SSP: Early prediction of sepsis using fully connected LSTM-CNN model.

Journal: Computers in biology and medicine
PMID: 33227577

Abstract

BACKGROUND: Sepsis is a life-threatening condition that occurs due to the body's reaction to infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of sepsis onset facilitates early interventions that promote the survival of suspected patients. However, reliable and intelligent systems for predicting sepsis are scarce.

Authors

  • Alireza Rafiei
    Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic address: alirezarafiei@ut.ac.ir.
  • Alireza Rezaee
    Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic address: arrezaee@ut.ac.ir.
  • Farshid Hajati
    School of Information Technology and Engineering, MIT Sydney, Sydney, New South Wales, Australia.
  • Soheila Gheisari
    Vision Science Group, Graduate School of Health, University of Technology Sydney, Australia. Electronic address: soheila.gheisari@uts.edu.au.
  • Mojtaba Golzan
    Vision Science Group, Graduate School of Health, University of Technology Sydney, Australia. Electronic address: mojtaba.golzan@uts.edu.au.

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