一个增强的灰狼优化器增强了机器学习预测模型,用于患者流量预测.
Xiang Zhang1, Bin Lu2, Lyuzheng Zhang3
1Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
Computers in biology and medicine
|June 26, 2023
概括
医院使用人工智能大数据进行资源管理,但患者流量预测仍然是一个挑战. 一个新的SRXGWO-SVR模型提高了患者流量预测的准确性,优化了医院资源配置.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 运营研究 运营研究
背景情况:
- 医院越来越多地使用人工智能和大数据来改善门诊服务,减少等待时间.
- 当前的人工智能系统由于环境,患者和医生行为等因素,往往无法达到预期.
- 准确的患者流量预测对于有效的医疗资源管理和及时的患者接入至关重要.
研究的目的:
- 开发一个先进的患者流预测模型,以解决当前医院资源管理的局限性.
- 通过考虑动态的患者流动模式和客观规则来预测患者的医疗需求.
- 为了提高医院门诊服务优化的效率和准确性.
主要方法:
- 提出了一个新的优化算法,SRXGWO (序列,考奇和定向突变灰狼优化器).
- 集成SRXGWO与支持向量回归 (SVR) 来创建SRXGWO-SVR患者流量预测模型.
- 通过基准函数实验验验证了SRXGWO的性能,并与其他12个算法进行了比较.
主要成果:
- 与其他七种模型相比,SRXGWO-SVR模型显示出更高的预测准确度和更低的错误率.
- 在废除和同行算法比较测试中,SRXGWO算法显示出高性能.
- 该模型使用培训和测试数据集有效预测患者流动动态和医疗需求.
结论:
- SRXGWO-SVR模型为医院患者流量预测提供了可靠和高效的解决方案.
- 该系统可以显著帮助医院优化医疗资源管理,提高运营效率.
- 改进的患者流量预测有助于改善医院门诊服务质量和减少患者等待时间.
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