[HTML][HTML] Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare
The digitization of healthcare systems has led to the proliferation of electronic health records
(EHRs), serving as comprehensive repositories of patient information. However, the vast …
(EHRs), serving as comprehensive repositories of patient information. However, the vast …
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 …
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 …
Multi-gate Mixture of Multi-view Graph Contrastive Learning on Electronic Health Record
Y Cao, Q Wang, X Wang, D Peng… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Electronic Health Record (EHR) is the digital form of patient visits containing various medical
data, including diagnosis, treatment, and lab events. Representation learning of EHR with …
data, including diagnosis, treatment, and lab events. Representation learning of EHR with …
Collaborative graph learning with auxiliary text for temporal event prediction in healthcare
Accurate and explainable health event predictions are becoming crucial for healthcare
providers to develop care plans for patients. The availability of electronic health records …
providers to develop care plans for patients. The availability of electronic health records …
HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks
FL Piya, M Gupta, R Beheshti - arXiv preprint arXiv:2403.18128, 2024 - arxiv.org
While electronic health records (EHRs) are widely used across various applications in
healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or …
healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or …
Multimodal fusion of ehr in structures and semantics: Integrating clinical records and notes with hypergraph and llm
Electronic Health Records (EHRs) have become increasingly popular to support clinical
decision-making and healthcare in recent decades. EHRs usually contain heterogeneous …
decision-making and healthcare in recent decades. EHRs usually contain heterogeneous …
Graph Representation Learning For Stroke Recurrence Prediction
Stroke is one of the leading causes of death worldwide, and its mortality rate is drastically
higher for patients who suffer recurrent strokes. Motivated by the recent success of graph …
higher for patients who suffer recurrent strokes. Motivated by the recent success of graph …
Predicting clinical events via graph neural networks
T Kanchinadam, S Gauher - 2022 21st IEEE International …, 2022 - ieeexplore.ieee.org
Timely detection of clinical events would provide healthcare providers the opportunity to
make meaningful interventions that can result in improved health outcomes. This work …
make meaningful interventions that can result in improved health outcomes. This work …
Clinical note owns its hierarchy: multi-level hypergraph neural networks for patient-level representation learning
Leveraging knowledge from electronic health records (EHRs) to predict a patient's condition
is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs …
is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs …