Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine.
Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine.
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DOI：
10.1016/j.coisb.2020.07.001
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发表时间：
2020-04
影响因子：
3.7
通讯作者：
Tewari M
中科院分区：
文献类型：
--
作者：
Tyler J;Choi SW;Tewari M
由于疾病进展的复杂和动态性质，准确地预测个体中疾病的发作和过程是医学中的一个重大挑战。来自可穿戴技术和生物标志物数据的连续数据提供了良好的时间分辨率，这为了解疾病进化的更多信息提供了一个独特的机会，并迎来了个性化和实时医学的新时代。本文中，我们提出了使用同种异体造血干细胞移植（HCT）患者护理作为实时测量的实时，连续测量的生理数据作为检测疾病转变的无创生物标志物方法的潜力。此外，我们回顾了一种使用生物标志物数据来识别疾病进展中的过渡状态的最新计算技术，即动态网络生物标志物方法。在整个过程中，我们认为，在新的个性化和实时医学范式中，在多个领域的协作增加对于实现可穿戴和生物标志物数据的全部潜力至关重要。
Accurately predicting the onset and course of a disease in an individual is a major unmet challenge in medicine due to the complex and dynamic nature of disease progression. Continuous data from wearable technologies and biomarker data with a fine time resolution provide a unique opportunity to learn more about disease evolution and to usher in a new era of personalized and real-time medicine. Herein, we propose the potential of real-time, continuously measured physiological data as a noninvasive biomarker approach for detecting disease transitions, using allogeneic hematopoietic stem cell transplant (HCT) patient care as an example. Additionally, we review a recent computational technique, the landscape dynamic network biomarker method, that uses biomarker data to identify transition states in disease progression and explore how to use it with both biomarker and physiological data for earlier detection of graft-versus-host disease specifically. Throughout, we argue that increased collaboration across multiple fields is essential to realizing the full potential of wearable and biomarker data in a new paradigm of personalized and real-time medicine.
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影响因子：
158.5
作者：
Knowler, WC;Barrett-Connor, E;Nathan, DM
通讯作者：
Nathan, DM
使用样本特定网络对疾病进行个性化表征
影响因子：
14.9
作者：
Liu X;Wang Y;Ji H;Aihara K;Chen L
通讯作者：
Chen L
影响因子：
4.2
作者：
Li, Benjamin Y.;Oh, Jeeheh;Wiens, Jenna
通讯作者：
Wiens, Jenna
影响因子：
2.8
作者：
Pavic, Matea;Klaas, Vanessa;Guckenberger, Matthias
通讯作者：
Guckenberger, Matthias
影响因子：
9.8
作者：
Li X;Dunn J;Salins D;Zhou G;Zhou W;Schüssler-Fiorenza Rose SM;Perelman D;Colbert E;Runge R;Rego S;Sonecha R;Datta S;McLaughlin T;Snyder MP
通讯作者：
Snyder MP