A5077746458- Sepsis Diagnosis and Treatment
- Machine Learning in Healthcare
- Acute Kidney Injury Research
- Cardiac Arrest and Resuscitation
- Artificial Intelligence in Healthcare
- Genomics and Rare Diseases
- Trauma and Emergency Care Studies
- Statistical Methods in Epidemiology
- Phonocardiography and Auscultation Techniques
- RNA and protein synthesis mechanisms
- Autopsy Techniques and Outcomes
- Heart Rate Variability and Autonomic Control
- Cancer Genomics and Diagnostics
- Hydrological Forecasting Using AI
- Hemodynamic Monitoring and Therapy
Shenzhen University
2022-2024
Shenzhen University Health Science Center
2022-2023
Somatic driver mutations play important roles in cancer and must be precisely identified to advance our understanding of tumorigenesis its promotion progression. However, identifying somatic remains challenging Homo sapiens genomics due the random nature high cost qualitative experiments. Building on powerful sequence interpretation capabilities language models, we propose a self-attention-based contextualized pretrained model for mutation identification. We with reference genome equip it...
The mortality rate in the intensive care unit (ICU) is a key metric of hospital clinical quality. To enhance performance, many methods have been proposed for stratification patients' different risk categories, such as severity scoring systems and machine learning models. However, these make capturing time sequence information difficult, posing challenges to continuous assessment patient's during their stay. Therefore, we built predictive model that can predictions throughout stay obtain...
This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying machine learning algorithm.Adult who were diagnosed during admission ICU extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used development internal validation. The other three databases external Our proposed developed based on Extreme Gradient Boosting (XGBoost) algorithm. generalizability, discrimination, validation our...
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...
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...
Septic patients admitted to the intensive care unit (ICU) are highly susceptible acute kidney injury (AKI), which leads reduced survival in these patients. It is thus necessary develop a model that can predict risk of AKI septic real time. Although continuous or near-continuous assessment likely necessary, few models have been designed for this purpose. Therefore, we constructed continuously sepsis-induced ICU. Our proposed optimally achieved an area under receiver operating characteristic...
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...