基于机器学习的口腔卫生保健工作者职业暴露风险的预测
Zhang Jian1,2, Zhu Jinting2,3, Lan Wang4
1Department of Oral Implantology, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, China.
口腔保健工作者面临职业暴露风险. 一个机器学习模型确定了诸如工作偏好和弹性等关键风险因素,使高风险个体能够进行早期查和干预.
科学领域:
- 职业健康 职业健康 职业健康
- 牙科公共卫生 牙科公共卫生
- 医疗信息学 医疗信息学
背景情况:
- 职业暴露对口腔保健工作者构成重大风险.
- 早期识别高风险个体对于实施预防措施至关重要.
研究的目的:
- 确定口腔保健工作者职业暴露的关键风险因素.
- 开发一个用于早期查和干预计划的预测模型.
主要方法:
- 一项涉及367名口腔卫生保健工作者在中国天津的多中心横截面研究.
- 通过在线调查问卷收集的关于人口统计,工作偏好库存,组织气候和弹性方面的数据.
- 使用后勤回归,随机森林,决策树和XGBoost算法构建的预测模型.
主要成果:
- 职业暴露的发生率在建模组为15.5%,在验证组为16.5%.
- 工作偏好库存,弹性,组织气候,职称,医院级别和性别被确定为独立的风险因素.
- 随机森林模型实现了最高的预测性能 (AUC: 0.755,准确率: 89.2%).
结论:
- 在口腔保健工作者中确定了职业暴露的关键风险因素.
- 一个基于森林的随机预测模型已成功开发用于风险评估.
- 结果支持有针对性的干预措施和未来使用各种数据集进行验证.
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