SCH: EXP: Integrated Real-Time Clinical Deterioration Prediction for Hospitalized Patients and Outpatients

住院患者的意外死亡仍然很常见，尽管有证据表明，处于危险之中的患者往往提前几个小时就显示出临床恶化的迹象。现有的早期预警系统存在重大缺陷，因为它们的可靠性较差，而且需要负担过重的临床工作人员进行监测。几乎每5名患者中就有1名在出院后30天内再次入院，每年纳税人的成本为150-170亿美元。因此，迫切需要能够提供及时和准确信息的自动化预警系统。该项目寻求整合和挖掘来自多个来源的患者数据，包括常规临床流程、床边监测、家庭传感和现有电子数据源，以促进以患者为中心的优化决策。具体地说，该项目旨在开发技术和系统，利用一种新的两级系统，对出院患者的临床恶化和再次入院提供早期预警。第一级对现有医院数据记录使用数据挖掘算法，以确定临床恶化和再次住院的风险最大的患者。第二层将临床数据与传感器数据相结合，以提高对被第一层识别为有风险的患者的预测的准确性。该项目的关键创新方面包括：(1)新的数据挖掘算法，用于从异质、多尺度和高维数据流预测临床恶化和再入院；(2)警报解释系统，以识别最相关的预后因素，并基于新的特征排名算法建议可能的干预；(3)基于成本敏感学习的新方案，以动态重新配置传感器，以实现监测成本和有效性之间的良好平衡。因此，目前在普通病房中使用的医疗保健实践的进步提供了几个关键好处，包括：(1)减少了临床工作人员的工作量；(2)能够对病房患者进行持续监测，可用于对护理工作进行分类，以优化预期的临床结果；(3)能够将医院监测扩展到再次住院的高风险患者，并通过瞄准早期先发制人的治疗干预措施来减少再次住院的好处。将技术转化为临床实践的计划包括在现实世界环境中对技术进行严格评估，以及广泛传播算法及其开源实施。该项目的一些潜在的更广泛的影响包括改善临床结果，降低患者死亡率和医疗成本，以及增加研究生在卫生信息学方面基于研究的跨学科培训的机会。有关该项目的更多信息，请访问：http://www.cse.wustl.edu/~wenlinchen/project/clinical/

Unexpected deaths of hospitalized patients continue to be common despite evidence that patients who are at risk often show signs of clinical deterioration hours in advance. Existing early warning systems have significant shortcomings because of their poor reliability and the need for monitoring by overburdened clinical staff. Almost 1 out of 5 patients are readmitted within 30 days of hospital discharge with an annual cost to tax payers of $15-17 Billion. Hence, there is an urgent need for automated early warning systems that can provide timely and accurate information. The project seeks to integrate and mine patient data from multiple sources, including routine clinical processes, bedside monitoring, at-home sensing, and existing electronic data sources to facilitate optimized patient-centered decision making. Specifically, the project aims to develop techniques and systems to provide early warning of clinical deterioration and hospital readmission of discharged patients using a novel two-tier system. Tier 1 uses data mining algorithms on existing hospital data records to identify patients who are most at risk of clinical deterioration and readmission. Tier 2 combines clinical data with sensor data to improve the accuracy of predictions on patients who are identified as being at risk by Tier 1. Key innovative aspects of the project include: (1) new data mining algorithms for predicting clinical deterioration and readmission from heterogeneous, multi-scale, and high-dimensional data streams; (2) an alert explanation system to identify the most relevant prognostic factors and suggests possible intervention based on novel feature ranking algorithms; (3) a novel scheme based on cost-sensitive learning to dynamically reconfigure the sensors for achieving good tradeoff between monitoring cost and effectiveness. The resulting advances in healthcare practices that are currently employed in general wards offer several key benefits including (1) reduced workload on clinical staff; (2) capability for continuous monitoring of ward patients that can be used to triage nursing efforts in order to optimize the desired clinical outcomes; (3) capability to extend hospital monitoring to patients at high-risk for hospital readmission with the attendant benefits of reducing readmissions by targeting early preemptive therapeutic interventions.Plans for transitioning the technology to clinical practice include rigorous evaluation of the technology in real-world settings and broad dissemination of the algorithms and their open-source implementations. Some potential broader impacts of the project include improved clinical outcomes, reduced patient mortality rates and healthcare costs, and enhanced opportunities for research-based interdisciplinary training of graduate students in health informatics. Additional information about the project can be found at: http://www.cse.wustl.edu/~wenlinchen/project/clinical/