Kinematics approach with neural networks for early detection of sepsis (KANNEDS).

Journal: BMC medical informatics and decision making
PMID: 34016115

Abstract

BACKGROUND: Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probability for sepsis using vital signs and other time-series variables as input.

Authors

  • Márcio Freire Cruz
    Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama, Ikoma, Nara, 8916-5, Japan. marciofreire@gmail.com.
  • Naoaki Ono
    Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan. nono@is.naist.jp.
  • Ming Huang
    College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
  • Md Altaf-Ul-Amin
    Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
  • Shigehiko Kanaya
    Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
  • Carlos Arthur Mattos Teixeira Cavalcante
    Graduate Program in Mechatronics, Federal University of Bahia, Salvador, Bahia, 40170-110, Brazil.

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