基于模糊推理,神经网络和支持矢量机器的新型决策支持系统,用于医疗保健中的主动风险管理
Amine En-Naaoui1, Mohammed Kaicer2, Aicha Aguezzoul3
1Department of Mathematics, Ibn Tofail University, Kenitra, Morocco; National Institute of Oncology, Ibn Sina University Hospital Center, Rabat, Morocco.
International journal of medical informatics
|April 2, 2024
概括
本研究介绍了使用模糊逻辑和人工智能的决策支持系统 (DSS),以改善发展中国家的医疗风险管理,实现高准确度的风险分类.
科学领域:
- 医疗保健管理的管理
- 风险工程风险工程
- 人工智能的人工智能
背景情况:
- 风险管理在医疗保健中至关重要,特别是在发展中国家.
- 新兴技术为工程师提供了有效管理医疗保健风险的解决方案.
研究的目的:
- 开发一个针对发展中国家医疗保健限制的决策支持系统 (DSS).
- 能够做出明智的决定,决定风险耐受性等级和风险治疗优先级.
主要方法:
- 结合故障模式和效果分析 (FMEA) 与模糊逻辑用于稳健的风险评估,产生模糊的风险优先级号码 (RPN).
- 利用人工神经网络进行风险耐受性分类,并支持向量机来优先考虑风险.
- 引入了一个"控制因子"来评估纠正不可容忍风险的可行性,增强FMEA对风险治疗的优先级.
主要成果:
- 模糊的FMEA克服了经典FMEA的局限性.
- 在风险耐受性分类中实现了超过98%的准确性.
- 证明了模糊推理系统在不精确情况下优先考虑风险的实用性,模糊优先结果的准确率为74%.
结论:
- 开发的DSS显示在医疗保健风险管理中的有效性.
- 建议在发展中国家的各种医疗保健部门进行进一步的验证,以确认更广泛的适用性和效率.
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