Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study.

Journal: International journal of medical informatics
Published Date: Jan 28, 2023

Abstract

BACKGROUND: Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compared to commonly used risk stratification criteria in recent studies. However, the black-box nature of these models can make them difficult to apply in clinical practice. In this study, we developed and validated an explainable deep learning model that can predict IBI in febrile infants ≤ 60 days of age visiting the emergency department.

Authors

  • Ying Yang
    Department of Endocrinology, The Affiliated Hospital of Yunnan University, Kunming, China.
  • Yi-Min Wang
    Biomedical Optical Imaging Lab, Department of Photonics, Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
  • Chun-Hung Richard Lin
    Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
  • Chi-Yung Cheng
    Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
  • Chi-Ming Tsai
    Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
  • Ying-Hsien Huang
    Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
  • Tien-Yu Chen
    Division of Cardiology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
  • I-Min Chiu
    Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.

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