Zengtian Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zengtian Chen has a total of 20 co-authors. Here are the top co-authors by number of shared publications:
- Bailin Niu
Department of Intensive Care Medicine, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
1 shared publication - Chongke Hu
Ningbo Institute of Innovation for Combined Medicine and Engineering (NIIME), The Affiliated Lihuili Hospital of Ningbo University, Ningbo, China
1 shared publication Fan Zhang
Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
1 shared publication- Fei Guo
Ningbo Institute of Innovation for Combined Medicine and Engineering (NIIME), The Affiliated Lihuili Hospital of Ningbo University, Ningbo, China
1 shared publication - Fen Liu
School of Medicine, Nanchang University, Nanchang, China
1 shared publication - Hong Zhang
Department of Laboratory Medicine, Chongqing Center for Clinical Laboratory Chongqing Academy of Medical Sciences Chongqing General Hospital School of Medicine Chongqing University Chongqing P. R. China
1 shared publication
Latest Publications
- Open Access
- 24 November 2025
- Published by Springer Nature in Nature Communications
- Vol. 16 (1) , 10396
Explainable AI unravels sepsis heterogeneity via coagulation-inflammation profiles for prognosis and stratification
Sepsis is a leading cause of hospital mortality, and its significant heterogeneity complicates prognosis and stratification. To address this challenge, we developed an explainable artificial intelligence prognostic model (SepsisFormer, a transformer-based neural network) and an automated risk-stratification tool (SMART) for sepsis. In a multi-center retrospective study of 12,408 sepsis patients, SepsisFormer achieved high predictive accuracy (AUC: 0.9301, sensitivity: 0.9346, and specificity: 0.8312). SMART (AUC: 0.7360) surpassed most established scoring systems. Seven coagulation-inflammatory routine laboratory measurements and patient age were identified to classify patients’ four risk levels (mild, moderate, severe, dangerous) and two subphenotypes (CIS1 and CIS2), each with distinct clinical characteristics and mortality rates. Notably, patients with moderate/severe levels or CIS2 derive more significant benefits from anticoagulant treatment. Our work, therefore, offers a set of simple, real-time executable tools for sepsis heterogeneity, demonstrating the potential to enhance sepsis clinical practice globally, particularly in resource-constrained healthcare settings.
- 24 November 2025
- Published by Springer Nature in Nature Communications
- Vol. 16 (1) , 10396
Open AccessExplainable AI unravels sepsis heterogeneity via coagulation-inflammation profiles for prognosis and stratification
Sepsis is a leading cause of hospital mortality, and its significant heterogeneity complicates prognosis and stratification. To address this challenge, we developed an explainable artificial intelligence prognostic model (SepsisFormer, a transformer-based neural network) and an automated risk-stratification tool (SMART) for sepsis. In a multi-center retrospective study of 12,408 sepsis patients, SepsisFormer achieved high predictive accuracy (AUC: 0.9301, sensitivity: 0.9346, and specificity: 0.8312). SMART (AUC: 0.7360) surpassed most established scoring systems. Seven coagulation-inflammatory routine laboratory measurements and patient age were identified to classify patients’ four risk levels (mild, moderate, severe, dangerous) and two subphenotypes (CIS1 and CIS2), each with distinct clinical characteristics and mortality rates. Notably, patients with moderate/severe levels or CIS2 derive more significant benefits from anticoagulant treatment. Our work, therefore, offers a set of simple, real-time executable tools for sepsis heterogeneity, demonstrating the potential to enhance sepsis clinical practice globally, particularly in resource-constrained healthcare settings.