从多层并发性疾病网络中解开摇篮到坟墓的疾病轨迹
Elma Dervić1,2,3, Johannes Sorger1, Liuhuaying Yang1
1Complexity Science Hub Vienna, Vienna, Austria.
NPJ digital medicine
|March 7, 2024
概括
这项研究在70年内绘制了1260个不同的患者疾病轨迹,使用了新的网络方法. 识别关键分歧点可以个性化未来的医疗保健决策.
科学领域:
- 医疗信息学 医疗信息学
- 网络科学 网络科学
- 公共卫生 公共卫生
背景情况:
- 了解长期疾病进展对于有效的医疗保健至关重要.
- 患者的健康轨迹是复杂的,并受到不断变化的并发症的影响.
- 当前的模型往往缺乏疾病发展的生命周期视角.
研究的目的:
- 确定典型的终身疾病轨迹和影响医院利用率和死亡率的关键事件.
- 开发和应用一种新的多层疾病网络方法来分析疾病的同时发生的演变.
- 通过整合生命历程视角,实现数据驱动的个性化医疗.
主要方法:
- 利用了奥地利4400万次住院病人的独特数据集 (2003-2014).
- 开发了一个多层疾病网络,其中的节点是10岁年龄组 (层) 内的诊断.
- 采用无监督的集群来检测疾病轨迹,并确定关键事件作为分离的轨迹点.
主要成果:
- 鉴定了1260个不同的疾病轨迹 (618名女性,642名男性) 跨越多达70年.
- 轨迹平均有9个诊断,揭示了复杂的长期健康路径.
- 发现了70对不同的轨迹,突出了导致生命后期不同健康状况的关键事件.
结论:
- 疾病轨迹框架为了解患者健康旅程提供了一种新的方法.
- 识别关键事件和分离的途径可以预测未来的高风险诊断.
- 研究结果支持将个性化,生命过程视角纳入临床决策,以改善患者的治疗结果.
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