A Masked language model for multi-source EHR trajectories contextual representation learning
<|reference_start|>A Masked language model for multi-source EHR trajectories contextual representation learning: 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).<|reference_end|>
@article{amirahmadi2024a,
    title={A Masked language model for multi-source EHR trajectories contextual
  representation learning},
    author={Ali Amirahmadi (1), Mattias Ohlsson (1,2), Kobra Etminani (1), Olle
  Melander (3), and Jonas Bj"ork (4) ((1) Center for Applied Intelligent
  Systems Research, Halmstad University, (2) Centre for Environmental and
  Climate Science, Lund University, (3) Department of Clinical Sciences, Lund
  University, (4) Division of Occupational and Environmental Medicine, Lund
  University)},
    journal={arXiv preprint arXiv:2402.06675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06675},
    primaryClass={cs.LG}
}