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探索在患有严重酸性病的患者中持续置换治疗的治疗效果,使用基于深度学习的因果推断
Min Woo Kang1, Sehoon Park2, Yong Chul Kim2
1Department of Internal Medicine, Korea University Guro Hospital, Seoul, Korea.
Scientific reports
|October 21, 2025
概括
持续置换疗法 (CKRT) 可以降低严重酸性结核病患者的死亡风险. 深度学习模型显示,CKRT可降低13%的死亡率,有助于选择性治疗决策.
科学领域:
- 腎病學和重症醫療醫學 腎病學和重症醫學
- 医疗保健中的人工智能
- 数据科学和机器学习
背景情况:
- 严重的代谢酸症是一种需要及时干预的危急状况.
- 持续置换疗法 (CKRT) 是一种至关重要的治疗方法,但由于潜在的并发症,需要仔细选择患者.
- 评估CKRT对严重酸化症死亡率的真正影响是具有挑战性的.
研究的目的:
- 研究CKRT对严重代谢酸症患者住院死亡率的治疗效果.
- 开发和验证基于深度学习的因果推理模型,用于预测CKRT的个性化治疗效果.
- 确定最能从CKRT中受益的患者,以优化其选择性应用.
主要方法:
- 利用密集护理III (MIMIC-III) 数据库的医疗信息中心,在ICU入院后48小时内识别严重酸性疾病 (pH<7.2) 的患者.
- 采用了用于推断个性化治疗效应 (GAINS) 的生成对抗网络深度学习模型进行因果推断.
- 随机将数据集分为85%用于培训和15%用于测试,使用准确度和AUC评估模型性能.
主要成果:
- 深度学习模型实现了高性能,在训练组中AUC为0.887,在测试组中为0.824.
- 虽然该模型最初预测了一些分析中由于CKRT导致的死亡概率略有增加,但最终显示了死亡风险的平均显著降低.
- 在训练和测试数据集中,CKRT应用与住院死亡风险平均降低13%有关.
结论:
- 基于深度学习的因果推断模型可以有效地评估CKRT在严重酸性病的个性化治疗效果.
- 对于患有严重代谢酸症的患者来说,CKRT显示出显著的死亡益处.
- 开发这样的预测模型可以加强临床决策,以便在重症监护机构选择性使用CKRT.
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