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Updated: May 2, 2026

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深度学习模型用于预测异常性颗粒性乳头炎的复发

Lanying Li1,2, Wen Yang3, Haiming Jia1

  • 1Vascular Surgery Breast Surgery Department, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, 621000, People's Republic of China.

Journal of inflammation research
|March 3, 2025
PubMed
概括

与其他机器学习模型相比,神经网络在预测异常性粒状乳腺炎 (IGM) 复发方面表现优越. 这一发现对于早期发现和改善患者对这种罕见的乳腺疾病的治疗至关重要.

关键词:
有颗粒状乳腺炎的乳腺炎.机器学习模型机器学习模型神经网络的神经网络的神经网络复发预测的复发预测血清学标志物 血清学标志物

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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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