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

摘要 由医疗护理引起的不良安全事件是可预防的伤害和死亡的主要原因 在美国。美国国家医学科学院建议医院和急救医疗 服务(EMS)系统“实施基于证据的方法，以减少急诊和创伤中的错误 但承认，由于缺乏关于儿童的高质量数据，实施受到限制。 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。我们发现了儿科院外心脏骤停(UchA) 作为自闭症和低存活率的特别高风险条件。EMS在健康和健康方面发挥着关键作用 美国人在心脏骤停期间的结局。在心脏病发作的头几分钟内接受有效治疗 逮捕可以使存活率增加一倍或三倍。然而，虽然成人OHCA和住院儿童OHCA的存活率 在过去的10-15年里，两者的存活率都有了显著的提高，但在很大程度上，儿科uchA的存活率仍然存在 保持不变。我们的重点是确定在整个uchA事件中发生的可预防的AS，公认的 成为死亡率和发病率的主要贡献者。现状，手工海图回顾，被认为是黄金 评估安全和护理质量的标准是昂贵和劳动密集型的。这项提案的主要目标是 是从电子EMS在人口水平上计算检测与儿科uchA相关的ASE 通过以下研究目标的图表：目标1.确定院前护理中的不良安全事件 通过基于规则和回归的儿科结构化数据的计算处理 EMS图表。目的2.利用深度学习从EMS图表叙事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化 EMS图表的筛选。目标3.前瞻性地展示ASES自动检测的可扩展性 在全州范围内的人口规模上。这项建议充分利用了一位经验丰富的 多学科研究团队，包括信息学家和临床医生-具有儿科专业知识的科学家 患者安全和美国心脏协会指南的制定。项目圆满完成 AIMS将创建能够大规模筛选EMS图表的自动化工具的基本要素 规模以识别、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带注释的数据集将用作 有价值的基础设施，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.