Using Machine Learning to find a life saving needle in a haystack of children's emergencies

利用机器学习在儿童紧急情况的大海捞针中找到救生针

基本信息

批准号：
10341239
负责人：
JEANNE-MARIE GUISE
金额：
$ 70.29万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10341239
关键词：
Academy Adult Adverse event Algorithms American American Heart Association Caring Cessation of life Child Child Care Child Health Childhood Clinical Collection Complex Computing Methodologies Data Data Set Detection Development Elements Emergency Care Emergency Situation Emergency medical service Encapsulated Environment Epidemiology Evaluation Event Foundations Future Goals Gold Guidelines Health Healthcare Systems Heart Arrest Hospitals Incidence Informatics Information Systems Infrastructure Injury Interdisciplinary Study Knowledge Learning Life Logic Machine Learning Manuals Medical Medical Errors Medicine Methods Modeling Modernization Monitor Morbidity - disease rate Natural Language Processing Ontology Oregon Outcome Output Pediatric Hospitals Play Population Pre-hospitalization care Procedures Provider Quality of Care Records Research Research Personnel Role Safety Sampling Savings Scientist Secure Structure Supervision Survival Rate System Techniques Testing Text Transcend United States Utah Visualization software Work base care costs care outcomes care systems computerized tools data visualization deep learning effective therapy evidence base experience health care delivery high risk improved mortality multidisciplinary novel out-of-hospital cardiac arrest patient safety pediatric emergency pediatric patients point of care prevent prospective relating to nervous system screening sequence learning service providers structured data tool trauma care

项目摘要

SUMMARY Adverse safety events (ASEs) resulting from medical care are a leading cause of preventable injury and death in the United States. The National Academy of Medicine recommends that hospital and Emergency Medical Services (EMS) systems “implement evidence-based approaches to reduce errors in emergency and trauma care for children,” but acknowledges that implementation is limited by the “paucity of high-quality data on the epidemiology of medical errors in children, particularly within the emergency care system.” Our research team developed and validated an EMS chart review tool to identify ASEs in the care of children and has begun to describe the epidemiology of these events. We have identified pediatric out-of-hospital cardiac arrest (OHCA) as a particularly high-risk condition for ASEs and poor survival. EMS plays a critical role in the health and outcomes of Americans during cardiac arrests. Receipt of effective treatment in the first few minutes of cardiac arrest can double or triple survival. However, while survival from adult OHCAs and in-hospital pediatric OHCAs have both increased significantly over the last 10-15 years, survival from pediatric OHCA remains largely unchanged. We focus on identifying preventable ASEs occurring over the entire episode of OHCA, recognized to be a major contributor to mortality and morbidity. The status quo, manual chart reviews, considered the gold standard for evaluating safety and quality of care, are costly and labor-intensive. The main goal of this proposal is to computationally detect ASEs associated with pediatric OHCA at a population level from electronic EMS charts through the following Study Aims: Aim 1. Identify adverse safety events in the prehospital care of children with OHCA via rules- and regression-based computational processing of structured data in pediatric EMS charts. Aim 2. Extract cardiac arrest-related indicators from EMS chart narrative text using deep learning NLP techniques and weak supervision techniques to augment the rules-and regression--based automatic screening of EMS charts. Aim 3. Prospectively demonstrate the scalability of automated detection of ASEs in OHCA at the scale of statewide populations. This proposal leverages the strengths of an experienced multidisciplinary research team that includes informaticians and clinician-scientists with expertise in pediatric patient safety and American Heart Association Guideline development. Successful completion of the project aims will create the foundational elements of an automated tool capable of screening EMS charts on a large scale to identify, monitor, and ultimately mitigate preventable pediatric prehospital patient safety events. Additionally, the computational tools and annotated dataset created in the course of this project will serve as valuable infrastructure to support future clinical and computational research.

概括医疗保健产生的不良安全事件（ASE）是可预防伤害和死亡的主要原因在美国。美国国家医学院建议医院和紧急医疗服务（EMS）系统“实施基于证据的方法来减少出现和创伤的错误照顾儿童”，但承认实施受到“缺乏关于有关的高质量数据的限制儿童医疗错误的流行病学，尤其是在急诊系统中。”我们的研究团队开发并验证了EMS图表审查工具，以识别儿童照顾的ASES，并已开始描述这些事件的流行病学。我们已经确定了小儿院外心脏骤停（OHCA）作为ASE和生存不佳的特别高风险条件。 EMS在健康中起着至关重要的作用在心脏骤停期间，美国人的结果。在心脏的头几分钟内接收有效治疗逮捕可以两倍或三重生存。但是，而成人OHCA和院内小儿OHCA的生存在过去的10 - 15年中，两者都显着增加，小儿OHCA的生存仍然很大不变。我们专注于确定在OHCA的整个情节中发生的可预防的ASE，这是公认的成为死亡率和发病率的主要贡献者。现状，手动图表评论考虑了黄金评估安全性和护理质量的标准是昂贵且劳动力密集的标准。该提议的主要目标是从电子EMS的人群水平上检测到与小儿OHCA相关的ASE 通过以下研究的图表目的：目标1。确定院前护理中的不良安全事件 OHCA通过规则和基于回归的小儿数据的计算处理的儿童 EMS图表。目的2。从EMS图表叙事文本中提取与心脏骤停的指标，使用深度学习 NLP技术和弱监督技术，以增强基于回归的自动筛选EMS图表。目标3。 OHCA在全州人口的规模上。该提议利用了经验的优势多学科研究团队，包括有帮助的人和临床科学家，具有儿科专业知识患者安全与美国心脏协会指南开发。成功完成项目 AIMS将创建能够在大型上筛选EMS图表的自动化工具的基础元素扩展以识别，监测并最终减轻可预防的小儿院前患者安全事件。此外，在本项目过程中创建的计算工具和注释数据集将作为有价值的基础设施，以支持未来的临床和计算研究。