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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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针对老年成人癌症幸存者的个性化风险建模:结合可穿戴数据和自我报告措施,解决时间变化的风险.

Zoe Valero-Ramon1, Gema Ibanez-Sanchez1, Antonio Martinez-Millana1

  • 1ITACA-SABIEN, Universitat Politècnica de València, 46022 Valencia, Spain.

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概括
此摘要是机器生成的。

这项研究将可穿戴设备数据与患者报告的结果相结合,为老年癌症幸存者创建动态风险模型. 研究结果显示,活动和睡眠模式影响了脆弱性和焦虑,改善了个性化癌症护理.

关键词:
动态风险模型的动态风险模型.年长的成年癌症患者.过程采矿过程采矿时间变化的风险.可穿戴设备可穿戴设备.

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科学领域:

  • 数字健康数字健康
  • 在瘤学瘤学.
  • 老年医学 老年医学

背景情况:

  • 电子健康记录 (EHR) 缺乏日常生活的背景,无法远程监控患者.
  • 可穿戴设备提供客观数据,但需要与患者报告的结果进行整合.
  • 老年癌症幸存者需要个性化的护理,考虑到不断变化的风险因素.

研究的目的:

  • 开发针对老年癌症幸存者的脆弱性和焦虑的动态风险模型.
  • 将可穿戴传感器数据与自我报告的结果集成为个性化癌症护理.
  • 分析活动和睡眠模式对患者报告的脆弱性和焦虑的影响.

主要方法:

  • 利用过程挖掘技术来分析可穿戴设备和自我报告结果的真实数据.
  • 将可穿戴设备的客观数据与患者的主观数据 (脆弱性,焦虑,抑郁) 结合起来.
  • 在与医疗保健专业人员合作开发了两种动态风险模型,使用LifeChamps研究的数据.

主要成果:

  • 长时间的久坐活动与更高的脆弱性风险相关.
  • 高度动态的睡眠模式与焦虑和抑郁的报道增加有关.
  • 前列腺转移的患者表现出比其他癌症类型更高的脆弱性风险.

结论:

  • 可穿戴式传感器和人工智能可以增强癌症护理中动态风险因素的分析.
  • 个人化风险模型整合可穿戴和自我报告数据对于不断变化的患者需求至关重要.
  • 了解生活方式模式和心理结果之间的相互作用可以改善支持性癌症护理.