activity
20192021
most citedBEHRT: Transformer for Electronic Health Records

16 citations · 21 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2021★ 4 cited

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…

cs.LG2020

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…

cs.LG2019★ 16 cited

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…

stat.ML2019

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…

cs.LG2019★ 1 cited

Performance Measurement for Deep Bayesian Neural Network

Yikuan Li, Yajie Zhu

Deep Bayesian neural network has aroused a great attention in recent years since it combines the benefits of deep neural network and probability theory. Because of this, the networ…