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Found 9 papers, 2 papers with code
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning
Most machine learning (ML) models are developed for prediction only; offering no option for causal interpretation of their predictions or parameters/properties.
Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records
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).
Transfer Learning in Electronic Health Records through Clinical Concept Embedding
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.
Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records
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
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.
An explainable Transformer-based deep learning model for the prediction of incident heart failure
Predicting the incidence of complex chronic conditions such as heart failure is challenging.
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records
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.
BEHRT: Transformer for Electronic Health Records
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.
Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation
Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population, both in absolute and relative terms.
