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.

摘要 意义：在这个SBIR项目中，我们建议开发Previse，一种新颖的基于软件的临床决策 本发明提供了一种用于预测急性肾损伤（阿基）并将阿基归因于几种原因之一的CDS支持系统， 机制（病因学）。Previse将使用机器学习方法和从电子 健康记录（EHR），以识别急性肾损伤的早期体征。通过在临床综合症之前这样做 当AKI完全发展时，Previse将为临床医生提供时间进行干预，以预防进一步的AKI。 肾损伤，减少阿基后遗症。将该预测模块与第二模块组合 这表明了导致早期或完全阿基的根本原因，Previse将使临床医生能够 为阿基患者做出更早、更明智的治疗决定。研究问题：一台机器- 基于学习的CDS预测住院患者72小时内阿基的发生和进展， KDIGO第2或第3阶段的进步，性能提供了接收器操作下的区域 特征曲线（AUROC）至少为0.85？是否可以使用贝叶斯模型来推断阿基的原因 准确度高（AUROC ≥ 0.75）？先前的工作：我们已经开发了Previse的原型版本 该系统预测阿基的时间比KDIGO 2或3期标准提前72小时，AUROC接近 0.70.我们之前已经开发了基于机器学习的败血症预测工具，住院死亡率， 和其他不良患者事件，性能比常用的基于规则的 评分系统具体目的：预测患者中KDIGO 2期或3阿基的发病情况。 回顾性数据，提前72小时（目标1）;使用从EHR中提取的数据确定阿基的原因 在发病时，以高准确性，并提出这种因果关系的推断，其可能性，和相关的证据 以人类可理解的方式支持它（目标2）。方法：我们将使用一种 深度递归神经网络（RNN）。这种表达，非线性分类器将纳入时间序列 在EHR的定性部分的信息，也将纳入来自文本的功能 例如放射学报告。在KDIGO阿基前72小时标记AUROC为0.85或更高， 目标1的成功。在目标2中，我们将训练一个动态贝叶斯网络来识别阿基的原因。 我们将使用半监督方法训练这个系统，其中一组阿基示例的原因将是 由临床专家手工注释;这些示例将分为两组，其中一些用于培训 其余的用于测试。目标2将是成功的，如果这种培训的结果，病因识别的准确性 至少0.75的概率。未来的方向：根据拟议的工作， 将在合作医院进行前瞻性研究。