使用myCOPD应用程序中的真实世界数据进行恶化预测建模
Henry M G Glyde1, Alison M Blythin2, Tom M A Wilkinson3
1EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol, Bristol, UK.
Heliyon
|May 28, 2024
概括
机器学习模型可以使用患者输入的数据提前8天预测COPD (AECOPD) 的急性恶化. 该工具显示了早期干预的潜力,并改善了COPD管理的结果.
科学领域:
- 数字健康数字健康
- 机器学习在医学中的应用
- 呼吸系统疾病的预测.
背景情况:
- 慢性肺炎的急性恶化 (AECOPD) 是导致住院,疾病进展和死亡的关键事件.
- 早期发现和干预对于改善COPD患者的治疗结果至关重要.
- 数字工具为捕获现实世界患者数据以监测COPD提供了一个有希望的途径.
研究的目的:
- 开发和验证一个机器学习模型,以提前1-8天预测AECOPD.
- 利用 myCOPD 自主管理应用程序中的常规患者输入数据进行预测建模.
主要方法:
- 在机器学习中使用了AdaBoost算法的调整.
- 使用了506名患者和55,066名稳定的COPD记录的数据集,以及1263个AECOPD记录 (2017-2021).
- 培训数据包括COPD评估测试 (CAT) 评分,症状评分,吸烟史和之前恶化的频率.
主要成果:
- 该EasyEnsemble分类器实现了67.0%的灵敏度和65%的特异性 (PPV:5.0%,NPV:98.9%).
- 一个具有成本敏感决策树的AdaBoost模型产生了35.0%的灵敏度和89.0%的特异性 (PPV:7.08%,NPV:98.3%).
结论:
- 机器学习应用于现实世界的数字治疗数据显示了预测AECOPD的潜力.
- 这些模型可以在8天的窗口内有效排除COPD恶化的风险.
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