4.3 Article

Using What You Get Dynamic Physiologic Signatures of Critical Illness

期刊

CRITICAL CARE CLINICS
卷 31, 期 1, 页码 133-+

出版社

W B SAUNDERS CO-ELSEVIER INC
DOI: 10.1016/j.ccc.2014.08.007

关键词

Data hierarchy; Fused parameter; Physiologic signature; Cardiopulmonary instability; Machine learning

资金

  1. National Institutes of Health [NR013912, HL07820, HL67181]
  2. Direct For Computer & Info Scie & Enginr
  3. Div Of Information & Intelligent Systems [1320347] Funding Source: National Science Foundation

向作者/读者索取更多资源

The development and resolution of cardiopulmonary instability take time to become clinically apparent, and the treatments provided take time to have an impact. The characterization of dynamic changes in hemodynamic and metabolic variables is implicit in physiologic signatures. When primary variables are collected with high enough frequency to derive new variables, this data hierarchy can be used to develop physiologic signatures. The creation of physiologic signatures requires no new information; additional knowledge is extracted from data that already exist. It is possible to create physiologic signatures for each stage in the process of clinical decompensation and recovery to improve outcomes.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据