Analyzing Streaming Multi-Sensor Data to Predict Stroke in Preterm Babies

分析流式多传感器数据以预测早产儿中风

• 批准号: 10250034
• 负责人: WILLIAM D. SHANNON
• 金额: $ 25.6万
• 依托单位: WILLIAM D SHANNON CONSULTING, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10250034
• 关键词: Adult; Adverse event; Algorithms; Area; Brain; Cerebral Palsy; Cessation of life; Clinical; Clinical Research; Collection; Computer software; Consumption; Critical Care; Data; Data Analyses; Decision Making; Development; Early Diagnosis; Early treatment; Electronic Health Record; Ensure; Environment; Event; Explosion; Fatigue; Generations; Goals; Graph; Growth; Health Personnel; Hemorrhage; Hospitals; Institutional Review Boards; Intellectual functioning disability; Internet; Internet of Things; Intubation; Measures; Medical; Medicine; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Neonatal; Neonatal Intensive Care Units; Neurosciences; Numerical value; Nursing Homes; Nursing Staff; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Performance; Phase; Physicians; Premature Infant; Proliferating; Psyche structure; Retrospective cohort; Sepsis; Small Business Innovation Research Grant; Source; Specific qualifier value; Specificity; Statistical Computing; Stream; Stroke; System; Telemedicine; Testing; Time; Universities; Very Low Birth Weight Infant; Washington; commercialization; data quality; data streams; design; experience; falls; high risk; improved; innovation; insight; intraventricular hemorrhage; live stream; medical schools; mortality; phase 1 study; prediction algorithm; predictive modeling; predictive tools; premature; sensor; sensor technology; software development; tool

PROJECT SUMMARY/ABSTRACT In this Phase I SBIR application for Analyzing Streaming Multi-Sensor Data for Predicting Stroke in Preterm Infants, we propose developing statistical software for predicting adverse medical events using sensor data from preterm infants. While medical sensor data is becoming widely available as part of the Internet of Medical Things (IoMT), healthcare provider’s ability to use these data is limited by a lack of real-time predictive algorithms for detecting deteriorating conditions in patients. Very low birth-weight preterm infants have a high risk of experiencing intraventricular hemorrhage (IVH), a serious form of bleeding in the brain associated with high rates of mortality and other serious conditions such as cerebral palsy. The algorithm we will develop uses an innovative approach of transforming sensor data into graphs of associations and applies decision rules from statistical process control to determine when a patient’s data indicates an adverse medical event such as an IVH. If successful, this algorithm can be implemented in neonatal intensive care units (NICU) to provide real-time alerts to hospital staff, allowing for early detection and treatment of IVH before it causes severe damage. Two aims are proposed: to develop the software and test it on an existing, curated, large retrospective cohort of NICU data collected at Washington University (Aim 1); and to compare the accuracy of the method and software to existing predictive models of neonatal IVH and other outcomes (Aim 2). The first aim builds upon existing proprietary software for object oriented data analysis and encompasses testing different methods of measuring and relating sensor data, as well as evaluating decision rules for the graphical objects created from these data. The second aim involves testing the accuracy and specificity of the alerts created by this method to ensure it can detect adverse events significantly better than chance or existing algorithms, and to ensure it does not substantially contribute to the problem of false alerts. If successful, this project will lead to a Phase II proposal to test the algorithm in real-time inside an NICU with nursing staff and develop the algorithm into a marketable software platform. Phase II would also involve extending the testing of this software for other types of sensor data and medical events, such as monitoring medical conditions for adult patients or nursing homes, etc. This project has commercialization potential both in providing an important tool for improving patient care in NICUs, and in the broader context of developing tools for predicting adverse medical events from all types of IoMT data.

项目总结/摘要 在此阶段I SBIR应用程序中，分析多传感器数据流以预测早产儿卒中 婴儿，我们建议开发统计软件，用于预测不良医疗事件使用传感器数据， 早产儿虽然医疗传感器数据作为医疗物联网的一部分正在变得广泛可用， （IoMT），医疗保健提供商使用这些数据的能力受到缺乏实时预测算法的限制， 检测病人的病情恶化极低出生体重早产儿有很高的风险， 经历脑室内出血（IVH），这是一种严重的脑出血， 死亡率和其他严重疾病，如脑瘫。我们将开发的算法使用一种创新的 一种将传感器数据转换为关联图的方法，并应用来自统计的决策规则 过程控制，以确定患者的数据何时指示不良医疗事件，例如IVH。如果 如果成功，该算法可以在新生儿重症监护病房（NICU）中实施，以提供实时警报 对医院工作人员，允许早期发现和治疗IVH之前，它造成严重损害。 提出了两个目标：开发软件并在现有的，策划的，大型回顾性队列中进行测试。 在华盛顿大学收集的NICU数据（目标1）;并比较方法和软件的准确性 新生儿IVH和其他结局的现有预测模型（目标2）。第一个目标是建立在现有的 面向对象的数据分析专有软件，包括测试不同的测量方法 关联传感器数据，以及评估从这些数据创建的图形对象的决策规则。 第二个目标是测试通过这种方法创建的警报的准确性和特异性，以确保 可以比偶然或现有算法更好地检测不良事件，并确保它不会 这在很大程度上导致了错误警报的问题。 如果成功，该项目将导致第二阶段的建议，在NICU内实时测试该算法， 护理人员和开发的算法成为一个适销对路的软件平台。第二阶段还将涉及 将该软件的测试扩展到其他类型的传感器数据和医疗事件，例如监测 成人患者或疗养院等的医疗条件。该项目具有商业化潜力， 为改善NICU的患者护理提供重要工具，并在更广泛的背景下开发工具 用于从所有类型的IoMT数据中预测不良医疗事件。