Early Identification of Acute Kidney Injury Using Deep Recurrent Neural Nets, Presented with Probable Etiology

使用深层循环神经网络早期识别急性肾损伤，并提出可能的病因

• 批准号: 9621546
• 负责人: Ritankar Das
• 金额: $ 34.93万
• 依托单位: DASCENA, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2020-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9621546
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Admission activity; Affect; Area; Bayesian Modeling; Clinical; Clinical Decision Support Systems; Clinical Trials; Computer software; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Early identification; Electronic Health Record; Etiology; Event; Fatigue; Future; Goals; Hand; Head Start Program; Hospital Costs; Hospital Mortality; Hospitals; Hour; Human; Injury to Kidney; Inpatients; Kidney; Knowledge; Label; Laboratories; Lead; Machine Learning; Measurement; Methods; Monitor; Patient risk; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Phase; Physical Examination; Plant Roots; Process; Prospective Studies; Radiology Specialty; Randomized Controlled Trials; Receiver Operating Characteristics; Recurrence; Renal function; Reporting; Research; Risk; Risk Assessment; Sepsis; Series; Small Business Innovation Research Grant; Supervision; Syndrome; System; Testing; Text; Therapeutic; Time; Training; United States; Work; base; clinical decision support; computer based statistical methods; effective therapy; experience; experimental study; imaging study; improved; improved outcome; insight; learning strategy; mortality; novel; overtreatment; predictive tools; prevent; prototype; rapid diagnosis; recurrent neural network; relating to nervous system; success; tool; trend

Abstract Significance: In this SBIR project we propose to develop Previse, a novel, software-based clinical decision support (CDS) system for predicting acute kidney injury (AKI), and attributing AKI to one of several causal mechanisms (etiologies). Previse will use machine learning methods and information drawn from the electronic health record (EHR) to identify the early signs of acute kidney injury. By doing so before the clinical syndrome of AKI is fully developed, Previse will give clinicians the time to intervene with the goals of preventing further kidney damage, and decreasing the sequelae of AKI. Combining this prediction module with a second module that suggests the underlying causes responsible for an incipient or full AKI, Previse will enable clinicians to make earlier and better-informed treatment decisions for AKI patients. Research Question: Can a machine- learning-based CDS predict the development and progression of AKI in hospitalized patients 72 hours in advance of KDIGO stage 2 or 3, with performance providing an area under the receiver operating characteristic curve (AUROC) of at least 0.85? Is it possible to use a Bayesian model to infer the cause of AKI with high accuracy (AUROC ≥ 0.75)? Prior work: We have developed a prototype version of the Previse system which predicts AKI up to 72 hours in advance of KDIGO stage 2 or 3 criteria, with an AUROC near 0.70. We have previously developed machine-learning-based predictive tools for sepsis, in-hospital mortality, and other adverse patient events with performance significantly improved over commonly used rules-based scoring systems. Specific Aims: To predict the onset of chart-abstracted KDIGO stage 2 or 3 AKI in retrospective data, 72 hours in advance (Aim 1); to use data drawn from the EHR to identify the cause of AKI at time of onset with high accuracy, and to present this causal inference, its likelihood, and relevant evidence supporting it in a human-interpretable fashion (Aim 2). Methods: We will predict the onset of AKI using a deep, recurrent neural network (RNN). This expressive, nonlinear classifier will incorporate time-series information in the qualitative portions of the EHR and will also incorporate features derived from text components, such as radiology reports. Labeling AUROC of 0.85 or higher at 72 hours pre-KDIGO AKI will constitute success in Aim 1. In Aim 2, we will train a dynamic Bayesian network to identify the cause of AKI. We will train this system using semi-supervised methods, where the causes of a set of AKI examples will be hand-annotated by clinician experts; these examples will be split into two groups, with some used for training and the remainder for testing. Aim 2 will be successful if this training results in etiology identification accuracy of at least 0.75 in the test set. Future Directions: Following the proposed work, the combined Previse system will be deployed for prospective studies at partner hospitals.

抽象的 意义：在这个SBIR项目中，我们建议开发一个基于软件的新型临床决定 预测急性肾脏损伤（AKI）的支持（CD）系统，并将AKI归因于几个因果关系之一 机制（病因）。预期将使用机器学习方法和从电子中汲取的信息 健康记录（EHR）确定急性肾脏损伤的早期迹象。通过在临床综合征之前这样做 AKI的完整开发，可以让临床医生有时间干预防止进一步的目标 肾脏损害，并减少AKI的后遗症。将此预测模块与第二个模块相结合 这表明基本原因负责初期或全部AKI，可以使临床医生能够 为AKI患者做出更早，更有信息的治疗决策。研究问题：机器可以吗 基于学习的CD预测住院患者AKI的发展和进展72小时 Kdigo第2阶段或3阶段的提前，性能在接收方下提供了操作的区域 特征曲线（AUROC）至少为0.85？是否可以使用贝叶斯模型来推断AKI的原因 具有高精度（AUROC≥0.75）？先前的工作：我们已经开发了预期的原型版本 系统可以预测AKI在KDIGO阶段2或3个标准之前最多72小时，并且AUROC接近 0.70。我们以前已经开发了基于机器学习的预测工具，用于败血症，院内死亡率， 与常用的基于规则的其他不良患者事件相比 评分系统。具体目的：预测图表吸收的Kdigo阶段2或3 Aki 回顾性数据，提前72小时（AIM 1）；使用从EHR绘制的数据来识别AKI的原因 在发病时以高准确性，并提出这种因果推断，其可能性和相关证据 以人为解剖的方式支持它（AIM 2）。方法：我们将使用A来预测AKI的发作 深，复发的神经网络（RNN）。这个表现力的非线性分类器将合并时间序列 EHR定性部分中的信息，还将合并从文本中得出的功能 组件，例如放射学报告。在72小时之前，AKI的AUROC为0.85或更高的标签将 在AIM 1中构成成功。在AIM 2中，我们将训练动态的贝叶斯网络以识别AKI的原因。 我们将使用半监督的方法训练该系统，其中一组AKI示例的原因是 由临床专家手工注销；这些示例将分为两组，其中一些用于培训 以及其余的测试。如果此培训导致病因识别准确性，AIM 2将成功 在测试集中至少为0.75。未来的方向：遵循拟议的工作，联合预期系统 将在合作伙伴医院部署进行前瞻性研究。