosis part of OHPP based on Functional Work Ability in the Portuguese language in the automotive industry was taken from 2019 to 2021. The most suitable regression models to predict the next medical appointment for the workers' body parts protection were the models based on CatBoost regression, with an RMSLE of 0.84 and 1.23 weeks (mean error), respectively. CatBoost algorithm is also used to predict the next body part severity of OHPP. This information can help our understanding of potential risk factors for OHPP and identify warning signs of the early stages of musculoskeletal symptoms and work-related absenteeism. |
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Human-Centered Explainable Artificial IntelligenceAutomotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptomsexplainable AI (XAI)functional work abilitymusculoskeletal symptomsnatural language processingoccupational health protection profilesPollutionPublic Health, Environmental and Occupational HealthHealth, Toxicology and MutagenesisSDG 3 - Good Health and Well-beingIn automotive and industrial settings, occupational physicians are responsible for monitoring workers' health protection profiles. Workers' Functional Work Ability (FWA) status is used to create Occupational Health Protection Profiles (OHPP). This is a novel longitudinal study in comparison with previous research that has predominantly relied on the causality and explainability of human-understandable models for industrial technical teams like ergonomists. The application of artificial intelligence can support the decision-making to go from a worker's Functional Work Ability to explanations by integrating explainability into medical (restriction) and support in contexts of individual, work-related, and organizational risk conditions. A sample of 7857 for the prognosis part of OHPP based on Functional Work Ability in the Portuguese language in the automotive industry was taken from 2019 to 2021. The most suitable regression models to predict the next medical appointment for the workers' body parts protection were the models based on CatBoost regression, with an RMSLE of 0.84 and 1.23 weeks (mean error), respectively. CatBoost algorithm is also used to predict the next body part severity of OHPP. This information can help our understanding of potential risk factors for OHPP and identify warning signs of the early stages of musculoskeletal symptoms and work-related absenteeism.DF – Departamento de FísicaLIBPhys-UNLRUNMollaei, NafisehFujão, CarlosSilva, LuísRodrigues, JoãoCepeda, CátiaGamboa, Hugo2022-11-28T22:13:16Z2022-08-032022-08-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article27application/pdfhttp://hdl.handle.net/10362/145840eng1660-4601PURE: 46308262https://doi.org/10.3390/ijerph19159552info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:26:29Zoai:run.unl.pt:10362/145840Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:17.290297Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Human-Centered Explainable Artificial Intelligence Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms |
title |
Human-Centered Explainable Artificial Intelligence |
spellingShingle |
Human-Centered Explainable Artificial Intelligence Mollaei, Nafiseh explainable AI (XAI) functional work ability musculoskeletal symptoms natural language processing occupational health protection profiles Pollution Public Health, Environmental and Occupational Health Health, Toxicology and Mutagenesis SDG 3 - Good Health and Well-being |
title_short |
Human-Centered Explainable Artificial Intelligence |
title_full |
Human-Centered Explainable Artificial Intelligence |
title_fullStr |
Human-Centered Explainable Artificial Intelligence |
title_full_unstemmed |
Human-Centered Explainable Artificial Intelligence |
title_sort |
Human-Centered Explainable Artificial Intelligence |
author |
Mollaei, Nafiseh |
author_facet |
Mollaei, Nafiseh Fujão, Carlos Silva, Luís Rodrigues, João Cepeda, Cátia Gamboa, Hugo |
author_role |
author |
author2 |
Fujão, Carlos Silva, Luís Rodrigues, João Cepeda, Cátia Gamboa, Hugo |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
DF – Departamento de Física LIBPhys-UNL RUN |
dc.contributor.author.fl_str_mv |
Mollaei, Nafiseh Fujão, Carlos Silva, Luís Rodrigues, João Cepeda, Cátia Gamboa, Hugo |
dc.subject.por.fl_str_mv |
explainable AI (XAI) functional work ability musculoskeletal symptoms natural language processing occupational health protection profiles Pollution Public Health, Environmental and Occupational Health Health, Toxicology and Mutagenesis SDG 3 - Good Health and Well-being |
topic |
explainable AI (XAI) functional work ability musculoskeletal symptoms natural language processing occupational health protection profiles Pollution Public Health, Environmental and Occupational Health Health, Toxicology and Mutagenesis SDG 3 - Good Health and Well-being |
description |
In automotive and industrial settings, occupational physicians are responsible for monitoring workers' health protection profiles. Workers' Functional Work Ability (FWA) status is used to create Occupational Health Protection Profiles (OHPP). This is a novel longitudinal study in comparison with previous research that has predominantly relied on the causality and explainability of human-understandable models for industrial technical teams like ergonomists. The application of artificial intelligence can support the decision-making to go from a worker's Functional Work Ability to explanations by integrating explainability into medical (restriction) and support in contexts of individual, work-related, and organizational risk conditions. A sample of 7857 for the prognosis part of OHPP based on Functional Work Ability in the Portuguese language in the automotive industry was taken from 2019 to 2021. The most suitable regression models to predict the next medical appointment for the workers' body parts protection were the models based on CatBoost regression, with an RMSLE of 0.84 and 1.23 weeks (mean error), respectively. CatBoost algorithm is also used to predict the next body part severity of OHPP. This information can help our understanding of potential risk factors for OHPP and identify warning signs of the early stages of musculoskeletal symptoms and work-related absenteeism. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-11-28T22:13:16Z 2022-08-03 2022-08-03T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/145840 |
url |
http://hdl.handle.net/10362/145840 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1660-4601 PURE: 46308262 https://doi.org/10.3390/ijerph19159552 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
27 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799138114433712128 |