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使用多变量多分类模型探索影响婴儿输血次数的婴儿特征:一项回顾性研究

Mengyi Zhang^1,2, Jian Chen^1,2, Jing Feng^1,2

¹Department of Laboratory Medicine, West China Second University Hospital, Sichuan University, Chengdu, China.

Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie

|May 7, 2025

概括

预测新生儿输血是非常重要的. 这项研究开发了使用围产期因素的模型,如阿普加得分,以预测婴儿输血需求,帮助早期管理.

科学领域:

新生儿护理新生儿护理
医疗信息学医学信息学
预测建模的预测建模.

背景情况:

新生儿输血是必不可少的,但受到复杂的围产因素的影响.
了解这些因素对于及时的婴儿护理和管理至关重要.

研究的目的:

利用围产期数据开发婴儿输血的预测模型.
确定影响新生儿输血需求的关键因素.

主要方法:

从婴儿的电子医疗记录收集的数据.
根据接收的输血数量对婴儿进行分类.
构建和评估多变量,随机森林和梯度增强树木模型,以预测输血需求.

主要成果:

低体重状况,早产,阿帕尔分数和妊娠糖尿病严重影响了输血需求.
随机森林模型显示出强大的预测性能 (在训练集上AUC高达0.8037).
1,5,10分钟的Apgar分数显示出作为初步预测因素的潜力.

结论:

阿普加尔评分可以为新生儿早期输血策略提供信息.
由于数据限制,需要进一步优化模型以准确地预测婴儿的输血情况.

关键词:

机器学习机器学习多重分类模型的多重分类模型.新生儿输血新生儿输血围产期因子围产期因子

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