Exploring the Feasibility of Approximate Sequential Pattern Discovery in Massive

探索大规模近似序列模式发现的可行性

• 批准号: 8318881
• 负责人: Satinder Singh Baveja
• 金额: $ 15.93万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8318881
• 关键词: Acute; Address; Admission activity; Adverse event; Algorithms; Area; Blood Pressure; Caregivers; Caring; Characteristics; Chronic; Clinical; Critical Care; Critical Illness; Data; Data Set; Decision Making; Disease; Economics; Environment; Evaluation; Event; Functional disorder; Future; Health; Heart Rate; Hospitals; Individual; Intensive Care Units; Knowledge; Machine Learning; Medical; Methods; Metric; Modeling; Morbidity - disease rate; Noise; Operative Surgical Procedures; Outcome; Patient Admission; Patient Monitoring System; Patients; Pattern; Physicians; Physiological; Pilot Projects; Population; Predictive Value; Receiver Operating Characteristics; Research; Resource Allocation; Risk; Risk Estimate; Sensitivity and Specificity; Series; Signal Transduction; Stratification; Surgical Intensive Care; System; Techniques; Therapeutic; Time; Variant; Work; adverse outcome; base; clinical decision-making; cost; data mining; design; high risk; improved; interest; knowledge base; mathematical model; mortality; novel; novel marker; prevent; prognostic; tool; trend

DESCRIPTION (provided by applicant): The ability to predict the clinical course of intensive care unit (ICU) patients in a timely manner can guide decision making and accelerate research into therapeutic efforts and the economics of care. To achieve this, a variety of ICU-based risk scoring systems have been proposed to risk stratify patients using "snapshots" of variables at specific points in time. These systems are not designed to provide a continuous assessment of patient status in real-time. In recent years, there has been a growing interest in intelligent patient monitoring (IPM) systems that can use information in continuously recorded ICU signals to recognize changes suggestive of dangerous pathophysiologieis. The aim of our research is to augment the knowledge base for these IPM systems by exploring the feasibility of pattern discovery methods to identify novel markers of future risk from large volumes of continuous ICU data. We focus, in particular, on sophisticated methods from data mining and machine learning that are computationally efficient, robust, and able to identify multi-parameter time-series trends useful for risk stratification. To facilitate these efforts, we will utilize the high volume acuity environment of the Surgical ICU (SICU) at the Henry Ford Hospital in Detroit. We will study the use of pattern discovery methods to identify high and low risk patterns in historical data from over 5,000 patients admitted to the SICU, and then prospectively evaluate our findings on real-time SICU data at the Henry Ford from over 3,700 patients. The specific aims of this proposal are: (1) to develop novel decision support tools based on high and low risk patterns discovered from large historical time-series ICU datasets. Using fully-automated and computationally efficient algorithms, we will first identify characteristic units of temporal activity that explain the multi-parameter physiological time-series for ICU patients, and then discover approximate sequences of these characteristic units associated with adverse events in patients with known outcomes. The patterns discovered using our approach will be integrated into real-time decision support tools to signal alerts when high risk patterns are observed; and (2) to prospectively validate decision support tools based on high and low risk patterns in ICU time-series on real-time ICU data. We will conduct a pilot study validating these tools by studying the association between the predictions for these tools and actual adverse outcomes observed in the SICU. Each tool will be evaluated based on the following metrics: sensitivity, specificity, positive predictive value, negative predictive value, area under the receiver operating characteristic curve, and clinical time-frame of prediction. We will further compare the improvement provided by these tools to existing risk scoring systems.

描述（由申请人提供）：及时预测重症监护室（ICU）患者临床病程的能力可以指导决策，并加速对治疗努力和护理经济学的研究。为了实现这一点，已经提出了各种基于ICU的风险评分系统，以使用特定时间点的变量的“快照”对患者进行风险分层。这些系统的设计目的不是实时提供对患者状态的连续评估。近年来，人们对智能患者监测（IPM）系统越来越感兴趣，该系统可以使用连续记录的ICU信号中的信息来识别提示危险病理生理学的变化。我们研究的目的是通过探索模式发现方法的可行性来增加这些IPM系统的知识库，以从大量连续的ICU数据中识别未来风险的新标记。我们特别关注来自数据挖掘和机器学习的复杂方法，这些方法计算效率高，鲁棒性强，能够识别对风险分层有用的多参数时间序列趋势。为了促进这些努力，我们将利用底特律亨利福特医院外科重症监护室（SICU）的高容量急诊环境。我们将研究使用模式发现方法来识别来自SICU收治的5,000多名患者的历史数据中的高风险和低风险模式，然后前瞻性地评估我们对亨利福特医院3,700多名患者的实时SICU数据的研究结果。该提案的具体目标是：（1）基于从大型历史时间序列ICU数据集中发现的高风险和低风险模式开发新的决策支持工具。使用全自动和计算效率高的算法，我们将首先确定时间活动的特征单元，解释ICU患者的多参数生理时间序列，然后发现这些特征单元与已知结果患者的不良事件相关的近似序列。使用我们的方法发现的模式将被集成到实时决策支持工具中，以在观察到高风险模式时发出警报;（2）基于实时ICU数据的ICU时间序列中的高风险和低风险模式前瞻性地验证决策支持工具。我们将进行一项试点研究，通过研究这些工具的预测与SICU中观察到的实际不良结局之间的关联来验证这些工具。将根据以下指标评价每种工具：灵敏度、特异性、阳性预测值、阴性预测值、受试者工作特征曲线下面积和临床预测时间范围。我们将进一步将这些工具提供的改进与现有的风险评分系统进行比较。