# A Masked language model for multi-source EHR trajectories contextual representation learning

arXiv: https://arxiv.org/abs/2402.06675 · HTML: https://mlsanity.com/paper/2402.06675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 1 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)

## Abstract (arXiv)

Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes).