基于时间抽象的重症监护数据异常检测:立场文件

G. J. Gelatti, A. Carvalho, P. Rodrigues
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引用次数: 2

摘要

在重症监护中,不断产生大量的信息。对这些数据流的分析可以提供有价值的见解,以改善对患者的监测。数据的数量、频率和复杂性,以及未标记的数据,使他们的分析成为一项具有挑战性的任务。机器学习(ML)技术已被成功地用于挖掘数据流,以提取对医疗监测有用的知识。它包括检测传感器行为的变化、机器或系统的故障以及数据异常。异常(或离群值)检测是一项ML任务,旨在发现数据集中的异常或异常。在医学背景下,这些例外可能代表一种新的疾病模式、需要进一步调查的事件、行为改变或潜在的健康并发症。尽管在数据流中进行分析是一项具有挑战性的任务,但时态抽象技术应该有所帮助,因为它们处理基于时间的数据的管理和抽象,在其上下文中提供每个数据对象的高级可视化。本文的目的是回顾异常检测和时间抽象的最新研究,并讨论它们在重症监护数据流中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anomaly Detection Through Temporal Abstractions on Intensive Care Data: Position Paper
A large amount of information is continuously generated in intensive health care. An analysis of these data streams can supply valuable insights to improve the monitoring of the patients. The volume, frequency and complexity of data, which come unlabeled, make their analysis a challenging task. Machine learning (ML) techniques have been successfully employed for mining data streams to extract useful knowledge for health care monitoring. It includes the detection of changes in the behavior of sensors, failures on machines or systems, and data anomalies. Anomaly (or outlier) detection is a ML task that aims to find exceptions or abnormalities in a dataset. These exceptions, in a medical context, can represent a new disease pattern, an event to be further investigated, behavior changes or potential health complications. Despite of its analysis in data streams is a challenging task, temporal abstractions techniques should help due to they deal with the management and abstraction of time based data, offering high level of visualization of each data object in its context. The aim of this paper is to review recent research in anomaly detection and temporal abstractions and discuss the application of their combination to intensive care data streams.
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