Enhanced Metadata Design, Architecture, and Learning (MeDAL) for Development of Generalizable Deep Learning-based Predictive Analytics from Electronic Health Records

败血症、感染性休克和急性肾损伤(AKI)是导致医院死亡的主要原因之一。 发病率，以及住院时间和费用的增加。成功的预防和管理 这些情况取决于临床医生估计风险的能力，最理想的是预测和预防的能力。 这些事件。急性护理环境，特别是重症监护病房(ICU)提供了一个环境 其中获取了大量数据，预计随着可穿戴设备和 生物识别补丁在这样的设置中将有更多的数据可用。但目前，这些数据很少 有效地用于预测，而现有的预测性分析风险评分缺乏 跨机构的概括性和同一机构内的绩效随时间的推移而下降。 这一提案的PI最近证明了基于深度学习的算法可以可靠地预测 急诊科、综合医院病房和ICU的新败血症病例增加了4-6例 提前数小时，曲线下面积(ROC)为0.85-0.90。此外，通过为期两年的试点 这项由生物医学高级研究和发展局(BARDA)资助的研究，我们最近加入了 迫使一个多中心学术联盟在每个站点回溯验证该算法。我们的 合作产生了危重患者的多中心纵向EHR数据集，并已 生成了与可移植和可推广设计相关的几个重要问题和发现 预测分析算法对因以下方面的差距、错误和偏差而产生的问题具有很强的稳健性 由于与工作流程相关的因素(例如人员配备水平)而产生的电子健康记录，以及 患者群体和测量设备。 我们建议通过设计更健壮的新深度学习体系结构来继续我们先前的工作 针对护理过程中的可变性带来的数据缺失和偏差，2)发展 新的学习方法，以提高所提出的模型在数据/总体下的泛化能力 漂移(也称为分布变化)，3)增强了元数据设计，以帮助量化“使用条件” 通过算法控制，以及4)基于HL7和FHIR的预期实现和 对这些方法进行测试，以提供建议的有效性的真实证据 接近了。最终，这些新的方法和工具将增强我们使用EHR和其他 持续测量的纵向数据类型，以预测不良事件，评估患者对 治疗，并在旁边优化和个性化护理。

Sepsis, Septic Shock, and Acute Kidney Injury (AKI) are among the top causes of hospital mortality, morbidity, and an increase in duration and cost of hospitalization. Successful prevention and management of these conditions rely on the ability of clinicians to estimate the risk, and ideally, to anticipate and prevent these events. Acute care settings and in particular intensive care units (ICUs) provide an environment where an immense amount of data is acquired, and it is expected that with the advent of wearables and biometric patches even more data will be available in such settings. But at present, very little of these data are used effectively to prognosticate, and the existing predictive analytics risk scores suffer from lack of generalizability across institutions and performance degradation within the same institution over time. The PIs on this proposal recently demonstrated that a Deep Learning-based algorithm can reliably predict new sepsis cases in the emergency departments, general hospital wards, and ICUs by as much as 4-6 hours in advance and an area under the curve (ROC) of 0.85-0.90. Furthermore, through a 2-year pilot study funded via Biomedical Advanced Research and Development Authority (BARDA), we recently joined forces in a multicenter academic consortium to retrospectively validate this algorithm at each site. Our collaboration has resulted in a multi-center longitudinal EHR dataset of critically ill patients and has generated several important questions and findings related to design of portable and generalizable predictive analytics algorithms that are robust to problems arising from gaps, errors, and biases in electronic health records (EHRs) due to workflow-related factors (e.g. staffing-level), and heterogeneity of patient populations and measurement devices. We propose to continue our prior work by designing new deep learning architectures that are more robust to data missingness and biases introduced through the variability in process of care, 2) development of new learning methodologies to improve generalizability of the proposed models under data/population drifts (aka distributional changes), 3) enhanced metadata design to assist in quantifying ‘conditions for use’ of such algorithms via algorithmic controls, and 4) HL7 and FHIR-based prospective implementation and testing of these methodologies to provide real-world evidence for the effectiveness of the proposed approaches. Ultimately, these novel methodologies and tools will enhance our ability to use EHR and other types of continuously measured longitudinal data to predict adverse events, assess patients’ response to therapy, and optimize and personalize care at the beside.