权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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：
10815094
负责人：
JEANNE-MARIE GUISE
金额：
$ 60.88万
依托单位：
BETH ISRAEL DEACONESS MEDICAL CENTER
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 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10815094
关键词：
Academy Adult Adverse event Algorithms American American Heart Association Automated Annotation Caring Cessation of life Child Child Care Child Health Childhood Clinical Collection Complex Computing Methodologies Data Data Set Detection Development Electronics Elements Emergency Care Emergency Situation Emergency medical service Encapsulated Environment Epidemiology Evaluation Event Future Goals 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 Recommendation Records Research Research Personnel Role Safety Sampling Scientist Secure Structure Supervision Survival Rate System Techniques Testing Text Transcend United States Utah Visualization software Work care systems computerized tools cost data visualization deep learning effective therapy evidence base experience health care delivery high risk hospital care improved mortality multidisciplinary neural novel out-of-hospital cardiac arrest patient safety pediatric emergency pediatric patients point of care prevent prospective 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.

总结