Department of Software and Knowledge Engineering, University San Pablo CEU, 28668 Madrid, Spain.
Artif Intell Med. 2009 Nov;47(3):219-38. doi: 10.1016/j.artmed.2009.08.002. Epub 2009 Sep 30.
Threshold alarms, the support supplied by commercial monitoring devices to supervise the signs that pathologies produce over physiological variables, generate a large amount of false positives, owing to the high number of artifacts in monitoring signals, and they are not capable of satisfactorily representing and identifying all monitoring criteria used by healthcare staff. The lack of an adequate support for monitoring the evolution of physical variables prevents the suitable exploitation of the information obtained when monitoring critical patients. This work proposes a solution for designing intelligent alarms capable of addressing the flaws and limitations of threshold alarms.
The solution proposed is based on the multivariable fuzzy temporal profile (MFTP) model, a formal model for describing certain monitoring criteria as a set of morphologies defined over the temporal evolution of the patient's physiological variables, and a set of relations between them. The MFTP model represents these morphologies through a network of fuzzy constraints between a set of points in the evolution of the variables which the physician considers especially relevant. We also provide a knowledge acquisition tool, TRACE, with which clinical staff can design and edit alarms based on the MFTP model.
Sixteen alarms were designed using the MFTP model; these were capable of supervising monitoring criteria that could be satisfactorily supervised with commercial monitoring devices. The alarms were validated over a total of 196h of recordings of physiological variables from 78 different patients admitted to an intensive care unit. Of the 912 alarm triggerings, only 7% were false positives. A study of the usability of the tool TRACE was also carried out. After a brief training seminar, five physicians and four nurses designed a number of alarms with this tool. They were then asked to fill in the standard System Usability Scale test. The average score was 68.2.
The proposal presented herein for describing monitoring criteria, comprising the MFTP model and TRACE, permits the supervision of monitoring criteria that cannot be represented by means of thresholds, and makes it possible to construct alarms that give a rate of false positives far below that for threshold alarms.
阈值报警是商业监测设备为监测病理产生的生理变量信号而提供的支持，但由于监测信号中存在大量伪迹，会产生大量的假阳性，并且无法令人满意地表示和识别医护人员使用的所有监测标准。缺乏对生理变量变化的适当监测支持，阻碍了对重症患者监测信息的有效利用。本文提出了一种设计智能报警的解决方案，旨在解决阈值报警的缺陷和局限性。
所提出的解决方案基于多变量模糊时间轮廓（MFTP）模型，这是一种用于描述某些监测标准的形式化模型，将监测标准表示为一组在患者生理变量的时间演变上定义的形态，以及它们之间的一组关系。MFTP 模型通过在变量演化过程中的一组关键点之间建立模糊约束网络来表示这些形态，这些关键点是医生认为特别相关的点。我们还提供了一个知识获取工具 TRACE，临床人员可以使用该工具基于 MFTP 模型设计和编辑报警。
使用 MFTP 模型设计了 16 个报警，这些报警能够监督可以用商业监测设备进行满意监督的监测标准。这些报警在 78 名不同的重症监护病房患者的生理变量共 196 小时的记录上进行了验证。在 912 次报警触发中，只有 7%是假阳性。还对工具 TRACE 的可用性进行了研究。在简短的培训研讨会之后，五名医生和四名护士使用该工具设计了一些报警。然后要求他们填写标准的系统可用性量表测试。平均得分为 68.2。
本文提出的描述监测标准的方法，包括 MFTP 模型和 TRACE，允许监督不能通过阈值表示的监测标准，并能够构建假阳性率远低于阈值报警的报警。
