基于深度森林的产前胎儿监护不平衡多分类判别 |
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引用本文:郭傲,陈妍荻,魏航,等.基于深度森林的产前胎儿监护不平衡多分类判别[J].医学信息学杂志,2021,42(3):43-49 |
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基金项目:国家自然科学基金资助项目“高阶网络模体聚类算法与应用研究”(项目编号:61976052);广东省医学科研基金资助项目“基于不平衡CTG数据的产前智能胎儿监护评价模型的研究”(项目编号:A2019428)。 |
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中文摘要:采用深度森林框架构建基于不平衡电子胎心宫缩监护数据的多分类判别模型,验证模型有效性,结果表明该模型预测性能较好,极大降低误判率,在产前胎儿健康状况智能评估中有良好应用前景。 |
中文关键词:产前胎儿监护 不平衡多分类 深度森林 胎心宫缩监护 |
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Imbalanced Multi-classification Discrimination for Antenatal Fetal Monitoring Based on Deep Forest |
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Abstract:The multi-classification discrimination model based on unbalanced Cardiotocography (CTG) data is built by Deep Forest (DF) framework. The validity of the model is verified, and the results show that the prediction performance of the model is good, the misjudgment rate is greatly reduced, and the model has a good application prospect in the intelligent assessment of antenatal fetal health status. |
keywords:antenatal fetal monitoring imbalanced multi-classification Deep Forest (DF) Cardiotocography (CTG) |
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