ArticleJournal of the Royal Society, Interface2016
The power of data mining in diagnosis of childhood pneumonia.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review.BMJ open · 2023Pooled it
- Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation.European journal of medical research · 2025Trial
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- A Novel Knowledge Fusion Ensemble for Diagnostic Differentiation of Pediatric Pneumonia and Acute Bronchitis.Diagnostics (Basel, Switzerland) · 2025Article
- Community-acquired pneumonia identification from electronic health records in the absence of a gold standard: A Bayesian latent class analysis.PLOS digital health · 2025Article
- Article
- Evaluation of Plasma Lipocalin-2 as a Predictor of Etiology and Severity in Adult Patients with Community-Acquired Pneumonia.Microorganisms · 2023Article
- Shine: A novel strategy to extract specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets.BMC bioinformatics · 2023Article
- New Insights into Stroke from Continuous Passively Collected Temperature and Sleep Data Using Wrist-Worn Wearables.Sensors (Basel, Switzerland) · 2023Article
- Article
- Relevance, redundancy, and complementarity trade-off (RRCT): A principled, generic, robust feature-selection tool.Patterns (New York, N.Y.) · 2022Article
- Machine learning predictive modelling for identification of predictors of acute respiratory infection and diarrhoea in Uganda's rural and urban settings.PLOS global public health · 2022Article
- Machine learning-assisted prediction of pneumonia based on non-invasive measures.Frontiers in public health · 2022Article
- Review of LINC00707: A Novel LncRNA and Promising Biomarker for Human Diseases.Frontiers in cell and developmental biology · 2022Review
- Developments in pediatrics in 2020: choices in allergy, autoinflammatory disorders, critical care, endocrinology, genetics, infectious diseases, microbiota, neonatology, neurology, nutrition, ortopedics, respiratory tract illnesses and rheumatology.Italian journal of pediatrics · 2021Review
- Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research.Journal of clinical microbiology · 2021Review
- Article
- Blood biomarkers differentiating viral versus bacterial pneumonia aetiology: a literature review.Italian journal of pediatrics · 2020Review
- Rethink Respiratory Rate for Diagnosing Childhood Pneumonia.EClinicalMedicine · 2019Article
- Handheld Point-of-Care Lactate Measurement at Admission Predicts Mortality in Ugandan Children Hospitalized with Pneumonia: A Prospective Cohort Study.The American journal of tropical medicine and hygiene · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Childhood pneumonia is the leading cause of death of children under the age of 5 years globally. Diagnostic information on the presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (C-reactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians.
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Registered trials
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