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

Clinical Information and Decision Making (1915) Clinical Informatics (1325) Artificial Intelligence (2129) Machine Learning (2076) Intensive Care Unit (ICU) (160) Nosocomial Infections (30) New Methods (1134) Computer-Aided Diagnosis (251)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55492, first published .
Complete Blood Count and Monocyte Distribution Width–Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study

Complete Blood Count and Monocyte Distribution Width–Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study

Complete Blood Count and Monocyte Distribution Width–Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study

Authors of this article:

Andrea Campagner1 Author Orcid Image ;   Luisa Agnello2 Author Orcid Image ;   Anna Carobene3 Author Orcid Image ;   Andrea Padoan4, 5 Author Orcid Image ;   Fabio Del Ben6 Author Orcid Image ;   Massimo Locatelli3 Author Orcid Image ;   Mario Plebani4, 5 Author Orcid Image ;   Agostino Ognibene7 Author Orcid Image ;   Maria Lorubbio7 Author Orcid Image ;   Elena De Vecchi1 Author Orcid Image ;   Andrea Cortegiani2, 8 Author Orcid Image ;   Elisa Piva9 Author Orcid Image ;   Donatella Poz10 Author Orcid Image ;   Francesco Curcio10 Author Orcid Image ;   Federico Cabitza1, 11 Author Orcid Image ;   Marcello Ciaccio2, 8 Author Orcid Image
Article Authors Cited by (8) Tweetations Metrics

Journals

  1. Marconi L, Cabitza F. Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI. International Journal of Medical Informatics 2025;202:105970 View
  2. Yesil M, Talli I, Pelloso M, Cosma C, Pangrazzi E, Plebani M, Ustundag Y, Padoan A. Impact of analytical bias on machine learning models for sepsis prediction using laboratory data. Clinical Chemistry and Laboratory Medicine (CCLM) 2025;63(10):2022 View
  3. Barkas G, Dimeas I, Kotsiou O. Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management. Diagnostics 2025;15(15):1890 View
  4. DEMİRCİ F, AKŞİT M, DEMİRCİ A. Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers. Forbes Journal of Medicine 2025 View
  5. Silva G, Barcellos Filho F, Wichmann R, da Silva Junior F, Chiavegatto Filho A. Strategies for detecting and mitigating dataset shift in machine learning for health predictions: A systematic review. Journal of Biomedical Informatics 2025;170:104902 View
  6. Carobene A, Cadamuro J, Frans G, Goldshmidt H, Debeljak Z, De Bruyne S, van Doorn W, Elias J, Özdemir H, Martin Perez S, Lame H, Tolios A, Cabitza F, Padoan A. EFLM checklist for the assessment of AI/ML studies in laboratory medicine: enhancing general medical AI frameworks for laboratory-specific applications. Clinical Chemistry and Laboratory Medicine (CCLM) 2026;64(1):27 View
  7. Kim H, Hur M, Lee H, Lee G, Lee K, Mannello F. Early Sepsis Detection in Adult Patients with Suspected Sepsis in an Emergency Setting: A Sequential Strategy of Monocyte Distribution Width and Presepsin. Diagnostics 2025;15(20):2574 View
  8. Zhou H, Li F, Liu X. Early prediction of septic shock in ICU patients using machine learning: development, external validation, and explainability with SHAP. International Journal of Medical Informatics 2026;206:106169 View