16 citations · 52 across the 10 of their papers we have counts for
4 papers · 1 filter
Transfer Learning in Electronic Health Records through Clinical Concept Embedding
Jose Roberto Ayala Solares, Yajie Zhu, Abdelaali Hassaine +6
Deep learning models have shown tremendous potential in learning representations, which are able to capture some key properties of the data. This makes them great candidates for tr…
Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records
Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi +5
Electronic health records represent a holistic overview of patients' trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk pre…
Risk factor identification for incident heart failure using neural network distillation and variable selection
Yikuan Li, Shishir Rao, Mohammad Mamouei +5
Recent evidence shows that deep learning models trained on electronic health records from millions of patients can deliver substantially more accurate predictions of risk compared…
An explainable Transformer-based deep learning model for the prediction of incident heart failure
Shishir Rao, Yikuan Li, Rema Ramakrishnan +6
Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction b…