16 citations · 51 across the 9 of their papers we have counts for
9 papers
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning
Yikuan Li, Mohammad Mamouei, Shishir Rao +5
Most machine learning (ML) models are developed for prediction only; offering no option for causal interpretation of their predictions or parameters/properties. This can hamper the…
Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records
Shishir Rao, Mohammad Mamouei, Gholamreza Salimi-Khorshidi +5
Observational causal inference is useful for decision making in medicine when randomized clinical trials (RCT) are infeasible or non generalizable. However, traditional approaches…
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…
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records
Yikuan Li, Shishir Rao, Abdelaali Hassaine +6
One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions. Currently, deep Bayes…
BEHRT: Transformer for Electronic Health Records
Yikuan Li, Shishir Rao, Jose Roberto Ayala Solares +5
Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs…
Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation
Abdelaali Hassaine, Dexter Canoy, Jose Roberto Ayala Solares +6
Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population, both in absolute and relative terms. However, multimorb…