Search Results for author: Gholamreza Salimi-Khorshidi

Found 11 papers, 2 papers with code

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

1 code implementation7 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

Transfer Learning in Electronic Health Records through Clinical Concept Embedding

no code implementations27 Jul 2021 Jose Roberto Ayala Solares, Yajie Zhu, Abdelaali Hassaine, Shishir Rao, Yikuan Li, Mohammad Mamouei, Dexter Canoy, Kazem Rahimi, Gholamreza Salimi-Khorshidi

In this study, we aim to (1) train some of the most prominent disease embedding techniques on a comprehensive EHR data from 3. 1 million patients, (2) employ qualitative and quantitative evaluation techniques to assess these embeddings, and (3) provide pre-trained disease embeddings for transfer learning.

Transfer Learning

Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records

no code implementations21 Jun 2021 Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, Kazem Rahimi

Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the existing deep learning architectures.

Risk factor identification for incident heart failure using neural network distillation and variable selection

no code implementations17 Feb 2021 Yikuan Li, Shishir Rao, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Dexter Canoy, Abdelaali Hassaine, Thomas Lukasiewicz, Kazem Rahimi

In this study, we propose two methods, namely, model distillation and variable selection, to untangle hidden patterns learned by an established deep learning model (BEHRT) for risk association identification.

Decision Making Variable Selection

Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records

no code implementations23 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

Topology Distance: A Topology-Based Approach For Evaluating Generative Adversarial Networks

no code implementations27 Feb 2020 Danijela Horak, Simiao Yu, Gholamreza Salimi-Khorshidi

Automatic evaluation of the goodness of Generative Adversarial Networks (GANs) has been a challenge for the field of machine learning.

$¶$ILCRO: Making Importance Landscapes Flat Again

no code implementations27 Jan 2020 Vincent Moens, Simiao Yu, Gholamreza Salimi-Khorshidi

This paper shows that most of the existing convolutional architectures define, at initialisation, a specific feature importance landscape that conditions their capacity to attend to different locations of the images later during training or even at test time.

Feature Importance Image Classification +2

BEHRT: Transformer for Electronic Health Records

1 code implementation22 Jul 2019 Yikuan Li, Shishir Rao, Jose Roberto Ayala Solares, Abdelaali Hassaine, Dexter Canoy, Yajie Zhu, Kazem Rahimi, Gholamreza Salimi-Khorshidi

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 of illness.

Management

Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation

no code implementations19 Jul 2019 Abdelaali Hassaine, Dexter Canoy, Jose Roberto Ayala Solares, Yajie Zhu, Shishir Rao, Yikuan Li, Mariagrazia Zottoli, Kazem Rahimi, Gholamreza Salimi-Khorshidi

Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population, both in absolute and relative terms.

Benchmarking

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