应用多策略改进的灰狼算法,以优化紧急选中的极端梯度提升
Journal of emergency nursing
|September 9, 2025
概括
一个新的机器学习模型,MIGWO-XGBOOST,显著提高了急诊部 (ED) 排列准确性和效率. 这种先进的方法通过更快,更精确的决策来优化患者护理途径.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于临床决策支持
背景情况:
- 有效的急诊室 (ED) 选对资源分配和患者的治疗结果至关重要.
- 传统的分组方法在不断增加的患者数量和复杂病例方面扎.
研究的目的:
- 为ED开发和评估一种新的机器学习分类模型.
- 提高紧急分拣决策的准确性和效率.
主要方法:
- 开发了MIGWO-XGBOOST,一种使用多策略改进的灰狼优化 (MIGWO) 进行参数调整的机器学习模型.
- 处理缺少的数据,并将数据集分为培训 (80%) 和测试 (20%).
- 与XGBOOST,GWO XGBOOST,AdaBoost,LSTM和CNN-BiGRU进行比较的性能.
主要成果:
- 与标准的XGBOOST相比,MIGWO-XGBOOST的准确性得到了8.5%的改善.
- 与GWO-XGBOOST相比,优化时间缩短了9,285秒.
- 与AdaBoost (12.5%),LSTM (3.3%) 和CNN-BiGRU (1.9%) 相比,显示出更高的准确性.
结论:
- MIGWO-XGBOOST为快速准确的ED分类提供了一个强大的框架.
- 该模型在复杂的数据环境中提高了预测强度和计算效率.
- 先进的机器学习可以显著支持紧急决策,并优化患者护理.
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