Continuous patient state attention model for addressing irregularity in electronic health records
Background Irregular time series (ITS) are common in healthcare as patient data is recorded
in an electronic health record (EHR) system as per clinical guidelines/requirements but not …
in an electronic health record (EHR) system as per clinical guidelines/requirements but not …
Long-term trend prediction of pandemic combining the compartmental and deep learning models
W Chen, H Luo, J Li, J Chi - Scientific reports, 2024 - nature.com
Predicting the spread trends of a pandemic is crucial, but long-term prediction remains
challenging due to complex relationships among disease spread stages and preventive …
challenging due to complex relationships among disease spread stages and preventive …
Oriented transformer for infectious disease case prediction
Accurate prediction of infectious disease cases plays a crucial role in achieving effective
infection prevention and control. However, the inherent variability of incubation periods and …
infection prevention and control. However, the inherent variability of incubation periods and …
Early prediction of atherosclerosis diagnosis with medical ambient intelligence
W Yang, Q Nie, Y Sun, D Zou, J Tang… - Frontiers in Physiology, 2023 - frontiersin.org
Atherosclerosis is a chronic vascular disease that poses a significant threat to human health.
Common diagnostic methods mainly rely on active screening, which often misses the …
Common diagnostic methods mainly rely on active screening, which often misses the …
Deep learning models for hepatitis E incidence prediction leveraging Baidu index
Y Guo, L Zhang, S Pang, X Cui, X Zhao, Y Feng - BMC Public Health, 2024 - Springer
Background Infectious diseases are major medical and social challenges of the 21 st
century. Accurately predicting incidence is of great significance for public health …
century. Accurately predicting incidence is of great significance for public health …
[HTML][HTML] Heterogeneous temporal representation for diabetic blood glucose prediction
Y Huang, Z Ni, Z Lu, X He, J Hu, B Li, H Ya… - Frontiers in …, 2023 - frontiersin.org
Background and aims: Blood glucose prediction (BGP) has increasingly been adopted for
personalized monitoring of blood glucose levels in diabetic patients, providing valuable …
personalized monitoring of blood glucose levels in diabetic patients, providing valuable …
An Epidemic Trend Prediction Model with Multi-source Auxiliary Data
B Wang, X He, H Lin, G Shen, X Kong - … Conference on Web and Big Data, 2024 - Springer
The global outbreak of epidemics profoundly affects public health and societal development.
The development of epidemic trend prediction models is crucial to prevent the recurrence of …
The development of epidemic trend prediction models is crucial to prevent the recurrence of …
Learning High-dimensional Associations for Nonalcoholic Fatty Liver Disease Diagnosis Prediction
Nonalcoholic Fatty Liver Disease (NAFLD) is a major liver disease worldwide, and NAFLD
diagnosis is the clinical foundation before healthcare strategies. NAFLD diagnosis predictive …
diagnosis is the clinical foundation before healthcare strategies. NAFLD diagnosis predictive …