III: Medium: Hardware/Software Accelerated Data Mining for Real-Time Monitoring of Streaming Pediatric ICU Data

在美国，每天都有至少1000名儿童在儿科重症监护病房(PICU)为自己的生命而战。在PICU中，病人的病情由自动传感器仔细监测。这些数据大多显示在一个五分钟的“滑动窗口”显示中，因此被召唤到病人床边的医生总是要考虑她最近的病史。然而，“掉下”这个滑动窗口的数据会发生什么呢？在大多数PICU中，只有一小部分数据被粗略地汇总和记录，但令人惊讶的是，这些数据中的大部分都被简单地丢弃了。即使大部分或全部数据都被记录下来，其庞大的数据量也会让研究人员和分析师不堪重负；几乎没有工具可以帮助他们理解这些数据并从中学习。这些目前被丢弃的数据是一个潜在的可操作知识的金矿，可以改善结果(降低死亡率/发病率、减轻疼痛等)，并降低成本(隐含在减少的住院时间)。然而，这种数据本身的性质--多变量、异质性、高维、时态、噪声、有偏见和高频--对传统的统计和数据挖掘分析技术提出了重大挑战。在这个项目中，一个跨学科的调查团队正在开发：(A)可扩展的机器学习算法，用于挖掘带注释的PICU数据的档案，以找到可用于辅助诊断和预测结果的规律性和模式；(B)实时监测ICU遥测技术，以检测在离线步骤中发现的模式和规则是否已经发生，并可用于指导干预(医生的行动)。该项目汇集了数据挖掘(Keogh，Tsotras)、高性能计算(Najjar)和医学(Wetzel)方面的专家，以调查上述问题的整体解决方案。该项目对加州大学河滨分校研究生和本科生的研究型高级培训做出了贡献。该项目创建的调查结果、数据集、软件和教材将永久存档于www.cs.ucr.edu/~eamonn/UCRPICU/

On any given day in America, there are at least one thousand children fighting for their lives in Pediatric Intensive Care Units (PICUs). In the PICU the patient's condition is carefully monitored with automatic sensors. Most of this data is shown in a five-minute "sliding window" display, so a doctor summoned to a patient's bedside always has her most recent history to consider. However what happens to the data that "falls off" this sliding window? In most PICUs, a tiny fraction of it is coarsely aggregated and recorded, but surprisingly, most of this data is simply discarded. Even if most or all the data is recorded, its sheer volume simply overwhelms researchers and analysts; very few tools exist to help them make sense of and learn from this data. This currently discarded data is a potential goldmine of actionable knowledge that could improve outcomes (decreased mortality/morbidity, reduce pain, etc.), and reduce costs (implicit in reduced length of stay). However, the very nature of this data - multivariate, heterogeneous, high dimensional, temporal, noisy, biased, and high frequency - poses significant challenges for traditional analytical techniques from statistics and data mining.In this project, an interdisciplinary team of investigators is developing: (a) xcalable machine learning algorithms for mining archives of annotated PICU data to find regularities and patterns that can be used to aid in diagnostics and prediction of outcomes; and (b) techniques for monitoring ICU telemetry in real time to detect whether the patterns and rules discovered in the offline step have occurred and can be used to guide interventions (actions by the doctor).The project brings together experts in data mining (Keogh, Tsotras), high performance computing (Najjar), and medicine (Wetzel) to investigate holistic solutions to the above problems. The project contributes to research-based advanced training of graduate and undergraduate students at the University of California Riverside. The findings, datasets, software, and teaching materials created by this project will be archived in perpetuity at www.cs.ucr.edu/~eamonn/UCRPICU/