人工智能驱动的呼吸困难综合征预测非常低出生体重婴儿:韩国多中心前性队列研究
Woocheol Jang1, Yong Sung Choi2, Ji Yoo Kim2
1Biomedical Engineering, Kyung Hee University, Yongin-si, Republic of Korea.
一个人工智能模型准确地预测了早产婴儿的呼吸困扰综合征 (RDS),有助于针对性地治疗出生体重非常低的新生儿的表面活性剂,并防止不必要的干预.
科学领域:
- 新生儿医学 新生儿医学
- 人工智能的人工智能
- 预测分析是一种预测分析.
背景情况:
- 呼吸应急综合征 (RDS) 影响过早出生的婴儿,原因是肺部发育不良和表面活性剂缺乏.
- RDS的发生率与早产程度相关.
- 目前的做法往往涉及预防性人工肺表面活性剂治疗早产婴儿,无论个体RDS的可能性.
研究的目的:
- 开发一种人工智能 (AI) 模型,用于预测早产婴儿的RDS.
- 通过准确识别具有RDS高风险的婴儿,减少不必要的表面活性剂处理.
- 为了提高对出生时体重非常低的婴儿的新生儿复苏准备.
主要方法:
- 利用了来自韩国新生儿网络76家医院的13087名出生时体重非常低的婴儿的数据.
- 纳入了各种数据点,包括婴儿特征,母亲病史,怀孕/分娩细节,家族病史,复苏程序和初始测试结果 (血液气体,阿普加尔得分).
- 对比了7个机器学习模型,提出了5层深度神经网络和5倍交叉验证进行增强预测的整体方法.
主要成果:
- 集体5层深度神经网络使用前20个特征,实现了高性能指标:83.03%的灵敏度,87.50%的特异性,84.07%的精度,85.26%的平衡精度和0.9187.7%的AUC.
- 基于经过验证的AI模型,开发了一个公共的网络应用程序,用于在早产婴儿中预测RDS的可访问性.
- 该模型显示了新生儿护理中临床决策支持的重大潜力.
结论:
- 开发的AI模型为预测早产婴儿的RDS概率提供了一个有价值的工具,特别是那些出生体重非常低的婴儿.
- 这种预测能力可以指导关于表面活性剂的管理决策,优化资源配置和患者护理.
- 人工智能模型支持加强新生儿复苏的准备,特别是高风险分娩.
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