2020年4月28日 · In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one’s...

Here we present the code for paper 'BEHRT: Transformer for Electronic Health Records', which is available at: https://www.nature.com/articles/s41598-020-62922-y.

2019年7月22日 · In this study, we introduce BEHRT: A deep neural sequence transduction model for EHR (electronic health records), capable of multitask prediction and disease trajectory mapping.

Repository for publication: Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records IEEE Transactions on Neural Networks and Learning Systems; Special Issue on Causality

BEHRT: Transformer for Electronic Health Recrods. Contribute to yikuanli/BEHRT development by creating an account on GitHub.

2019年7月22日 · In this study, we introduce BEHRT: A deep neural sequence transduction model for EHR (electronic health records), capable of multitask prediction and disease trajectory mapping.

2024年4月23日 · Through incremental optimization, we study BERT-based EHR modeling and isolate the sources of improvement for key design choices, giving us insights into the effect of data representation, individual technical components, and training procedure.

2023年9月29日 · BEHRT (BERT for EHR) is introduced, which is a deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions...

In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one’s...

In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR).