ArticleBMC infectious diseases2026
Machine learning triumphs in differentiating fungal and bacterial infections.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectivesPatients with multiple myeloma (MM) are immunocompromised and highly susceptible to severe infections. Invasive fungal disease (IFD) carries a particularly high mortality rate, yet its presentation often mimics bacterial infection, leading to misdiagnosis, inappropriate antibiotic use, and delayed antifungal therapy. This case-control study aimed to develop and validate a machine learning-based model to accurately distinguish IFD from bacterial infection in MM patients.
methodsIn this retrospective case-control study, we analyzed epidemiological, clinical, and laboratory data from 140 MM patients with IFD (cases) and 158 MM patients with bacterial infections (controls). 16 key variables were used to train nine machine learning models (including Random Forest and XGBoost). The dataset was split into a training cohort (70%) and a test cohort (30%) for performance evaluation.
resultsThe findings demonstrate that the area under the receiver operating characteristic curve (AUC) values for the nine models varied between 0.860 and 0.967. Notably, the logistic regression model exhibited superior performance, achieving an AUC of 0.967, an accuracy of 0.918, a recall of 0.908, and a precision of 0.919. Bootstrap analysis using 500 stratified bootstrap samples revealed that the model’s performance metrics had a standard deviation of less than 0.04 and narrow 95% confidence intervals, with the AUC interval width at 0.021, indicating consistent performance and high accuracy. Additionally, SHapley Additive exPlanation (SHAP) analysis enhanced the model’s interpretability by elucidating the contribution of each predictor.
conclusionWe developed and validated an accurate machine learning-based model that effectively distinguishes IFD from bacterial infections in MM patients. The resulting SHAP analysis provides clinicians with a practical tool for the early identification of high-risk IFD, potentially guiding timely and appropriate treatment decisions.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.