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用于预防和控制感染的可解释AI:模拟爱尔兰一家用变压器医院的CPE获取和患者结果

Minh-Khoi Pham1,2, Tai Tan Mai3,4, Martin Crane3,4

  • 1School of Computing, Dublin City University, Dublin, Ireland. minhkhoi.pham@adaptcentre.ie.

BMC medical informatics and decision making
|October 22, 2025
PubMed
概括

使用像TabTransformer这样的深度学习模型的可解释的AI框架有效地预测了Carbapenemase-Producing Enterobacteriace (CPE) 的获取和患者的结果. 关键的风险因素包括医院暴露和网络特征,有助于感染控制.

关键词:
深度学习是一种深度学习.电子医疗记录电子医疗记录可解释的人工智能变压器 变压器 变压器

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