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

Ali Amirahmadi, Mattias Ohlsson, Kobra Etminani, Olle Melander, Jonas Björk

Abstract: 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).