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利用机器学习来预测分娩妇女上部切除术的风险因素

Mojdeh Banaei¹, Nasibeh Roozbeh¹, Fatemeh Darsareh¹

¹Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran (all authors).

AJOG global reports

|December 25, 2024

概括

机器学习模型确定了表皮切割的关键风险因素,包括平价,分娩开始,妊娠年龄,BMI和doula支持,改善了这种常见的分娩程序的预测准确性.

科学领域:

产科和妇科产科和妇科
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

外科切口 (Episiotomy) 是一种手术切口,具有特殊的适应性,可以预防严重的母体伤和内不等并发症.
关于与表皮切割相关的确切风险因素的辩论仍在继续,需要先进的分析方法.

研究的目的:

通过机器学习模型,使用产妇内数据预测刺骨切除术的风险因素.
为了确定关键的临床变量与需要在阴道分娩时进行表皮切除.

主要方法:

一项回顾性队列研究分析了2022年1月至2023年1月期间1775例阴道分娩的产后数据.
采用了八种机器学习模型,根据特征工程的P值选择了显著变量.
模型性能使用诸如曲线下的面积 (AUC),准确性,精度,回忆和F1-Score.等指标进行评估.

主要成果:

在1775例阴道分娩中,有629例 (35.4%) 经历了表皮切除术.
线性回归证明了最好的诊断性能,AUC为0.85.
刺骨切除术的关键预测因素包括平价,分娩开始,妊娠年龄,体重指数和杜拉支持.

结论:

机器学习模型在使用临床数据预测表皮切除术风险因素方面表现有前途.
进一步的研究,包括产后临床数据和分娩护理人员的观点,可以提高预测准确度.

关键词:

人工智能的人工智能是人工智能.这是一种Episiotomy.机器学习是机器学习.

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