Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

Journal: BMC medical informatics and decision making
Published Date: Sep 9, 2024

Abstract

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.

Authors

  • Md Sohanur Rahman
    Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
  • Khandaker Reajul Islam
    Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia.
  • Johayra Prithula
    Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
  • Jaya Kumar
    Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia. jayakumar@ukm.edu.my.
  • Mufti Mahmud
  • Mohammed Fasihul Alam
    Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, 2713, Qatar.
  • Mamun Bin Ibne Reaz
    Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Bangi Selangor 43600, Malaysia.
  • Abdulrahman Alqahtani
    Department of Medical Equipment Technology, College of Applied, Medical Science, Majmaah University, Majmaah City 11952, Saudi Arabia.
  • Muhammad E H Chowdhury
    Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.

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