ArticleFrontiers in microbiology2019
Artificial Neural Networks for Prediction of Tuberculosis Disease.
Article in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.
- Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Data-driven model analysis of the impact of environmental and socioeconomic factors on tuberculosis incidence.Infectious Disease Modelling · 2026Article
- Machine learning algorithms for identifying tuberculous pericardial effusion: insights from a retrospective study in Vietnam.BMC medical informatics and decision making · 2026Article
- Machine learning methods to predict leprosy misdiagnosis in Yunnan Province, People's Republic of China.Frontiers in public health · 2026Article
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- Article
- Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings.Scientific reports · 2024Article
- Machine Learning to Predict the Adsorption Capacity of Microplastics.Nanomaterials (Basel, Switzerland) · 2023Article
- A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network.Frontiers in genetics · 2023Article
- A Scoring System Based on Laboratory Parameters and Clinical Features to Predict Unfavorable Treatment Outcomes in Multidrug- and Rifampicin-Resistant Tuberculosis Patients.Infection and drug resistance · 2023Article
- Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters.American journal of epidemiology · 2022Article
- Machine learning in the loop for tuberculosis diagnosis support.Frontiers in public health · 2022Article
- A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis.Frontiers in genetics · 2022Article
- Impact of a computer system as a triage tool in the management of pulmonary tuberculosis in a HIV reference center in Brazil.Revista da Sociedade Brasileira de Medicina Tropical · 2022Article
- A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data.Frontiers in neuroscience · 2022Article
- Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.Scientific reports · 2021Article
- Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks.Genes · 2021Article
- Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection.Journal of public health research · 2021Article
- Article
- Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.BMC medical research methodology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.