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

Machine Learning (1401) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147) Innovations and Technology in Cancer Care (405) Infectious Diseases (non-STD/STI) (1490)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24651, first published .
Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study

Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study

Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study

Authors of this article:

Junsang Yoo1 Author Orcid Image ;   Si-Ho Kim2 Author Orcid Image ;   Sujeong Hur3, 4 Author Orcid Image ;   Juhyung Ha5 Author Orcid Image ;   Kyungmin Huh6 Author Orcid Image ;   Won Chul Cha4, 7, 8 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

Journals

  1. Kim S, Mun S, Kang J, Moon C, Kim H, Lee H. Multifaceted Evaluation of Antibiotic Therapy as a Factor Associated with Candidemia in Non-Neutropenic Patients. Journal of Fungi 2023;9(2):270 View
  2. Li J, Li Y, Gao Y, Niu X, Tang M, Fu C, Wang Z, Liu J, Song B, Chen H, Gao X, Guan X, Parameshachari B. [Retracted] Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single‐Centre Retrospective Study. BioMed Research International 2022;2022(1) View
  3. Giacobbe D, Mora S, Signori A, Russo C, Brucci G, Campi C, Guastavino S, Marelli C, Limongelli A, Vena A, Mikulska M, Marchese A, Di Biagio A, Giacomini M, Bassetti M. Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia. Diagnostics 2023;13(5):961 View
  4. Fernandes G, Choi A, Schauer J, Pfammatter A, Spring B, Darwiche A, Alshurafa N. An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study. Journal of Medical Internet Research 2023;25:e42047 View
  5. Giacobbe D, Marelli C, Mora S, Guastavino S, Russo C, Brucci G, Limongelli A, Vena A, Mikulska M, Tayefi M, Peluso S, Signori A, Di Biagio A, Marchese A, Campi C, Giacomini M, Bassetti M. Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project. Annals of Medicine 2023;55(2) View
  6. Cao Y, Li Y, Wang M, Wang L, Fang Y, Wu Y, Liu Y, Liu Y, Hao Z, Kang H, Gao H. INTERPRETABLE MACHINE LEARNING FOR PREDICTING RISK OF INVASIVE FUNGAL INFECTION IN CRITICALLY ILL PATIENTS IN THE INTENSIVE CARE UNIT: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE. Shock 2024;61(6):817 View
  7. Giacobbe D, Marelli C, Mora S, Cappello A, Signori A, Vena A, Guastavino S, Rosso N, Campi C, Giacomini M, Bassetti M. Prediction of candidemia with machine learning techniques: state of the art. Future Microbiology 2024:1 View
  8. Kaminski K, Skora M, Krzyściak P, Stączek S, Zdybicka-Barabas A, Cytryńska M. Synthesis and Study of Antifungal Properties of New Cationic Beta-Glucan Derivatives. Pharmaceuticals 2021;14(9):838 View