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

增强元数据设计、架构和学习 (MeDAL)，用于根据电子健康记录开发基于深度学习的通用预测分析

• 批准号: 10420954
• 负责人: SHAMIM NEMATI
• 金额: $ 33.58万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10420954
• 关键词: Accident and Emergency department; Acute Renal Failure with Renal Papillary Necrosis; Acute Respiratory Distress Syndrome; Adult; Adverse event; Algorithms; Architecture; Area Under Curve; Artificial Intelligence; Biological Assay; Biometry; Blood Vessels; Caring; Case Study; Cessation of life; Clinical; Collaborations; Computer software; Computers; Critical Illness; Data; Data Set; Development; Devices; Effectiveness; Electronic Health Record; Ensure; Environment; Evaluation; Event; Fast Healthcare Interoperability Resources; Frequencies; Funding; General Hospitals; Goals; Health; Healthcare Systems; Heterogeneity; Hospital Costs; Hospital Mortality; Hospitalization; Hospitals; Hour; Hypotension; Incidence; Infection; Inflammation; Injury to Kidney; Inpatients; Institution; Intensive Care Units; Learning; Life; Liver; Lung; Measurement; Measures; Medical; Metadata; Methodology; Methods; Modeling; Morbidity - disease rate; Outcome; Patients; Pattern; Performance; Pharmaceutical Preparations; Pilot Projects; Population; Predictive Analytics; Prevention; Process; Reader; Reproducibility; Research; Research Personnel; Respiratory Failure; Risk; Risk Estimate; Savings; Sepsis; Septic Shock; Site; System; Testing; Time; Training; Uncertainty; Variant; Work; acute care; authority; base; cloud based; deep learning; demographics; design; improved; interest; laboratory equipment; mortality; multi-task learning; novel; organ injury; patient population; patient response; personalized care; portability; prediction algorithm; predictive modeling; prevent; prospective; research and development; response; septic patients; theories; tool; treatment optimization; treatment response; trustworthiness; ward

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.

项目摘要 /摘要 败血症，败血性休克，急性肾脏损伤（AKI），急性呼吸窘迫综合征（ARDS）和 呼吸衰竭是医院死亡率，发病率和持续时间增加的主要原因之一 和住院费用。成功的预防和管理这些条件取决于能力 临床医生可以估计风险，理想情况下，预测和预防这些事件。急性护理设置 特别是重症监护病房（ICU）提供了大量数据的环境 被收购，可以预期，随着可穿戴设备和生物识别量的出现 将在此类设置中可用。但是目前，这些数据很少有效地用于 预后，现有的预测分析风险评分遭受了整个跨越可普遍性的影响 随着时间的流逝，机构和绩效退化。 该提案的PI最近表明，基于深度学习的算法可以可靠 预测急诊室，综合医院病房和ICU的新败血症病例 提前4-6小时，曲线（ROC）下的面积为0.85-0.90。此外，通过2年 通过生物医学高级研发局（Barda）资助的试点研究，我们最近 在一个中心学术联盟中联手回顾性地验证了每个站点的该算法。 我们的合作导致了一个多中心的纵向EHR数据集的重症患者，并且有 产生了几个重要的问题和发现，与便携式和可推广的设计有关 预测分析算法对因差距，错误和偏见引起的问题而强大的算法 由于与工作流有关的因素（例如人员配置级）和异质性，电子健康记录（EHRS） 患者人群和测量设备的。 我们建议通过设计新的深度学习体系结构来大大扩展我们的先前工作 通过护理过程的变异性引入的数据缺失和偏见的强大，2） 开发新的学习方法，以提高拟议模型的普遍性 数据/人口漂移（又称分布变化），3）增强的元数据设计以帮助量化 通过算法控件对此类算法的“使用条件”，以及4）HL7和基于FHIR的前瞻性 实施和测试这些方法，以提供现实世界的临床证据 拟议方法的有效性。最终，这些新颖的方法和工具将增强 我们使用EHR和其他类型的连续测量纵向数据来预测不利的能力 事件，评估患者对治疗的反应，并在旁边进行优化和个性化护理。