9 citations · 17 across the 4 of their papers we have counts for
5 papers
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Siddharth Biswal, Soumya Ghosh, Jon Duke +3
Researchers require timely access to real-world longitudinal electronic health records (EHR) to develop, test, validate, and implement machine learning solutions that improve the q…
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
Ardavan Afshar, Ioakeim Perros, Haesun Park +5
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of p…
Causal Regularization
Dominik Janzing
I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample r…
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Edward Choi, Cao Xiao, Walter F. Stewart +1
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require traini…
Causal Regularization
Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi +3
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to st…