使用时空卷积神经网络预测喘患者使用短效β-激动剂的预测
Nicholas Hirons1, Angier Allen2, Noah Matsuyoshi1
1Propeller Health, San Francisco, CA, United States.
JAMIA open
|October 30, 2023
概括
机器学习模型可以预测喘患者每天使用短效β-激动剂 (SABA). 时间卷积网络 (TCN) 显示出最佳性能,使用过去的SABA使用和环境数据来预测需求并告知干预措施.
科学领域:
- 数字健康数字健康
- 机器学习在呼吸道医学中的应用
- 喘管理的管理
背景情况:
- 短效β激动剂 (SABA) 的使用是控制喘和恶化风险的关键指标.
- 吸入器传感器提供了一种被动方法来收集纵向的SABA使用数据.
- 早期识别有风险的患者可以改善喘管理和治疗结果.
研究的目的:
- 评估各种机器学习 (ML) 模型在预测喘患者日常SABA使用中的性能.
- 确定对这些预测模型的准确性有助于最重要的特征.
主要方法:
- 利用数字健康平台上的13,202名喘患者的数据,包括吸入器传感器数据.
- 应用了线性回归,随机森林和时卷积网络 (TCN) 来预测每人每天的SABA膨胀.
- 使用解释差异 (R2),平均精度 (AP) 和ROC AUC进行模型性能比较,用于预测有显著SABA使用的日子.
主要成果:
- 一个TCN模型表现出卓越的性能,达到R2为0.562的吸气数和0.344的日复一日变化.
- 该TCN准确地预测了≥10个气泡 (ROC AUC:0.952) 和≥1个气泡 (AP:0.762) 的日子.
- 过去的SABA使用 (7天) 是最重要的预测因素,其次是空气污染物特征.
结论:
- 预测SABA使用可以作为早期临床干预和加强喘自我管理的预测工具.
- 需要进一步的研究来验证这些预测与实际喘恶化事件的相关性.
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