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

总结 医疗保健引起的不良安全事件（ASES）是可预防的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急诊室 EMS系统“实施基于证据的方法，以减少紧急情况和创伤中的错误 ”，但也承认，由于“缺乏关于儿童保健的高质量数据， 儿童医疗差错的流行病学，特别是在紧急护理系统中。我们的研究团队 开发并验证了EMS图表审查工具，以确定儿童护理中的ASE，并已开始 描述这些事件的流行病学。我们已经确定了儿科院外心脏骤停（OHCA） 作为一个特别高风险的条件，为ASES和穷人的生存。EMS在健康方面发挥着关键作用， 美国人在心脏骤停期间的结果。在心脏病发作的最初几分钟内接受有效治疗 可以使存活率提高一倍或三倍。然而，虽然成人OHCAs和住院儿科OHCAs的生存率 在过去的10-15年里，儿童OHCA的生存率显著增加， 不变我们专注于识别在OHCA的整个事件中发生的可预防的ASE， 是导致死亡率和发病率的主要因素。现状，手工图表审查，认为黄金 用于评估护理安全性和质量标准是昂贵的和劳动密集的。该提案的主要目标是 是通过电子EMS在人群水平上计算检测与儿科OHCA相关的ASE 通过以下研究目标绘制图表：目标1。识别院前护理中的不良安全性事件 通过基于规则和回归的计算处理儿科结构化数据的OHCA儿童 EMS图表。目标二。使用深度学习从EMS图表叙述文本中提取心脏骤停相关指标 NLP技术和弱监督技术，以增强基于规则和回归的自动 筛选EMS图表。目标3. Proximity展示了在以下方面自动检测ASE的可扩展性： OHCA在全州人口规模。该提案利用了经验丰富的 一个多学科研究团队，包括信息学家和临床科学家与儿科专业知识 患者安全和美国心脏协会指南的制定。成功完成项目 aims将创建一个自动化工具的基本要素，该工具能够在大型 用于识别、监测并最终缓解可预防的儿科院前患者安全事件。 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。

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.