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，处于危险中的患者往往提前数小时表现出临床恶化的迹象。现有的早期预警系统由于可靠性差和需要由负担过重的临床工作人员进行监测而存在重大缺陷。几乎五分之一的病人在出院后30天内再次入院，每年给纳税人造成的损失为150亿至170亿美元。因此，迫切需要能够提供及时和准确信息的自动预警系统。该项目旨在整合和挖掘来自多个来源的患者数据，包括常规临床过程、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。具体而言，该项目旨在开发技术和系统，使用一种新的双层系统，为出院患者的临床恶化和再入院提供早期预警。Tier 1在现有医院数据记录上使用数据挖掘算法来识别最有临床恶化和再入院风险的患者。第2层将临床数据与传感器数据相结合，以提高对第1层确定为高危患者的预测准确性。该项目的关键创新方面包括：(1)从异构、多尺度和高维数据流中预测临床恶化和再入院的新数据挖掘算法；(2)预警解释系统，识别最相关的预后因素，并基于新的特征排序算法提出可能的干预措施；(3)提出了一种基于代价敏感学习的传感器动态重构方案，实现了监测成本与监测效果的平衡。目前在普通病房采用的医疗保健实践的进步提供了几个关键的好处，包括：(1)减少了临床工作人员的工作量；(2)持续监测病房病人的能力，可用于分类护理工作，以优化预期的临床结果；(3)将医院监测扩展到高危再入院患者的能力，并通过针对早期先发制人的治疗干预措施减少再入院。将该技术转化为临床实践的计划包括在现实环境中对该技术进行严格评估，并广泛传播算法及其开源实现。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保健费用，以及增加以研究为基础的跨学科卫生信息学研究生培训的机会。有关该项目的更多信息可以在http://www.cse.wustl.edu/~wenlinchen/project/clinical/上找到

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/