Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records

1 code implementation • 7 Feb 2022 • Shishir Rao, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Yikuan Li, Rema Ramakrishnan, Abdelaali Hassaine, Dexter Canoy, Kazem Rahimi

The rise of "doubly robust" non-parametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data, offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHR).

Causal Inference Decision Making

An explainable Transformer-based deep learning model for the prediction of incident heart failure

no code implementations • 27 Jan 2021 • Shishir Rao, Yikuan Li, Rema Ramakrishnan, Abdelaali Hassaine, Dexter Canoy, John Cleland, Thomas Lukasiewicz, Gholamreza Salimi-Khorshidi, Kazem Rahimi

Predicting the incidence of complex chronic conditions such as heart failure is challenging.

Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records

no code implementations • 23 Mar 2020 • Yikuan Li, Shishir Rao, Abdelaali Hassaine, Rema Ramakrishnan, Yajie Zhu, Dexter Canoy, Gholamreza Salimi-Khorshidi, Thomas Lukasiewicz, Kazem Rahimi

In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for more comprehensive uncertainty estimation.

Decision Making Gaussian Processes