对心力衰竭患者的入院优先考虑,这些患者有多种并发症
Rahul Awasthy1, Meetu Malhotra1, Michael L Seavers1
1Data Science, Harrisburg University of Science and Technology, Harrisburg, PA, United States.
Frontiers in digital health
|July 17, 2024
概括
这项研究引入了一种机器学习 (ML) 工具,用于优先考虑紧急护理中的心力衰竭 (HF) 患者. ML模型在识别高风险患者方面实现了超过97.8%的效率,以更好地分配资源.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 急性心力衰竭 (HF) 管理需要有效的患者优先级,特别是伴随性疾病.
- 目前的紧急护理系统面临着快速评估和根据死亡风险对HF患者进行排名的挑战.
- 自动决策支持可以提高医疗保健的运营效率.
研究的目的:
- 开发一种使用机器学习 (ML) 的自动化患者优先级 (PP) 工具,用于紧急护理中的HF患者.
- 提高医疗保健提供者为急性HF患者提供服务的效率和有效性.
- 支持医疗保健工作人员优先考虑患有慢性病和并发病的HF患者.
主要方法:
- 应用机器学习模型到FysioNet数据集 (2016-2019) 中的齐贡第四人民医院的HF患者.
- 利用住院和死亡记录进行模型培训和验证.
- 将模型结果与HCUP马里兰州住院患者数据 (2014) 进行比较,以评估可概括性.
主要成果:
- 机器学习模型在识别高风险HF患者时表现出超过97.8%的效率和95%的特异性.
- 这些模型根据患者的死亡风险水平准确地对患者进行了排名.
- 该PP工具有效地支持HF患者管理的风险评估.
结论:
- 开发的基于ML的PP工具显著提高了紧急护理场所中HF患者的优先级.
- 这种方法支持有效地将资源分配给最迫切需要的患者.
- 这些发现表明,ML对于提高医疗保健效率和患者结果的更广泛的适用性.
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