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

基本信息

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
项目类别：
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

项目摘要

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 Previse，一种新颖的、基于软件的临床决策用于预测急性肾损伤 (AKI) 的支持 (CDS) 系统，并将 AKI 归因于几个原因之一 Previse 将使用机器学习方法和从电子数据中获取的信息。健康记录（EHR）可在临床综合征出现之前识别急性肾损伤的早期迹象。 AKI 已完全发展，Previse 将为指挥官提供干预时间，以防止进一步发生肾脏损伤，并减少 AKI 的后遗症。将此预测模块与第二个模块相结合。表明导致早期或完全 AKI 的根本原因，Previse 将能够优于为 AKI 患者做出更早、更明智的治疗决策研究问题：机器可以吗？基于学习的 CDS 在 72 小时内预测住院患者 AKI 的发生和进展 KDIGO 第 2 或第 3 阶段的进步，其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 AKI 的原因？具有高精度（AUROC ≥ 0.75）？之前的工作：我们开发了 Previse 的原型版本系统可提前 72 小时预测 AKI，达到 KDIGO 2 或 3 阶段标准，AUROC 接近 0.70。我们之前开发了基于机器学习的脓毒症、院内死亡率、和其他不良患者事件，与常用的基于规则的方法相比，性能显着提高具体目标：预测图表抽象 KDIGO 2 期或 3 期 AKI 的发作。提前 72 小时进行回顾性数据（目标 1），使用从 EHR 中提取的数据来确定 AKI 的原因在发病时以高精度提供该因果推论、其可能性和相关证据以人类可解释的方式支持它（目标 2）：我们将使用预测 AKI 的发作。这种富有表现力的非线性分类器将包含时间序列。 EHR 定性部分中的信息，还将包含源自文本的特征组件，例如在 KDIGO AKI 前 72 小时标记 AUROC 为 0.85 或更高。构成目标 1 的成功。在目标 2 中，我们将训练动态贝叶斯网络来识别 AKI 的原因。我们将使用半监督方法训练该系统，其中一组 AKI 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，其中一些用于培训如果此训练能够实现病因识别的准确性，则其余的目标 2 将成功。测试集中至少为 0.75 未来方向：根据提议的工作，组合 Previse 系统。将被部署在合作医院进行前瞻性研究。