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

Decision Support for Health Professionals (1392) Clinical Information and Decision Making (1724) Intensive Care Unit (ICU) (143)

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

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