ArticleBMC medical informatics and decision making2026
Machine learning algorithms for identifying tuberculous pericardial effusion: insights from a retrospective study in Vietnam.
Article in BMC medical informatics and decision making, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTuberculous pericardial effusion (TPE) presents significant diagnostic challenges due to its nonspecific clinical presentation and similarities with other types of pericardial effusion. The available data on the use of artificial intelligence for predicting TPE is minimal and needs further expansion. This study aimed to evaluate the diagnostic performance of various machine learning algorithms (MLAs) in identifying TPE among patients with pericardial effusion. MATERIALS AND
methodsA retrospective study was conducted at Cho Ray Hospital in Vietnam from 2010 to 2020. Eight MLAs-logistic regression, K-nearest neighbor, support vector machine, random forest, Lagrangian support vector machine, random tree (RT), chi-square automatic interaction detection, and C5.0-were evaluated for their diagnostic accuracy. The performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and accuracy.
resultsOf the 248 patients with pericardial effusion, 52 were confirmed to have tuberculosis. Predictive factors for TPE included male sex, a lower body mass index, and fever at admission. The RT model demonstrated the highest accuracy (94%) and area under the curve (AUC) (0.971). Pericardial fluid adenosine deaminase was identified as the most significant feature for TPE diagnosis, with an optimal threshold of 27.8 U/L, a sensitivity of 80.8% and a specificity of 84.2%.
conclusionMachine learning algorithms, particularly the random tree model, demonstrate promising potential for improving TPE diagnosis through noninvasive data analysis. However, successful implementation requires external validation and careful consideration of local healthcare capabilities.
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.