利用人工智能研究接受剖腹产的妇女住院情况
Arianna Scala1, Giuseppe Bifulco2, Anna Borrelli3
1Department of Public Health, University of Naples Federico II, Naples, 80131, Italy. ariannascala7@gmail.com.
BMC public health
|January 20, 2025
概括
诸如心血管疾病和孕前等母亲的并发症显著延长了剖腹产 (CS) 后的住院时间. 识别这些因素有助于医院优化资源配置并降低成本.
科学领域:
- 产科和妇科 产科和妇科
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
背景情况:
- 全球剖腹产率不断上升,需要了解影响产后康复的因素.
- 在CS后住院时间 (LOS) 影响患者的治疗结果和医疗资源利用率.
研究的目的:
- 确定影响剖腹产后住院时间 (LOS) 的关键因素.
- 分析人工智能模型的预测性能,以确定与CS相关的LOS.
主要方法:
- 利用了9,900名在大学医院接受CS治疗的妇女 (2014-2021) 的数据.
- 采用人工智能模型,包括多重线性回归和决策树,以分析LOS预测器.
- 评估母亲和胎儿的特征,以及作为独立变量的并发症.
主要成果:
- 多重线性回归显示,变量和LOS之间存在强烈的相关性 (R值=0.815).
- 显著的LOS预测因素包括异常胎儿,心血管疾病,呼吸系统疾病,高血压,出血,多胎,孕前,以及分娩/手术并发症.
- 决策树模型在预测损失方面取得了75%的准确性.
结论:
- 特定的并发症,特别是心血管疾病和孕前,是扩大后CS的LOS的重要决定因素.
- 研究结果支持针对性医院管理策略,以优化资源和降低成本,基于有影响力的LOS因素.
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