Department of Natural Resources and Environmental Engineering, University of Vigo, Lagoas Marcosende, 36310, Vigo, Spain.
Department of Statistics and Operational Research, University of Vigo, Lagoas Marcosende, 36310, Vigo, Spain.
Environ Sci Pollut Res Int. 2019 Oct;26(29):29560-29569. doi: 10.1007/s11356-018-2962-6. Epub 2018 Aug 18.
Medical records generated during occupational health surveillance processes have large amounts of unexploited information that can help to reduce silica-related health risks and many occupational diseases. The methodology applied in this study consists in analyzing through machine learning techniques a database with 70,000 medical examinations from workers in the energy and construction industry in Spain. First, a general unsupervised Bayesian model is built and node force analysis is used to identify the factors with the greatest impact on the worker's health surveillance process. Second, a predictive Bayesian model is created and mutual information is employed to assess the more relevant factors affecting the medical capability of workers exposed to silica dust. The lung auscultation and the breathing exploration are the two factors that influence the most the medical capability of silica-exposed employees. Probabilistic inference shows a remarkable gender effect, where women present more resilience towards occupational diseases than men showing a higher proportion of normal results in certain key factors, such as body mass index (♀49.73%, ♂25.17%) or spirometry (♀53.73%, ♂48.91%). Finally, environmental conditions demonstrate to have a major influence on spatial variability of occupational diseases. The design of health prevention programs based on geographical variations can be crucial to the attainment of an ongoing and sustained healthier workforce with a reduction in the number of chronic workplace illnesses.
职业健康监测过程中产生的医疗记录包含大量未被充分利用的信息，可以帮助降低与二氧化硅相关的健康风险和许多职业病。本研究应用的方法包括通过机器学习技术分析来自西班牙能源和建筑行业的 70000 名工人的体检数据库。首先，建立一个通用的无监督贝叶斯模型，并使用节点力分析来确定对工人健康监测过程影响最大的因素。其次，创建一个预测贝叶斯模型，并利用互信息评估影响接触二氧化硅粉尘的工人医疗能力的更相关因素。肺部听诊和呼吸探索是影响暴露于二氧化硅的员工医疗能力的两个最重要的因素。概率推理显示出显著的性别效应，女性比男性对职业病更具抵抗力，在某些关键因素（如体重指数（♀49.73%，♂25.17%）或肺活量测定法（♀53.73%，♂48.91%）中，正常结果的比例更高。最后，环境条件表明对职业病的空间变异性有重大影响。基于地理变化设计的健康预防计划对于实现持续和可持续的更健康的劳动力队伍以及减少慢性职业疾病的数量至关重要。
