ArticleBMC infectious diseases2026
Explainable prediction of MDR/RR-TB in tuberculosis-diabetes mellitus multimorbidity: a machine learning model developed and validated in a dual-center study.
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
11 authors.
Funding
Abstract
backgroundTuberculosis-diabetes mellitus (TB-DM) multimorbidity significantly increases the risk of multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR-TB). Early risk stratification tools for this high-risk population remain lacking.
objectiveTo develop and validate an interpretable machine learning (ML) model for predicting MDR/RR-TB in patients with TB-DM multimorbidity, and to identify key predictive factors using explainable artificial intelligence.
methodsThis dual-center retrospective study enrolled 245 patients with TB-DM multimorbidity from January 2019 to December 2022. Seven machine learning algorithms were constructed and validated with 10-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1-score, calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was applied to identify critical predictive factors.
resultsThe random forest (RF) model achieved the optimal performance, with an AUC-ROC of 0.818, accuracy of 0.806, precision of 0.688, recall of 0.611, and F1-score of 0.647; the moderate recall indicates a considerable false-negative rate (FNR) , supporting its use as a triage tool rather than a stand-alone diagnostic test. Calibration and DCA confirmed robust predictive reliability and substantial clinical net benefit within a clinically relevant threshold range of 0.06-0.80. SHAP analysis identified the symptom-to-diagnosis interval, tuberculosis (TB) treatment history, treatment adherence, pulmonary cavitation, and smoking history as the top five critical predictors.
conclusionThe interpretable RF model accurately and reliably predicts the risk of MDR/RR-TB in patients with TB-DM multimorbidity. The symptom-to-diagnosis interval is the most crucial risk factor. This model can assist clinical triage, early intervention, and personalized management.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.