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

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
  5. Rawat S, Shanmugam H, Airen L. Machine Learning and Deep Learning Models for Early Sepsis Prediction: A Scoping Review. Indian Journal of Critical Care Medicine 2025;29(6):516 View
  6. Yong Lee J, Ishfaq Hussain M, Lee K, Seop Shim H, Han S, Yang D. Transfer Learning-Based Super-Resolution for High-Precision Medical Imaging. IEEE Access 2025;13:124776 View