使用性能状态和可穿戴设备指标对肺癌患者的数据驱动评估
Ioannis Bilionis1, Luis Fernandez Luque1, Santiago Ponce Aix2
1Adhera Health Inc., Santa Cruz, CA, USA.
Studies in health technology and informatics
|April 9, 2025
概括
可穿戴设备与肺癌患者的病情有中等的相关性,有助于治疗监测. 需要进一步的研究来改进机器学习模型,以预测患者状况的变化.
科学领域:
- 在瘤学瘤学.
- 数字健康数字健康
- 生物医学工程 生物医学工程
背景情况:
- 肺癌,特别是非小细胞肺癌 (NSCLC),是全球癌症死亡的主要原因.
- 传统的绩效状态 (PS) 评估是主观的,缺乏临床间的一致性.
- 对身体活动和PS的客观监测对于个性化肺癌治疗至关重要.
研究的目的:
- 评估可穿戴设备和用于监测肺癌患者的移动应用程序的可用性.
- 探索临床医生评估的PS和可穿戴设备衍生数据之间的相关性.
- 研究可穿戴技术在管理肺癌患者护理方面的潜力.
主要方法:
- 进行了一项两阶段的非干预性观察性研究 (LUPA研究).
- 第一阶段采用混合方法方法来评估可用性和数据实用性.
- 第二阶段包括61名肺癌患者在临床观察中,将可穿戴设备数据与ECOG-PS分数相关联.
主要成果:
- 从可穿戴设备的数据和临床医生报告的ECOG-PS得分之间观察到适度的相关性.
- 在利用机器学习 (ML) 模型来预测患者状况波动时,发现了挑战.
- 可穿戴技术显示出对肺癌患者客观监测的前景.
结论:
- 可穿戴设备提供了一种潜在的客观方法来监测肺癌患者的病情.
- 改进ML模型,增加样本大小,并整合更多可穿戴数据特征对于提高预测准确性至关重要.
- 未来的研究应该专注于优化可穿戴数据集成,以改善瘤学的临床决策.
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