使用纵向数据和深度学习模型来增强在家庭医疗保健中的资源配置
Ling Chen1, Ching-Po Lin2, Chi-Hua Chung1
1Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan.
International journal of medical informatics
|April 29, 2025
概括
人工智能 (AI) 深度学习模型准确预测家庭医疗护理阶段. 这些模型可以帮助优化资源配置,以便在家庭医疗机构中更好地提供患者护理.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 老年医疗护理 医疗护理
背景情况:
- 人口老龄化增加了医疗保健需求和成本.
- 有效的家庭保健计划是必不可少的.
- 准确的患者评估是资源分配和量身定制服务的关键.
研究的目的:
- 探索人工智能 (AI) 在预测家庭医疗护理阶段的应用.
- 通过预测建模来增强家庭护理的提供.
- 在这种情况下,评估深度学习模型的性能.
主要方法:
- 使用台北市医院 (2015-2021) 电子健康记录进行的回顾性研究.
- 将深度学习 (Transformer,LSTM,GRU) 和机器学习模型进行比较.
- 在3,5和10次连续的患者访问中训练模型进行分类任务.
主要成果:
- 在LSTM模型中,通过5次访问,实现了最高的AUC (0.908),区分家庭护理的存在/缺席.
- 变压器模型在5次访问的4个家庭护理阶段 (S0-S3) 的分类中实现了最佳AUC (0.86).
- 在5次访问中训练的模型为预测提供了足够的准确性.
结论:
- 人工智能深度学习模型展示了预测家庭医疗保健阶段的巨大潜力.
- 性能最好的模型可以帮助医疗保健专业人员优化资源配置.
- 准确预测护理阶段可以导致更高效和有效的家庭医疗保健服务.
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