在COVID大流行期间采用机器学习算法来预测患者 (建筑工人) 停留时间
S Selvakumara Samy1, S Karthick1, Meghna Ghosal1
1Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu 603203 India.
概括
机器学习模型可以预测建筑工人的住院时间 (LOS),改善健康和安全政策. 随机森林实现了90%的准确性,优化了资源配置,降低了建筑行业的成本.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 建筑工程安全 建筑工程安全
背景情况:
- 印度建筑行业面临着工人健康和安全方面的挑战,而疫情加剧了这一挑战.
- 该行业的无组织性导致了由于工人住院而导致的重大成本.
- 预测患者停留时间 (LOS) 对医疗保健和行业的资源管理和成本降低至关重要.
研究的目的:
- 应用机器学习算法来预测患者在医院的停留时间 (LOS).
- 通过使用预测分析,加强建筑公司内部的健康和安全政策.
- 评估不同机器学习模型在预测LOS方面的有效性.
主要方法:
- 使用了密集护理医疗信息中心 (MIMIC III) 数据集.
- 应用了四种机器学习算法:决策树分类器,随机森林,人工神经网络 (ANN) 和物流回归.
- 执行数据预处理,特征选择 (用chi2选择最佳),以及数据分割用于培训和测试.
主要成果:
- 对比了四个应用的机器学习算法的准确性.
- 随机森林最初实现了89%的准确性.
- 使用网格搜索在Random Forest上的超参数调整提高了准确度到90%.
结论:
- 机器学习,特别是随机森林,在预测患者LOS方面表现出高准确度.
- 预测建模可以显著改善建筑行业的健康安全政策和资源优化.
- 计算技术的整合提供了一种现代化的方法来管理建筑中的健康风险和成本.
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