1 citations · 1 across the 6 of their papers we have counts for
6 papers
Predicting Short-Term Mortality in Elderly ICU Patients with Diabetes and Heart Failure: A Distributional Inference Framework
Junyi Fan, Shuheng Chen, Li Sun +5
Elderly ICU patients with coexisting diabetes mellitus and heart failure experience markedly elevated short-term mortality, yet few predictive models are tailored to this high-risk…
Interpretable Machine Learning Model for Early Prediction of 30-Day Mortality in ICU Patients With Coexisting Hypertension and Atrial Fibrillation: A Retrospective Cohort Study
Shuheng Chen, Yong Si, Junyi Fan +5
Hypertension and atrial fibrillation (AF) often coexist in critically ill patients, significantly increasing mortality rates in the ICU. Early identification of high-risk individua…
Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia
Junyi Fan, Shuheng Chen, Li Sun +5
Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and bone marrow failure. ICU admission in these patients often signals critical compl…
Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability
Shuheng Chen, Yong Si, Junyi Fan +5
Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that imposes a significant burden on healthcare systems. ICU readmissions among AP pat…
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients
Yong Si, Junyi Fan, Li Sun +5
Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vu…
One Size Cannot Fit All: a Self-Adaptive Dispatcher for Skewed Hash Join in Shared-nothing RDBMSs
Jinxin Yang, Hui Li, Yiming Si +6
Shared-nothing architecture has been widely adopted in various commercial distributed RDBMSs. Thanks to the architecture, query can be processed in parallel and accelerated by scal…