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