A machine learning based fetal monitoring system to predict and prevent fetal hypoxia.

项目摘要/摘要： 尽管EFM在美国被广泛部署用于大多数交付，但它未能降低 新生儿脑病等缺氧性损伤，尽管剖宫产率增加。这种缺乏 改进归因于在手动分析过程中模糊指南的应用不一致 EFM的痕迹。市场上现有的用于增强医生能力的自动化工具采用以下形式 低精度、简单的基于规则的警报，这会导致警报疲劳，也无法提供改进。 该项目提出了建立和验证一个机器学习模型来预测产中胎儿 低氧具有很高的敏感性和特异性，以满足这一需求。使用包含50,000条轨迹的多站点数据集 再加上电子健康记录，结合了临床知识和各种机器学习 技术将被用来创造一个具有领先性能的模型。清除FDA设定的高标准 患者的安全性，这项提议旨在通过展示高灵敏度来验证这一模型 以及对这个大型多站点数据集的坚持部分的特异性，以及一项用户研究来证明 借助软件帮助提高临床医生的绩效。在本项目论证了安全和 此模式对患者护理的有效性，未来的第二阶段将测试集成此模式的软件解决方案 在产房和产房。这项提案中概述的研究计划将为产科医生提供有价值的 以证据为基础的工具，帮助他们解释EFM轨迹。

Project Summary/Abstract: Although EFM is widely deployed in the United States for most deliveries, it has failed to reduce rates for hypoxic injuries such as neonatal encephalopathy, despite an increased rate of cesarean sections. This lack of improvement has been attributed to inconsistent applications of vague guidelines during manual analysis of EFM tracings. Existing automated tools available in the market to augment physician capabilities take the form of low-precision simplistic rule-based alerts, which cause alarm fatigue and also fail to deliver improvements. This project proposes the creation and validation of a machine learning model for prediction of intrapartum fetal hypoxia with high sensitivity and specificity to address this need. Using a multi-site dataset of 50,000 tracings coupled with electronic health records, a combination of clinical knowledge and a variety of machine learning techniques will be used to create a model with leading performance. To clear the high bar set by FDA for patient safety with a de novo device, this proposal aims to validate this model by demonstrating high sensitivity and specificity on a held-out portion of this large multi-site data set, along with a user study to demonstrate improved performance by clinicians with software assistance. After this project demonstrates the safety and efficacy of this model for patient care, a future Phase II will beta test a software solution integrating this model in labor and delivery wards. The research plan outlined in this proposal will give obstetricians a valuable evidence-based tool to help them interpret EFM tracings.