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机器学习用于预测可选生育保护结果.

Itai Braude1, Einat Haikin Herzberger1,2, Mor Semo1

  • 1Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel.

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概括

机器学习准确地预测了生育保护结果. 使用治疗前和治疗后数据的模型预测卵细胞产量,帮助寻求生育保护的妇女规划治疗.

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科学领域:

  • 生殖医学 生殖医学
  • 医疗保健中的人工智能
  • 数据科学在临床应用中的应用

背景情况:

  • 选择性生育保护对于面临影响生育能力的医疗治疗的妇女至关重要.
  • 预测治疗结果,特别是卵细胞产量,对于优化生育保护协议至关重要.
  • 目前的预测方法可能缺乏个性化治疗策略所需的精度.

研究的目的:

  • 评估机器学习模型在接受生育保护的妇女中预测卵细胞产量的有效性.
  • 确定影响生育保护结果的治疗前和治疗后关键参数.
  • 将不同机器学习算法的预测准确度与统计回归进行比较.

主要方法:

  • 对250名接受选择性生育保护 (2019-2022) 的妇女进行了回顾性分析.
  • 机器学习模型 (随机森林分类器,XGBoost分类器) 和统计回归的应用.
  • 使用治疗前和治疗后的数据预测卵细胞计数 (OC) 类 (低,中,高).

主要成果:

  • 随机森林分类器实现了最高的准确性,处理后的AUC为87%,处理前的AUC为77%.
  • XGBoost分类器显示了可比性能,治疗后AUC为86%,治疗前AUC为74%.
  • 关键预测因素包括基底FSH,基底LH, antral毛囊数 (AFC) 和触发日的雌激素水平.

结论:

  • 机器学习模型在预测生育保护治疗结果方面表现出高准确度.
  • 这些模型可以有效地利用临床数据来预测卵细胞检索数量.
  • 这些发现支持将人工智能驱动的预测工具整合到生育保护咨询和管理中.