Hitanet: Hierarchical time-aware attention networks for risk prediction on electronic health records
Deep learning methods especially recurrent neural network based models have
demonstrated early success in disease risk prediction on longitudinal patient data. Existing …
demonstrated early success in disease risk prediction on longitudinal patient data. Existing …
Federated learning for privacy-preserving open innovation future on digital health
Privacy protection is an ethical issue with broad concern in artificial intelligence (AI).
Federated learning is a new machine learning paradigm to learn a shared model across …
Federated learning is a new machine learning paradigm to learn a shared model across …
Medpath: Augmenting health risk prediction via medical knowledge paths
The broad adoption of electronic health records (EHR) data and the availability of
biomedical knowledge graphs (KGs) on the web have provided clinicians and researchers …
biomedical knowledge graphs (KGs) on the web have provided clinicians and researchers …
Lsan: Modeling long-term dependencies and short-term correlations with hierarchical attention for risk prediction
Risk prediction using electronic health records (EHR) is a challenging data mining task due
to the two-level hierarchical structure of EHR data. EHR data consist of a set of time-ordered …
to the two-level hierarchical structure of EHR data. EHR data consist of a set of time-ordered …
Recent advancements and applications of deep learning in heart failure: Α systematic review
G Petmezas, VE Papageorgiou, V Vassilikos… - Computers in Biology …, 2024 - Elsevier
Background Heart failure (HF), a global health challenge, requires innovative diagnostic and
management approaches. The rapid evolution of deep learning (DL) in healthcare …
management approaches. The rapid evolution of deep learning (DL) in healthcare …
Multimodal data matters: language model pre-training over structured and unstructured electronic health records
As two important textual modalities in electronic health records (EHR), both structured data
(clinical codes) and unstructured data (clinical narratives) have recently been increasingly …
(clinical codes) and unstructured data (clinical narratives) have recently been increasingly …
Learning with small data
In the era of big data, it is easy for us collect a huge number of image and text data.
However, we frequently face the real-world problems with only small (labeled) data in some …
However, we frequently face the real-world problems with only small (labeled) data in some …
[HTML][HTML] Federated learning of medical concepts embedding using behrt
OB Shoham, N Rappoport - JAMIA open, 2024 - pmc.ncbi.nlm.nih.gov
Objectives Electronic health record data is often considered sensitive medical information.
Therefore, the EHR data from different medical centers often cannot be shared, making it …
Therefore, the EHR data from different medical centers often cannot be shared, making it …
Leveraging graph-based hierarchical medical entity embedding for healthcare applications
Automatic representation learning of key entities in electronic health record (EHR) data is a
critical step for healthcare data mining that turns heterogeneous medical records into …
critical step for healthcare data mining that turns heterogeneous medical records into …
SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation
Effectively medication recommendation with complex multimorbidity conditions is a critical
yet challenging task in healthcare. Most existing works predicted medications based on …
yet challenging task in healthcare. Most existing works predicted medications based on …