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 …
[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 …
[HTML][HTML] Hypergraph transformers for ehr-based clinical predictions
Electronic health records (EHR) data contain rich information about patients' health
conditions including diagnosis, procedures, medications and etc., which have been widely …
conditions including diagnosis, procedures, medications and etc., which have been widely …
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 …
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 …
MEGACare: Knowledge-guided multi-view hypergraph predictive framework for healthcare
Predicting a patient's future health condition by analyzing their Electronic Health Records
(EHRs) is a trending subject in the intelligent medical field, which can help clinicians …
(EHRs) is a trending subject in the intelligent medical field, which can help clinicians …
Counterfactual and factual reasoning over hypergraphs for interpretable clinical predictions on ehr
Abstract Electronic Health Record modeling is crucial for digital medicine. However, existing
models ignore higher-order interactions among medical codes and their causal relations …
models ignore higher-order interactions among medical codes and their causal relations …
Knowledge guided diagnosis prediction via graph spatial-temporal network
Predicting the future health conditions of patients based on Electronic Health Records (EHR)
is an important research topic. Due to the temporal nature of EHR data, the major challenge …
is an important research topic. Due to the temporal nature of EHR data, the major challenge …
Heterogeneous graph construction and HinSAGE learning from electronic medical records
HN Cho, I Ahn, H Gwon, HJ Kang, Y Kim, H Seo… - Scientific Reports, 2022 - nature.com
Graph representation learning is a method for introducing how to effectively construct and
learn patient embeddings using electronic medical records. Adapting the integration will …
learn patient embeddings using electronic medical records. Adapting the integration will …