机器学习预测煤炭工人肺炎症的分类,基于少数的临床数据
Jiaqi Jia1, Jingying Huang2, Yuming Cui1
1School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, Jiangsu, People's Republic of China.
Digital health
|July 16, 2025
概括
机器学习使用临床数据准确预测煤炭工人肺炎病 (CWP) 阶段. 这种方法通过识别ALB,PLT和WBC等关键生物标志物来帮助早期诊断,改善患者的治疗结果.
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 职业健康 职业健康 职业健康
背景情况:
- 煤炭工人肺炎 (CWP) 在早期诊断和治疗方面存在挑战,原因是长时间的潜伏期.
- 精确的CWP分期对于有效的患者管理和干预至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以准确预测CWP分阶段.
- 利用有限的患者数据,确定可预测CWP的关键临床生物标志物.
- 加强CWP的早期查和诊断能力.
主要方法:
- 对202名健康个体和81名CWP患者的临床数据进行比较分析.
- 应用过量抽样技术来解决数据不平衡的问题.
- 使用多种ML方法进行监督学习,包括随机森林 (RF) 和Adaboost.
- 开发一种创新的特征选择方法,整合特征的重要性和独立性.
主要成果:
- ALB,PLT和WBC被确定为CWP的重要预测因素.
- 通过 7:3 或 6:4 (重要性:独立性) 的特征选择权重比实现了最佳的ML模型性能.
- 该RF-Adaboost模型显示了CWP分阶段的最高预测准确性,达到0.8757.7的F1得分.
结论:
- 将临床生物化学数据与ML模型,特别是RF-Adaboost集成,可以有效预测CWP分期.
- 拟议的综合特征选择方法显著改善了CWP预测的ML模型性能.
- 这种基于ML的方法为CWP的早期查和诊断提供了有价值的工具.
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