Rapid Response System Based On Graph Attention Network For Forecasting Clinical Decline In EHR
B Sushma, SD Sree, MS Yadav - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Integrating electronic health records (EHR) with advanced predictive systems has
revolutionized healthcare by enabling early identification and intervention for patients at risk …
revolutionized healthcare by enabling early identification and intervention for patients at risk …
Rapid Response System Based on Graph Attention Network for Predicting In-Hospital Clinical Deterioration
TC Do, HJ Yang, GS Lee, SH Kim, BG Kho - IEEE Access, 2023 - ieeexplore.ieee.org
In-hospital clinical deterioration is a major worldwide healthcare burden in the intensive care
units (ICUs), as it requires rapid intervention. Rapid response systems (RRSs) are widely …
units (ICUs), as it requires rapid intervention. Rapid response systems (RRSs) are widely …
Temporal Graph Attention Model for Enhanced Clinical Risk Prediction
R Bharath, P Sriram - 2024 IEEE International Students' …, 2024 - ieeexplore.ieee.org
Electronic health record (EHR)-based clinical risk prediction can help clinicians make better
decisions and understand early diagnosis. Nevertheless, accurate representations derived …
decisions and understand early diagnosis. Nevertheless, accurate representations derived …
DHGL: Dynamic hypergraph‐based deep learning model for disease prediction
Electronic health record (EHR) data is crucial in providing comprehensive historical disease
information for patients and is frequently utilized in health event prediction. However, current …
information for patients and is frequently utilized in health event prediction. However, current …
Time-aware context-gated graph attention network for clinical risk prediction
Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in
better judgment and can make sense of early diagnosis. However, the prediction …
better judgment and can make sense of early diagnosis. However, the prediction …
Self-supervised graph learning with hyperbolic embedding for temporal health event prediction
Electronic health records (EHRs) have been heavily used in modern healthcare systems for
recording patients' admission information to health facilities. Many data-driven approaches …
recording patients' admission information to health facilities. Many data-driven approaches …
Predictive Modeling with Temporal Graphical Representation on Electronic Health Records
Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are
receiving increasing attention in healthcare. An effective representation of a patient's EHR …
receiving increasing attention in healthcare. An effective representation of a patient's EHR …
Generalizable Model Design for Clinical Event Prediction using Graph Neural Networks
While many machine learning and deep learning-based models for clinical event prediction
leverage various data elements from electronic healthcare records such as patient …
leverage various data elements from electronic healthcare records such as patient …
Enhancing Diagnosis Prediction in Healthcare with Knowledge-based Recurrent Neural Networks
H Shen - IEEE Access, 2023 - ieeexplore.ieee.org
The objective of diagnosis prediction involves foreseeing the potential diseases/conditions
according to analyzing patients' historical Electronic Health Records (EHRs). The primary …
according to analyzing patients' historical Electronic Health Records (EHRs). The primary …
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction
S Li, H Cheng, R Li, W Li - arXiv preprint arXiv:2404.14815, 2024 - arxiv.org
The widespread application of Electronic Health Records (EHR) data in the medical field
has led to early successes in disease risk prediction using deep learning methods. These …
has led to early successes in disease risk prediction using deep learning methods. These …