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

Formative Evaluation of Digital Health Interventions (2193) Machine Learning (1370) Decision Support for Health Professionals (1127) Clinical Information and Decision Making (1314) Intensive Care Unit (ICU) (101) Hygiene and Infection Prevention (297) Electronic Health Records (961) Tools, Programs and Algorithms (236)

Published on in Vol 5, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28000, first published .
A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study

A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study

A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study

Authors of this article:

Inger Persson1, 2 Author Orcid Image ;   Andreas Östling1 Author Orcid Image ;   Martin Arlbrandt3 Author Orcid Image ;   Joakim Söderberg2 Author Orcid Image ;   David Becedas2 Author Orcid Image
Article Authors Cited by (13) Tweetations (4) Metrics

Journals

  1. . The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden. Journal of Health Economics and Outcomes Research 2022;9(1):101 View
  2. Chen Q, Li R, Lin C, Lai C, Chen D, Qu H, Huang Y, Lu W, Tang Y, Li L. Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Medical Informatics and Decision Making 2022;22(1) View
  3. Chen Q, Li R, Lin C, Lai C, Huang Y, Lu W, Li L. SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis. Applied Clinical Informatics 2023;14(01):65 View
  4. Yang Z, Cui X, Song Z. Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis. BMC Infectious Diseases 2023;23(1) View
  5. Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of Healthcare Informatics Research 2023;7(3):313 View
  6. Wang Z, Qi Y, Wang F, Zhang B, Jianguo T. Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis. Frontiers in Immunology 2023;14 View
  7. Persson I, Grünwald A, Morvan L, Becedas D, Arlbrandt M. A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study. JMIR Formative Research 2023;7:e45979 View
  8. Persson I, Macura A, Becedas D, Sjövall F. Early prediction of sepsis in intensive care patients using the machine learning algorithm NAVOY® Sepsis, a prospective randomized clinical validation study. Journal of Critical Care 2024;80:154400 View
  9. Wu Q, Ye F, Gu Q, Shao F, Long X, Zhan Z, Zhang J, He J, Zhang Y, Xiao Q. A customised down-sampling machine learning approach for sepsis prediction. International Journal of Medical Informatics 2024;184:105365 View
  10. Kashish Joshi , K. Janardhan , M. Manohar , L. Harshavardhan , Mrs. D. Hima Bindu . LifeGuardAI-Artificial Intelligence for Predicting Mortality Due to Sepsis. International Journal of Advanced Research in Science, Communication and Technology 2024:228 View