快速医疗互操作性 基于资源的预测交付支持系统 类型:模型开发和评估研究
João Coutinho-Almeida1,2,3, Alexandrina Cardoso4, Ricardo Cruz-Correia1,2,3
1Faculty of Medicine, University of Porto, Porto, Portugal.
JMIR formative research
|April 8, 2024
概括
机器学习系统可以识别潜在的不必要的剖腹产,帮助临床决策,并可能降低葡萄牙的成本. 需要进一步分析现实世界的应用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 卫生经济学 卫生经济学
背景情况:
- 剖腹产产率在全球范围内不断上升,影响着母亲和新生儿的健康.
- 葡萄牙的剖腹产分娩率在2020年达到36.3%,原因不明.
- 国家倡议旨在减少葡萄牙的剖腹产事件.
研究的目的:
- 开发基于机器学习 (ML) 的决策支持系统,以识别可能不必要的剖腹产.
- 利用产科临床决策支持系统的互操作性标准.
- 确定交付类型的预测因素,并评估ML工具的经济影响.
主要方法:
- 来自9家葡萄牙公共医院 (2019-2020年) 的母亲和胎儿数据的回顾分析.
- 开发和部署ML模型,以LightGBM为效率选择.
- 将ML模型输出与临床医生的评估和经济模拟进行比较.
主要成果:
- 轻GBM模型实现了ROC曲线下的88%的面积.
- 在试验阶段,3.8%的分娩引发了对可能不必要的剖腹产的警报.
- 经济模拟表明,对30%的葡萄牙公共医院有潜在的好处.
结论:
- ML系统在识别可能不正确的剖腹产决策方面表现有前途,影响医疗实践和健康经济学.
- 挑战包括模型偏差和进一步评估临床决策影响的需要.
- 仔细实施和强有力的分析对于实现该系统的全部潜力至关重要.
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