新生儿出生时体重低:调查发病率,风险因素,以及用于风险估计的AI预测建模
Archana Maju1, Sarita Shokandha1, Sugandha Arya2
1Rajkumari Amrit College of Nursing, DGHS, Ministry of Health and Family Welfare, New Delhi, India.
Journal of neonatal-perinatal medicine
|June 16, 2025
概括
低出生体重 (LBW) 影响30.47%的新生儿. 关键的危险因素包括母亲体重增加不足,早产,胎儿并发症和多胎妊娠. 一个AI模型准确地预测了LBW风险.
科学领域:
- 新生儿健康 新生儿健康
- 孕产妇和胎儿医学 孕产妇和胎儿医学
- 医疗保健中的人工智能
背景情况:
- 低出生体重 (LBW) 是全球母亲健康和产前护理有效性的关键指标.
- 评估LBW发病率和确定相关风险因素对于改善新生儿结果至关重要.
- 使用人工智能的预测建模可以增强早期检测和干预策略.
研究的目的:
- 确定新生儿低出生体重 (LBW) 的发生率和重大风险因素.
- 开发一个人工智能 (AI) 驱动的预测模型,用于LBW风险评估.
- 评估AI预测模型的准确性和潜在的临床实用性.
主要方法:
- 采用了双重研究设计,结合了描述性和病例控制方法.
- 描述性和推断性统计数据被用于数据分析.
- 一个基于人工智能的物流回归模型被开发用于预测LBW.
主要成果:
- 每1000个活产婴儿中,LBW的发病率为304.7 (30.47%).
- 确定的重大风险因素包括母亲体重增加不足 (<9公斤),早产妊娠 (<37周),胎儿并发症和多胎妊娠.
- 人工智能预测模型在根据出生体重对新生儿进行分类时,总体准确率高达90%.
结论:
- 已识别的LBW风险因素在很大程度上是可以修改的,这强调了早期产前护理的重要性.
- 人工智能预测模型显示出高准确性和早期风险检测的潜力.
- 将这种人工智能模型集成到医疗保健系统中可以显著减少LBW发生率并改善新生儿健康结果.
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