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

Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Intensive Care Unit (ICU) (112)

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 .
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

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 (2) 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