ArticleOpen forum infectious diseases2022
Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis.
Article in Open forum infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.Die Naturwissenschaften · 2026Article
- Machine Learning-Based Severity Stratification for Smart Preventive Decision Support: Evidence from Measles Surveillance in a Resource-Constrained Region.Journal of clinical medicine · 2026Article
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Epidemiological shifts and advances in research on early diagnosis of invasive fungal infection in critically ill patients.Frontiers in cellular and infection microbiology · 2025Review
- Development of a predictive algorithm for patient survival after traumatic injury using a five analyte blood panel.medRxiv : the preprint server for health sciences · 2024Article
- Management of invasive candidiasis in the ICU: Challenges and advances (Review).Medicine internationalReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Methods: Results: Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive Conclusions: Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.
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Registered trials
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