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新生儿重症监护病房复杂大量生理数据流的实时多维时间分析

Real-time multidimensional temporal analysis of complex high volume physiological data streams in the neonatal intensive care unit.

作者信息

McGregor Carolyn, James Andrew, Eklund Mike, Sow Daby, Ebling Maria, Blount Marion

机构信息

Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON, Canada.

出版信息

Stud Health Technol Inform. 2013;192:362-6.

Abstract

The intensive care of immature preterm infants is a challenging, dynamic clinical task that is complicated because these infants frequently develop a range of comorbidities as they grow and develop after their premature birth. Earliest reliable condition onset detection is a goal within this setting and high frequency physiological analysis is showing potential new pathophysiological indicators for earlier onset detection of several conditions. To realise this, a platform for multi-stream, multi-condition, multi-feature risk scoring is required. In this paper we demonstrate our multi-stream online analytics approach for condition onset detection and demonstrate a user interface approach for patient state that can be available in real-time to support condition risk scoring.

摘要

对未成熟早产儿的重症监护是一项具有挑战性的动态临床任务,其复杂性在于这些婴儿在早产出生后成长和发育过程中经常会出现一系列合并症。最早的可靠病情发作检测是这一背景下的一个目标,高频生理分析正在显示出用于更早检测多种病情发作的潜在新病理生理指标。为实现这一目标,需要一个用于多流、多病情、多特征风险评分的平台。在本文中,我们展示了用于病情发作检测的多流在线分析方法,并展示了一种可实时提供的用于患者状态的用户界面方法,以支持病情风险评分。

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