Interpretable disease prediction using heterogeneous patient records with self-attentive fusion encoder.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Sep 18, 2021

Abstract

OBJECTIVE: We propose an interpretable disease prediction model that efficiently fuses multiple types of patient records using a self-attentive fusion encoder. We assessed the model performance in predicting cardiovascular disease events, given the records of a general patient population.

Authors

  • Heeyoung Kwak
    Department of Electrical Engineering, Seoul National University, Seoul, Republic of Korea.
  • Jooyoung Chang
    Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea.
  • Byeongjin Choe
    Department of Electrical Engineering, Seoul National University , Seoul, Republic of Korea.
  • Sangmin Park
    Department of Transportation System Engineering, Ajou University, Suwon, Republic of Korea.
  • Kyomin Jung
    Department of Electrical Engineering , Seoul National University, Seoul, Republic of Korea.

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