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一个集成的优化和深度学习管道,用于预测IVF活产成功,使用特征优化和基于变压器的模型

Arezoo Borji¹, Hossam Haick², Birgit Pohn³

¹Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria; Department of Medicine, Danube Private University (DPU), Krems, Austria; Department of Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.

Computer methods and programs in biomedicine

|July 30, 2025

概括

这项研究开发了一种人工智能 (AI) 管道,以准确预测体外受精 (IVF) 治疗中活产的结果. 人工智能模型实现了97%的准确性,为个性化生育护理提供了潜力.

科学领域:

生殖医学生殖医学
医疗保健中的人工智能
生物医学数据科学生物医学数据科学

背景情况:

由于复杂的临床,人口和程序因素,预测体外受精 (IVF) 的成功具有挑战性.
准确预测活产结果对于优化辅助生殖技术治疗至关重要.

研究的目的:

开发一个高度准确的人工智能 (AI) 管道,用于预测试管婴儿中活产的结果.
提高用于生育治疗预测的AI模型的可解释性.

主要方法:

评估了各种特征选择方法 (PCA,PSO) 和机器学习分类器 (RF,决策树,变压器,Tab_transformer).
分析了混因素 (年龄,以前的周期) 和预处理技术.
为了模型的可解释性,使用了沙普利增量解释 (SHAP).

主要成果:

对特征选择的粒子优化 (PSO) 和Tab_transformer深度学习模型的组合实现了97%的准确性和98.4%的AUC.
SHAP分析确定了不孕不育的关键预测因素,并改善了模型的解释性.

结论:

开发了一个强大的AI管道,以高准确性和可解释性预测试管婴儿活产的结果.
人工智能管道显示了增强个性化生育治疗和改善患者护理的潜力.

关键词:

功能选择功能选择在体外受精 (IVF)生产成功生产成功粒子群集优化 (pso) 的方法标签_变压器变压器基于变压器的模型

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