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人工智能和机器学习辅助的亚表型化用于症个性化免疫疗法.
Evdoxia Kyriazopoulou1, Eleni Karakike2, Pavlos Myrianthefs2
12nd Department of Critical Care Medicine, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.
Journal of personalized medicine
|January 27, 2026
概括
人工智能 (AI) 和机器学习 (ML) 识别了败血症亚型,揭示了对免疫疗法的不同反应. 需要进一步的研究来验证这些AI衍生的败血症分类,以改善患者的治疗结果.
科学领域:
- 关键护理医学 关键护理医学
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 败血症异质性阻碍了免疫治疗的进展.
- 人工智能 (AI) 和机器学习 (ML) 为分析复杂的败血症数据提供了新的方法.
- 了解败血症亚表型对于有针对性的治疗策略至关重要.
研究的目的:
- 审查人工智能衍生性败血症亚型的现有证据.
- 探索基于败血症亚型的免疫调节疗法的治疗反应.
- 讨论人工智能在败血症研究中的影响.
主要方法:
- 在MEDLINE上进行了结构化的文献搜索.
- 进行了有关出版物的叙事审查.
- 审查的重点是利用临床,生物和omics数据用于败血症亚型的研究.
主要成果:
- 确定了用于败血症亚型的多个AI/ML算法.
- 主要分类区分高和低炎症亚表型.
- 类他类药物,皮质类固醇,活性蛋白C和血栓模块素在败血症亚型中表现出差异性影响.
结论:
- 对人工智能衍生性败血症亚型的前性验证至关重要.
- 人工智能驱动的亚型化有可能在败血症中进行个性化免疫疗法.
- 解决有效性,公平性,伦理和可行性对于临床实施至关重要.


