挖掘风险:在工人中早期发现心血管疾病
Ricardo Jorquera1, Guillermo Droppelmann2, Max Dollmann1
1Workmed, Santiago, Chile.
Frontiers in medicine
|December 15, 2025
概括
机器学习模型使用矿工的体重指数和血糖准确预测心血管风险进展. 这种方法提高了高风险人群的职业健康评估.
科学领域:
- 职业健康 职业健康 职业健康 职业健康
- 机器学习是机器学习.
- 评估心血管疾病风险评估
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 现有的心血管风险 (CVR) 工具仅限于像矿工这样的独特人群.
- 这项研究利用机器学习 (ML) 和纵向数据来预测职业环境中的CVR进展.
研究的目的:
- 开发ML模型,使用可访问的临床标志物预测心血管风险进展.
- 评估身体质量指数 (BMI) 和血糖 (BG) 作为矿工CVR代理的实用性.
- 解决当前在极端工作条件下CVR评估工具的局限性.
主要方法:
- 对89,045名智利矿工的健康数据进行了追溯纵向分析.
- 模拟定义的BMI和BG类别之间的过渡,使用连续的访问对.
- 应用ML技术 (XGB,RF) 与分层交叉验证和超参数调整.
- 使用AUC,精度,灵敏度和特异性评估模型性能.
主要成果:
- ML模型在预测BMI转变方面取得了很高的准确性,严重进展 (病态肥胖症) 的AUC高达0.95.
- 血糖 (BG) 过渡预测显示了可操作的结果,AUC高达0.83的进展到糖尿病.
- 模型表现出良好的概括性和一致性,几乎没有过度配合的证据.
结论:
- ML模型有效地从职业健康数据中预测临床相关的BMI和BG风险过渡.
- 纵向数据和基于场景的评估提高了CVR评估的ML模型性能.
- 这种方法为改善高风险劳动人口中CVR评估和预防性决策提供了潜力.
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