Accessibility settings

This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (2367) Clinical Information and Decision Making (3914) Intensive Care Unit (ICU) (194)

Published on in Vol 8, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18352, first published .
Two healthcare professionals in PPE review a chart in a hospital hallway.

Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology

Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology

Authors of this article:

Nazmus Sakib1 Author Orcid Image ;   Sheikh Iqbal Ahamed1 Author Orcid Image ;   Rumi Ahmed Khan2 Author Orcid Image ;   Paul M Griffin3 Author Orcid Image ;   Md Munirul Haque4 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

Journals

  1. Bomrah S, Uddin M, Upadhyay U, Komorowski M, Priya J, Dhar E, Hsu S, Syed-Abdul S. A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability. Critical Care 2024;28(1) View
  2. Yadgarov M, Rebrova O, Berikashvili L, Polyakov P, Kadantseva K, Yakovlev A, Grechko A, Likhvantsev V. Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data. Journal of Clinical Medicine 2026;15(2):777 View
  3. Yadgarov M, Rebrova O, Shvyrev S, Berikashvili L, Polyakov P, Yakovlev A, Likhvantsev V. Prospective internal validation of an XGBoost-based machine learning model for sepsis prediction in patients with prolonged ICU stays. Medical Doctor and Information Technologies 2026;(3):150 View
  4. Meyer-Eschenbach F, Schmiedler R, von Stoephasius J, Zhang C, Kronfli L, Frey N, Näher A, Ehret J, Nothacker J, von Kalle C, Kohler S, Grünewald E, Edel A, Kumpf O, Barrenetxea J, Balzer F. Variability in automated sepsis case detection: a systematic analysis of implementation methods in clinical data repositories. Journal of the American Medical Informatics Association 2026 View

Conference Proceedings

  1. Danda J, Priyansh K, Shahriar H, Haddad H, Cuzzocrea A, Sakib N. 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). Predicting Mortality Rate based on Comprehensive Features of Intensive Care Unit Patients View