Deep-CDS: Deep Learning Semantic Data Lake for Clinical Decision Support

美国ICU每年收治500多万患者，报告的平均死亡率为 范围为8- 19%，或每年约500，000例死亡。脓毒症是院内死亡的主要原因， 其中三分之一的住院死亡是由于败血症。脓毒症的发病率一直在增加， 每年有27万人死于败血症。早期发现恶化已被证明可以减少 需要将患者转移到更高的护理单位，缩短住院时间，并提高生存率。的每个小时 ICU入院延迟与ICU死亡风险增加1.5%和ICU死亡风险增加1%相关。 医院死亡。许多研究表明，每延迟一小时，死亡率就会增加。 抗生素将患者风险分层与适当的医院干预水平相结合， 死亡风险。在楼层单位和ICU中发现的监测水平之间的中间单位中的患者是 尤其难以预测病情恶化的可能性。自动监控、警报和趋势 分析对于识别和主动干预受胁迫的患者至关重要。的当前方法 监测患者健康的特异性低，并且具有显著的改进空间。 该项目将开发Deep-CDS，这是一种基于云的深度学习系统，用于上下文敏感的临床 监测和预测患者健康恶化和脓毒症风险进展的决策支持 这些因素可以实时改善结果并优化整个医院人群的护理管理。 为了支持临床护理团队，Deep-CDS为团队成员提供（a）临床护理知识库， (b)健康状况恶化的预警评分，（c）预测脓毒症的模型，（d） 循证临床实践指南，以及（e）患者健康状况趋势的可视化。深CDS 解决NIGMS败血症诊断和治疗的小企业发展优先事项，NOT-GM-20- 028：1）急诊科设置的诊断工具; 2）预测性临床算法和床旁护理 诊断; 3）结合联合收割机各种类型的数据用于诊断脓毒症患者的技术;和4）临床 决策支持，包括使用人工智能和机器学习方法，以开发工具， 脓毒症的识别、治疗反应和患者恶化的评估以及长期预后 在各种护理环境中进行预测。

More than 5 million patients are admitted annually to United States ICUs with average mortality rate reported ranging from 8-19%, or about 500,000 deaths annually. Sepsis is the leading cause of in-hospital mortality, where one in three inpatient deaths are due to sepsis. Incidence of sepsis has been increasing with 1.7 million sepsis cases and 270,000 deaths per year. Early identification of deterioration has been shown to reduce the need for patient transfer to higher care units, reduce lengths of stay, and improve survival rates. Each hour of delay in ICU admission has been associated with a 1.5% increased risk of ICU death and a 1% increase in risk of hospital death. Many studies support that there is an increase in mortality rate for every hour delay in antibiotics. Pairing patient risk stratification with appropriate levels of hospital intervention is essential to reduce risk of mortality. Patients in intermediate units between the levels of monitoring found in floor units and ICUs are especially difficult to predict possibility of condition deterioration. Automated monitoring, alerts, and trend analysis are essential to identifying and proactively intervening patients under duress. Current methods of monitoring patient health have low specificity and have significant room for improvement. This project will develop Deep-CDS, a cloud-based deep learning system for context-sensitive clinical decision support in monitoring and predicting the deterioration of patient health and progression of sepsis risk factors in real-time to improve outcomes and optimize the management of care across the hospital population. To support the clinical care team, Deep-CDS provides team members with (a) a clinical care knowledgebase, (b) an early warning score for deteriorating health conditions, (c) a model for predicting septic conditions, (d) evidence-based clinical practice guidelines, and (e) visualization of patient health status trends. Deep-CDS addresses NIGMS Priorities for Small Business Development of Sepsis Diagnostics and Therapeutics, NOT-GM-20- 028: 1) Diagnostic tools for emergency department settings; 2) Predictive clinical algorithms and point-of-care diagnostics; 3) Technologies that combine various types of data for diagnosis of sepsis patients; and 4) Clinical decision support, including use of artificial intelligence and machine learning approaches, to develop tools for early recognition of sepsis, assessment of treatment responses and patient deterioration, and long-term prognosis prediction in various care settings.