Learning the graphical structure of electronic health records with graph convolutional transformer
Effective modeling of electronic health records (EHR) is rapidly becoming an important topic
in both academia and industry. A recent study showed that using the graphical structure …
in both academia and industry. A recent study showed that using the graphical structure …
Multimodal risk prediction with physiological signals, medical images and clinical notes
The broad adoption of electronic health record (EHR) systems brings us a tremendous
amount of clinical data and thus provides opportunities to conduct data-based healthcare …
amount of clinical data and thus provides opportunities to conduct data-based healthcare …
Multi-Gradient Siamese Temporal Model for the Prediction of Clinical Events in Rapid Response Systems
TN Nguyen, SH Kim, BG Kho… - IEEE Intelligent …, 2024 - ieeexplore.ieee.org
Early identification of emergency situations for intensive care unit (ICU) patients is a critical
component of precision medicine. We present a Multi-gradient Siamese Temporal (MG …
component of precision medicine. We present a Multi-gradient Siamese Temporal (MG …
Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks
Predicting the future health information of patients from the historical Electronic Health
Records (EHR) is a core research task in the development of personalized healthcare …
Records (EHR) is a core research task in the development of personalized healthcare …
Graph neural network-based diagnosis prediction
Diagnosis prediction is an important predictive task in health care that aims to predict the
patient future diagnosis based on their historical medical records. A crucial requirement for …
patient future diagnosis based on their historical medical records. A crucial requirement for …
A dual-channel deep learning approach to continuous prediction of acute kidney injury in the intensive care unit
X Kong, P Zhang, Y Ling, D Lv, J Xu… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Acute Kidney Injury (AKI) often occurs in the intensive care units (ICU), where it is associated
with high morbidity and mortality. Early and continuous prediction of AKI plays a crucial role …
with high morbidity and mortality. Early and continuous prediction of AKI plays a crucial role …
TRIDE: A Temporal, Robust, and Informative Data Augmentation Framework for Disease Progression Modeling
Modeling the progression of a target disease using electronic health records (EHRs),
especially early predicting the onset of a disease, is critical for timely and accurate clinical …
especially early predicting the onset of a disease, is critical for timely and accurate clinical …
[HTML][HTML] A deep attention model to forecast the Length Of Stay and the in-hospital mortality right on admission from ICD codes and demographic data
Abstract Leveraging the Electronic Health Records (EHR) longitudinal data to produce
actionable clinical insights has always been a critical issue for recent studies. Non …
actionable clinical insights has always been a critical issue for recent studies. Non …
Multi Head Graph Attention for Drug Response Predicton
PS Rajendran, M Sivannarayna - 2023 3rd International …, 2023 - ieeexplore.ieee.org
Precision medicine is based on curing diseases based on a patient's genetic profile,
lifestyle, and environmental factors. This method improves clinical trial success rates and …
lifestyle, and environmental factors. This method improves clinical trial success rates and …
MAIN: multimodal attention-based fusion networks for diagnosis prediction
Y An, H Zhang, Y Sheng, J Wang… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Predicting the future diagnoses from patients' historical Electronic Health Records (EHR) is a
significant task in healthcare. EHR consist of multiple modal data, each modality has …
significant task in healthcare. EHR consist of multiple modal data, each modality has …