临床环境中基于模型的数据集成。
Model-based data integration in clinical environments.
作者信息
Heldt Thomas, Verghese George C
机构信息
Computational Physiology and Clinical Inference Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5209-12. doi: 10.1109/IEMBS.2010.5626101.
As a result of improved hospital information-technology infrastructure and declining costs of storage media, vast amounts of physiological waveform and trend data can now be continuously collected and archived from bedside monitors in operating rooms, intensive care units, or even regular hospital rooms. The real-time or off-line processing of such volumes of high-resolution data, in attempts to turn raw data into clinically actionable information, poses significant challenges. However, it also presents researchers - and eventually clinicians - with unprecedented opportunities to move beyond the traditional individual-channel analysis of waveform data, and towards an integrative patient-monitoring framework, with likely improvements in patient care and safety. We outline some of the challenges and opportunities, and propose strategies for model-based integration of physiological data to improve patient monitoring.
由于医院信息技术基础设施的改善以及存储介质成本的下降，现在可以从手术室、重症监护病房甚至普通病房的床边监测仪持续收集并存档大量生理波形和趋势数据。试图将如此大量的高分辨率数据进行实时或离线处理，将原始数据转化为临床可操作的信息，这带来了重大挑战。然而，这也为研究人员——最终也为临床医生——提供了前所未有的机会，使其能够超越传统的波形数据单通道分析，迈向综合患者监测框架，有望改善患者护理和安全。我们概述了一些挑战和机遇，并提出基于模型整合生理数据以改善患者监测的策略。
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