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使用变压器与跳过连接令牌用于表格数据的败血症患者的预后预测
Jee-Woo Choi1, Minuk Yang1, Jae-Woo Kim2
1Mediv Corporation, Cheongju-si, Chungcheongbuk-do, Republic of Korea; Chungbuk National University College of Medicine, Cheongju-si, Chungcheongbuk-do, Republic of Korea.
Artificial intelligence in medicine
|March 10, 2024
概括
一种新型的深度学习模型有效地预测了急症室的败血症患者的结果,包括死亡率和停留时间. 这种人工智能方法增强了严重败血症和败血症休克的临床管理,改善了患者的护理.
科学领域:
- * 计算生物学和医学
- * 医疗保健中的人工智能
背景情况:
- * 败血症是重症监护病房 (ICU) 的关键病症,是全球死亡的主要原因.
- *严重的败血症和败血性休克需要有效的临床管理策略.
研究的目的:
- * 开发一种深度学习模型,用于预测毒症患者的死亡率,ICU停留时间 (>14天) 和住院时间 (>30天) 的时间.
- * 支持重症监护室 (ICU) 的临床医生,以更好地管理败血症患者.
主要方法:
- * 开发一种经过修改的变压器架构,使用跳过连接的令牌来整合从表格数据中的本地和全球信息.
- *对591名患者病历进行了回顾性分析,其中包括16个与顺序性器官衰竭评估 (SOFA) 评分相关的特征.
- * 拟议模型与传统的机器学习和深度学习模型进行比较.
主要成果:
- * 拟议的深度学习模型的表现优于ElasticNet,XGBoost,随机森林,MLP,变压器和FT-Transformer.
- *实现了高绩效指标:AUROC为0.8047的死亡率,为0.8314的ICU停留时间,为0.7342的住院时间.
结论:
- *新型基于变压器的深度学习模型显示,使用表格数据预测ICU患者的临床终点具有显著的前景.
- *这种方法提供了一个有价值的工具,用于管理血症和潜在的其他条件使用电子健康记录.
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