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

项目摘要/摘要 败血症、感染性休克、急性肾损伤(AKI)、急性呼吸窘迫综合征(ARDS)和 呼吸衰竭是医院死亡率、发病率和病程延长的主要原因之一。 以及住院费用。这些情况的成功预防和管理有赖于 临床医生估计风险，最理想的是预测和预防这些事件。急诊护理环境 尤其是重症监护病房(ICU)提供了海量数据的环境 随着可穿戴设备和生物识别补丁的出现，预计会有更多的数据 将在这样的设置中可用。但目前，这些数据中很少有被有效利用的 预测，而现有的预测分析风险分数在以下方面缺乏概括性 机构和同一机构内的绩效随着时间的推移而下降。 该方案的PI最近证明了基于深度学习的算法能够可靠地 预测急诊科、综合医院病房和ICU的新败血症病例 提前4-6小时，曲线下面积(ROC)为0.85-0.90。此外，通过两年的 由生物医学高级研究和发展局(BARDA)资助的试点研究，我们最近 在一个多中心学术联盟中联合起来，在每个地点回溯验证这一算法。 我们的合作已经产生了危重患者的多中心纵向EHR数据集，并 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)和异质性导致的电子健康记录(EHR) 患者群体和测量设备的数量。 我们建议通过设计新的深度学习体系结构来显著扩展我们之前的工作 对护理过程中的可变性带来的数据缺失和偏差具有很强的抵抗力，2) 开发新的学习方法，以提高拟议模型在以下方面的推广能力 数据/人口漂移(也称为分布变化)，3)增强的元数据设计以帮助量化 通过算法控制使用这种算法的条件，以及4)基于HL7和FHIR的预期 实施和测试这些方法，以提供真实世界的临床证据 所提议的方法的有效性。最终，这些新颖的方法和工具将增强 我们能够使用EHR和其他类型的连续测量的纵向数据来预测不利 事件，评估患者对治疗的反应，并在旁边优化和个性化护理。

Project Summary / Abstract Sepsis, Septic Shock, Acute Kidney Injury (AKI), acute respiratory distress syndrome (ARDS) and respiratory failure 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 significantly expand our prior work by designing new deep learning architectures that are 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 clinical 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.