Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Feb 24, 2023

Abstract

BACKGROUND AND OBJECTIVE: Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post hoc) instead of incorporating explanation into the design (ante hoc). This study aimed to demonstrate a human-guided deep learning with ante-hoc explainability by convolutional network from non-image data to develop, validate, and deploy a prognostic prediction model for PROM and an estimator of time of delivery using a nationwide health insurance database.

Authors

  • Herdiantri Sufriyana
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Department of Medical Physiology, College of Medicine, University of Nahdlatul Ulama Surabaya, Surabaya 60237, Indonesia. Electronic address: herdiantrisufriyana@unusa.ac.id.
  • Yu-Wei Wu
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
  • Emily Chia-Yu Su
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw.

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