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将深度学习纳入公共卫生:PICC-RVT风险评估的新方法

Yue Li1,2, Shengxiao Nie3, Lei Wang3

  • 1Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Frontiers in public health
|January 22, 2025
PubMed
概括

深度学习模型使用时间序列数据准确预测外围插入的中央导管相关静脉血栓塞 (PICC-RVT) 风险. 专水平,预填密封剂和aPTT是管理高风险患者的关键预测指标.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.周围插入的中央导管.血栓形成的原因是血栓形成.时间到事件的时间.

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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