智能框架用于从轻微症状早期检测严重的儿科疾病
Zelal Shearah1, Zahid Ullah1, Bahjat Fakieh1
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|October 28, 2023
概括
这项研究开发了一种机器学习 (ML) 框架,用于识别严重的儿科疾病. 该系统准确地检测儿童的紧急情况,帮助家长决定是否需要紧急护理.
科学领域:
- 儿科医学 儿科医学
- 计算健康 计算健康
- 医疗保健中的人工智能
背景情况:
- 许多导致死亡或长期疾病的儿童疾病都有可预防或可治疗的原因.
- 严重儿科疾病的早期症状可能是轻微的,并使及时诊断复杂化.
- 准确和快速评估儿科疾病严重程度对于有效的干预至关重要.
研究的目的:
- 开发一种机器学习 (ML) 框架,用于检测儿童疾病的严重程度.
- 创建一个系统,可以区分紧急/严重的儿科病情和不那么严重的病情.
- 为家长提供有关孩子是否需要立即访问急诊室的指导.
主要方法:
- 该研究实施了一种机器学习 (ML) 框架,使用九种不同的ML方法.
- 该模型结合了关键变量,包括出现症状,风险因素 (例如年龄) 和孩子的病史.
- 进行了ML方法的性能评估,以确定检测疾病严重程度的最有效方法.
主要成果:
- 拟议的ML框架在识别严重的儿科疾病方面表现出很高的表现.
- 决策树和随机森林算法实现了最高的准确性,达到94%.
- 结果表明该框架作为儿科决策支持系统的可靠性.
结论:
- 开发的ML框架是检测严重儿科疾病的可靠工具.
- 该系统可以有效地帮助父母做出明智的决定,为他们的孩子寻求紧急医疗护理.
- 这项研究提供了一个有希望的,亲子友好的方法来管理常见的儿童症状和评估疾病严重程度.
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