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

Machine Learning (1501) Artificial Intelligence (1421) Clinical Information and Decision Making (1434) Electronic Health Records (1068)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37689, first published .
Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach

Authors of this article:

Donghun Yang1, 2 Author Orcid Image ;   Jimin Kim3 Author Orcid Image ;   Junsang Yoo4 Author Orcid Image ;   Won Chul Cha4, 5 Author Orcid Image ;   Hyojung Paik2, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

Journals

  1. Agnello L, Vidali M, Padoan A, Lucis R, Mancini A, Guerranti R, Plebani M, Ciaccio M, Carobene A. Machine learning algorithms in sepsis. Clinica Chimica Acta 2024;553:117738 View
  2. 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
  3. 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
  4. Visconte V, Maciejewski J, Guarnera L. The potential promise of machine learning in myelodysplastic syndrome. Seminars in Hematology 2024 View