ArticleInfection2020
Personalized machine learning approach to predict candidemia in medical wards.
Article in Infection, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Antifungal Stewardship in Invasive Fungal Infections, a Systematic Review.Advances in experimental medicine and biology · 2025Pooled it
- Two-step deep-learning candidemia prediction model using two large time-sequence electronic health datasets.medRxiv : the preprint server for health sciences · 2026Article
- Can Artificial Intelligence Optimize the Early Diagnosis of Invasive Candidiasis? A Systematic Review and Meta-Analysis.Journal of fungi (Basel, Switzerland) · 2026Review
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Learning for the Early Diagnosis of Candidemia.Infectious diseases and therapy · 2025Article
- Epidemiological shifts and advances in research on early diagnosis of invasive fungal infection in critically ill patients.Frontiers in cellular and infection microbiology · 2025Review
- FromFrontiers in medicine · 2025Article
- Prediction of candidemia with machine learning techniques: state of the art.Future microbiology · 2024Review
- Predictive models-assisted diagnosis of AIDS-associated Pneumocystis jirovecii pneumonia in the emergency room, based on clinical, laboratory, and radiological data.Scientific reports · 2024Article
- Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.BMC medical informatics and decision making · 2024Article
- Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study.Applied sciences (Basel, Switzerland) · 2024Article
- A machine learning model for early candidemia prediction in the intensive care unit: Clinical application.PloS one · 2024Article
- Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia.Diagnostics (Basel, Switzerland) · 2023Article
- Novel evidence on sepsis-inducing pathogens: from laboratory to bedside.Frontiers in microbiology · 2023Review
- The prediction of mortality influential variables in an intensive care unit: a case study.Personal and ubiquitous computing · 2023Article
- Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project.Annals of medicine · 2023Article
- Machine Learning and Antibiotic Management.Antibiotics (Basel, Switzerland) · 2022Article
- Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases.Computational and mathematical methods in medicine · 2022Review
- Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost.Diagnostics (Basel, Switzerland) · 2021Article
- Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project.Journal of clinical medicine · 2021Article
Corrections and comments
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Authors and funding
12 authors at 9 institutions in 1 country.
Funding
Abstract
purposeCandidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs).
methodsA set of 42 potential predictors was acquired in a sample of 295 patients (male: 142, age: 72 ± 15 years; candidemia: 157/295; bacteremia: 138/295). Using tenfold cross-validation, a RF algorithm was compared with a classic stepwise multivariable logistic regression model; discriminative performance was assessed by C-statistics, sensitivity and specificity, while calibration was evaluated by Hosmer-Lemeshow test.
resultsThe best tuned RF algorithm demonstrated excellent discrimination (C-statistics = 0.874 ± 0.003, sensitivity = 84.24% ± 0.67%, specificity = 91% ± 2.63%) and calibration (Hosmer-Lemeshow statistics = 12.779 ± 1.369, p = 0.120), markedly greater than the ones guaranteed by the classic stepwise logistic regression (C-statistics = 0.829 ± 0.011, sensitivity = 80.21% ± 1.67%, specificity = 84.81% ± 2.68%; Hosmer-Lemeshow statistics = 38.182 ± 15.983, p < 0.001). In addition, RF suggests a major role of in-hospital antibiotic treatment with microbioma highly impacting antimicrobials (MHIA) that are found as a fundamental risk of candidemia, further enhanced by TPN. When in-hospital MHIA therapy is not performed, PICC is the dominant risk factor for candidemia, again enhanced by TPN. When PICC is not used and MHIA therapy is not performed, the risk of candidemia is minimum, slightly increased by in-hospital antibiotic therapy.
conclusionRF accurately estimates the risk of candidemia in patients admitted to IMWs. Machine learning technique might help to identify patients at high risk of candidemia, reduce the delay in empirical treatment and improve appropriateness in antifungal prescription.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.