A Masked language model for multi-source EHR trajectories contextual
  representation learning
Ali Amirahmadi Mattias Ohlsson Kobra Etminani Olle Melander Jonas Björk
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).