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T

Tae Gun Shin

Samsung Medical Center
H指数:30
被引数:3,652

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Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients’ Historical Data

基于患者历史数据的急诊分诊中用于预测发热患者败血症和脓毒性休克的可解释机器学习模型

2026-07-210
OAAI
Early vasopressin administration in septic shock: A systematic review, meta-analysis, and multicenter retrospective observational study

早期血管加压素在脓毒性休克中的应用:系统评价、Meta分析及多中心回顾性观察研究

2026-05-080
PRE
Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling

基于机器学习和回归建模混合方法预测难治性脓毒症休克风险的量表

2024-12-192
PRE
Sex differences in in-hospital management in patients with sepsis and septic shock: a prospective multicenter observational study

脓毒症和脓毒症休克患者住院治疗的性别差异: 一项前瞻性多中心观察性研究

2024-02-280
OAAI
Modified Cardiovascular Sequential Organ Failure Assessment Score in Sepsis: External Validation in Intensive Care Unit Patients

改良的脓毒症心血管序贯器官衰竭评估评分: 重症监护病房患者的外部验证

2023-01-013
OAAI
Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic Shock Visiting the Emergency Department: A Multi-Center, Prospective Cohort Study

机器学习模型的开发和验证,用于预测败血症休克的4期实体癌患者急诊预后: 一项多中心,前瞻性队列研究

2022-12-054
OAAI
Impact of COVID-19 Pandemic on Management and Outcomes in Patients with Septic Shock in the Emergency Department

新型冠状病毒肺炎大流行对急诊科脓毒症休克患者管理和结局的影响

2022-11-010
OAAI