Human-Centered Explainable Artificial Intelligence
Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms
Datos Bibliográficos
| ID | 15469710 |
|---|---|
| Autores | Nafiseh Mollaei (0000-0002-8332-489X, University of Lisbon, autor de correspondencia), Carlos Fujão (0000-0002-1433-5712, Volkswagen Autoeuropa, Industrial Engineering and Lean Management, Quinta da Marquesa, 2954-024 Quinta do Anjo, Portugal), Luís Silva (0000-0002-2517-7932, University of Lisbon), João Rodrigues (0000-0003-3711-7598, University of Lisbon), Cátia Cepeda (0000-0002-2998-976X, University of Lisbon), Hugo Gambôa (0000-0002-4022-7424, University of Lisbon) |
| Año | 2022 |
| Volumen | 19 |
| Número | 15 |
| Páginas | 9552-9552 |
| Fecha de publicación | 2022-08-03 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | International Journal of Environmental Research and Public Health (JOURNAL) |
| Identificadores de la revista | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Editorial | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph19159552 |
| PMID | 35954919 |
| OpenAlex | W4289711073 |
| Idioma | EN |
| Citas recibidas | 1 |
| Referencias citadas | 46 |
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
Absenteeism · Automotive industry · Environmental health · Occupational medicine · Occupational safety and health · Poison control · Risk analysis (engineering · Work (physics · Applied Psychology · Computer Science · Engineering · Healthcare Systems and Public Health · Human Factors and Ergonomics · Medicine · Occupational Health and Safety Research · Psychology · Quality and Safety in Healthcare · Social Psychology
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| Obras citantes distintas | 1 |
|---|---|
| Citas por año | 1 |
| Intervalo de citas | 2026 - 2026 (1) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 1 |