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使用机器学习来识别兰克郡事故和紧急服务的频繁出勤情况

Fergus Reid¹, S Josephine Pravinkumar², Roma Maguire¹

¹Computer and Information Sciences, University of Strathclyde, Glasgow, UK.

|March 4, 2025

概括

机器学习模型可以通过识别关键健康状况和风险因素来预测频繁的事故和紧急情况 (A&E) 出勤. 这项研究提高了预测的准确性,并确定了患者的风险概况,以改善医疗管理.

科学领域:

医疗分析医疗分析
医学中的预测建模.
公共卫生信息学公共卫生信息学

背景情况:

事故和紧急情况 (A&E) 服务的频繁参与者对医疗保健系统构成重大挑战.
机器学习 (ML) 为频繁的A&E出席提供了预测性管理的潜力,但其应用目前有限.
识别频繁A&E出席的风险因素对于开发有效的预防策略至关重要.

研究的目的:

评估机器学习 (ML) 方法来预测频繁的A&E出席率.
为了确定与频繁的A&E出席相关的关键风险因素.
将发现与现有研究进行比较,以寻找共同点和差异.

主要方法:

利用了NHS Lanarkshire (2021-2022) 中17437名A&E患者的健康和社会护理数据.
测试了五种分类模型:多项逻辑回归,随机森林,支向量机,k-最近邻居和多层感知子 (MLP).
采用沙普利值来识别风险因素,并使用精度,回忆,F1分数和曲线下面积来评估模型.

主要成果:

多层感知子 (MLP) 模型获得了最高的F1得分 (0.75),超过了其他测试模型.
在所有评估的模型中,关键的健康状况和风险因素一致预测了频繁的出席率.
模型性能显示了一些变化,突出了数据集特定的特征.

结论:

结合ML模型可以提高频繁的A&E出席率的预测准确性.
该研究成功地确定了与频繁使用A&E相关的重大风险因素和健康状况.
结果与现有研究保持一致,同时提供特定于数据集和方法的独特见解.

关键词:

频繁的出席经常的出席事故和紧急情况.机器学习是机器学习.公共卫生公共卫生.有关风险因素的风险因素.

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