基于人工智能的智能紧急服务支持911呼叫处理器使用文本功能和SVM模型进行数字健康优化
Afraa Attiah1, Manal Kalkatawi1
1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Frontiers in big data
|July 22, 2025
概括
一个由人工智能驱动的紧急呼叫助理 (ECA) 帮助911呼叫处理器减少紧急情况下的错误. 这个数字健康工具使用人工智能和NLP来改善应急响应,并通过准确的实时决策支持挽救生命.
科学领域:
- 数字健康数字健康
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 9-1-1呼叫处理人员面临高压环境,导致紧急数据收集中的潜在错误.
- 应急响应中的人类错误可能会对9111的表现和患者的结果产生负面影响.
研究的目的:
- 引入人工智能驱动的数字健康框架,即紧急呼叫助手 (ECA),以帮助911呼叫处理人员.
- 利用人工智能和NLP来改善应急响应,准确的决策和减少人为错误.
主要方法:
- 该ECA框架将音频呼叫转换为文本,并使用NLP进行分析,以获取上下文信息.
- 支持矢量机器 (SVM) 学习模型以92.7%的准确度对紧急情况进行分类,根据受伤严重程度和武器参与来优先考虑.
主要成果:
- 该ECA框架通过预测紧急情况类型,建议问题,提供指示和生成报告来帮助呼叫处理人员.
- 使用SVM进行紧急分类显示出高准确度,伤害严重程度和武器参与是关键因素.
结论:
- 该ECA框架提供了一个数字健康解决方案,以提高911呼叫处理的准确性和效率.
- 在紧急医疗保健中,人工智能和NLP的整合可以彻底改变反应,改善患者的治疗结果,并减少关键错误.
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