A5040761440- Sepsis Diagnosis and Treatment
- Machine Learning in Healthcare
- Cardiac Arrest and Resuscitation
- Acute Kidney Injury Research
- Phonocardiography and Auscultation Techniques
- Autopsy Techniques and Outcomes
- Trauma and Emergency Care Studies
- Statistical Methods in Epidemiology
- Artificial Intelligence in Healthcare
Shenzhen University
2023-2024
Shenzhen University Health Science Center
2022-2023
Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in function within few hours or days, leading to failure damage. Although AKI associated with poor outcomes, current guidelines overlook heterogeneity among patients this condition. Identification of subphenotypes could enable targeted interventions and deeper understanding injury's pathophysiology. While previous approaches based unsupervised representation learning have...
Acute kidney injury (AKI) is associated with increased mortality in critically ill patients. Due to differences the etiology and pathophysiological mechanism, current AKI criteria put it an embarrassment evaluate clinical therapy prognosis. We aimed identify subphenotypes based on routinely collected data expose unique pathophysiologic patterns. A retrospective study was conducted Medical Information Mart for Intensive Care IV (MIMIC-IV) eICU Collaborative Research Database (eICU-CRD), a...
Background: Acute kidney injury (AKI) is associated with increasing mortality in critically ill patients. Due to differences the etiology and pathophysiological mechanism, current AKI criteria put it an embarrassment evaluate clinical therapy prognosis. We aimed identify subphenotypes based on routinely collected data expose unique pathophysiologic patterns.Methods:A retrospective study was conducted Medical Information Mart for Intensive Care IV (MIMIC-IV) eICU Collaborative Research...
Despite the abundance of subphenotype clustering studies on sepsis and acute kidney injury (AKI), few models consider real-time information clinical features. The lack supervision may lead to patient subgroups being derived as clusters without stratification patients based outcome interests. sensitivity dimension in methods is generally ignored, so robustness. In this study, we propose an ensembled outcome-driven bidirectional long short-term memory autoencoder (BiLSTM-AE) architecture with...