ICU Prognosis using Explainable Generative Pre-trained Transformers from Electronic Health Records

  • Research theme/s:

    Healthcare

  • Principal investigator (PI):

    Dr. Mohammad Yaqub

  • Researcher/s:

    Nil

  • Funding:

    MBZUAI

  • Department:

    Machine learning and natural language processing

  • Co-PI:

    Nil

  • Student/s

    Diego Saenz

  • Collaborators / partners:

    Nil

Current digital EHR systems gather and organize information from thousands – or even millions – of individuals into curated databases. The medical information collected in these systems for each patient can overwhelm attending clinicians. We propose PICUT, a novel, efficient and explainable transformer-based framework to aggregate patients’ EHR information and produce a broader and global medical language understanding of patients’ history.