A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning.

Journal: Studies in health technology and informatics
Published Date:

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).

Authors

  • Ali Amirahmadi
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
  • Mattias Ohlsson
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  • Kobra Etminani
    Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address: EtminaniK@mums.ac.ir.
  • Olle Melander
    Department of Clinical Sciences, Lund University, Malmö, Sweden.
  • Jonas Björk
    Department of Clinical Sciences, Lund University, Sweden.