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

Formative Evaluation of Digital Health Interventions (2491) Machine Learning (1501) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Intensive Care Unit (ICU) (114) Hygiene and Infection Prevention (325) Electronic Health Records (1068) Tools, Programs and Algorithms (275)

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 (19) Tweetations (4) Metrics

Journals

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  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
  11. Gupta J, Majumder A, Sengupta D, Sultana M, Bhattacharya S. Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions. Journal of Intensive Medicine 2024;4(4):468 View
  12. Gupta A, Chauhan R, G S, Shreekumar A, Wang F. Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms. PLOS Digital Health 2024;3(8):e0000569 View
  13. Yadgarov M, Landoni G, Berikashvili L, Polyakov P, Kadantseva K, Smirnova A, Kuznetsov I, Shemetova M, Yakovlev A, Likhvantsev V. Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis. Frontiers in Medicine 2024;11 View
  14. Aityan S, Mosaddegh A, Herrero R, Inchingolo F, Nguyen K, Balzanelli M, Lazzaro R, Iacovazzo N, Cefalo A, Carriero L, Mersini M, Legramante J, Minieri M, Santacroce L, Gargiulo Isacco C. Integrated AI Medical Emergency Diagnostics Advising System. Electronics 2024;13(22):4389 View
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