预测性生存模型对与艾滋病毒相关的密码球菌病:比较机器学习方法
Xuemin Fu1, Luling Wu2,3, Jingna Xun2
1Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
Frontiers in cellular and infection microbiology
|May 19, 2025
概括
机器学习模型可以通过识别免疫表型和使用处罚生存模型来预测与艾滋病毒相关的加密菌病的存活率. 这有助于个性化风险评估和改善患者管理,以获得更好的结果.
科学领域:
- 传染性疾病 传染性疾病
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 与艾滋病毒相关的加密球菌病在全球呈现出不可预测的疾病过程和高死亡率.
- 目前这种疾病的风险分层和临床管理策略有限,需要改进方法.
研究的目的:
- 开发和验证机器学习模型,用于预测与艾滋病毒相关的加密球菌病的疾病严重程度和生存结果.
- 识别与疾病严重程度和预后相关的免疫表型.
主要方法:
- 对98例与艾滋病毒相关的密码球菌病例的临床和免疫学数据的分析.
- 应用无监督聚类,弹性网调整的考克斯回归和随机生存森林.
- 严格的模型性能评估使用C指数,布赖尔得分和时间依赖的AUC与嵌套交叉验证.
主要成果:
- 鉴定一种具有过度炎症 (EXC) 的免疫表型,与增加疾病严重程度,神经症状和较差的生存率有关.
- 一个弹性净调整的考克斯回归模型在36个月的结果中实现了平均C指数0.78和平均布里尔得分0.13.
- 12个月后的0.84和36个月后的0.79的时间依赖的AUC值证明了该模型的预测稳定性.
结论:
- 基于细胞因子的聚类为与艾滋病毒相关的密码球菌病提供了细微的严重程度分层.
- 处罚生存模型增强了个性化的风险评估,支持量身定制的临床决策.
- 机器学习应用程序有望改善这种疾病的生存预测和个性化患者管理.
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