机器学习技术的应用,用于开发急诊医疗急诊急诊预测模型
1Author Affiliations: Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, and Department of Emergency Medicine, Chiayi Branch, Taichung Veteran's General Hospital (Tsai); and Department of Information Management and Asian Institute for Impact Measurement and Management, National Central University, Taoyuan City (Hu), Taiwan.
Computers, informatics, nursing : CIN
|December 12, 2023
概括
一个新的机器学习模型,即紧急医疗快速选和预测援助模型,准确地预测了急诊室关键患者的结果. 这种工具有助于护士更快地进行分拣,并改善重症监护服务.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 紧急医疗 紧急医疗
背景情况:
- 医疗紧急情况需要快速,明智的临床决策.
- 紧急护士对于最前线的分拣和优先排序至关重要.
- 现有的分类工具可能缺乏对关键结果的预测准确性.
研究的目的:
- 开发一种基于人工智能的模型,用于预测紧急部门的关键结果.
- 为了增强急诊护士的临床决策.
- 提高患者分拣和资源分配的速度和准确性.
主要方法:
- 利用台湾地区教学医院的电子医疗记录.
- 应用了五种监督机器学习技术来开发分类模型.
- 使用F1得分和ROC曲线下的面积来评估模型性能.
主要成果:
- 后勤回归模型实现了F1得分为0.861和AUC为0.855.
- 开发的紧急医疗快速选和预测援助模型的性能优于现有的选秤和预警工具.
- 对于重症监护室的入院和住院的表现优越的预测.
结论:
- 拟议的AI模型显著提高了对关键患者结果的预测.
- 它为 triage 中的急诊护士和重症监护团队提供了宝贵的支持.
- 改善患者护理和有效利用医疗资源的潜力.
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