ArticlePLoS neglected tropical diseases2018
Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision.
Article in PLoS neglected tropical diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07432061 (Prediction of Infectious Diseases in LMICs Using Electronic Health Record Data), which is not on this map. Cited by 18 papers.
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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.
Prediction of Infectious Diseases in LMICs Using Electronic Health Record Data
Who cites it
18 citing papers in PubMed, 44 citations in OpenAlex.
- Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study.PloS one · 2025Article
- Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity.Science advances · 2024Article
- Automation for lateral flow rapid tests: Protocol for an open-source fluid handler and applications to dengue and African swine fever tests.PLOS global public health · 2024Article
- Development and validation of a clinical rule for the diagnosis of chikungunya fever in a dengue-endemic area.PloS one · 2023Article
- Typhoid Fever among Patients Diagnosed with Dengue in a Tertiary Care Centre: A Descriptive Cross-sectional Study.JNMA; journal of the Nepal Medical Association · 2022Article
- Mysterious dengue-like virus: A case report from Pakistan.Clinical case reports · 2022Article
- Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce.Healthcare (Basel, Switzerland) · 2022Review
- Machine learning for emerging infectious disease field responses.Scientific reports · 2022Article
- Article
- Laboratory Findings in Patients with Probable Dengue Diagnosis from an Endemic Area in Colombia in 2018.Viruses · 2021Article
- The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings.Journal of thoracic disease · 2021Article
- Surveillance and Epidemiology of Dengue in Medellín, Colombia from 2009 to 2017.The American journal of tropical medicine and hygiene · 2021Article
- Comparing machine learning with case-control models to identify confirmed dengue cases.PLoS neglected tropical diseases · 2020Article
- Diagnosis of COVID-19 in a Dengue-Endemic Area.The American journal of tropical medicine and hygiene · 2020Article
- Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia.The American journal of tropical medicine and hygiene · 2020Article
- Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection.PLoS neglected tropical diseases · 2020Article
- Etiologies of Acute Undifferentiated Febrile Illness in Bangkok, Thailand.The American journal of tropical medicine and hygiene · 2019Article
- A fully automated sample-to-answer PCR system for easy and sensitive detection of dengue virus in human serum and mosquitos.PloS one · 2019Article
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 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differentiating dengue patients from other acute febrile illness patients is a great challenge among physicians. Several dengue diagnosis methods are recommended by WHO. The application of specific laboratory tests is still limited due to high cost, lack of equipment, and uncertain validity. Therefore, clinical diagnosis remains a common practice especially in resource limited settings. Bayesian networks have been shown to be a useful tool for diagnostic decision support. This study aimed to construct Bayesian network models using basic demographic, clinical, and laboratory profiles of acute febrile illness patients to diagnose dengue. Data of 397 acute undifferentiated febrile illness patients who visited the fever clinic of the Bangkok Hospital for Tropical Diseases, Thailand, were used for model construction and validation. The two best final models were selected: one with and one without NS1 rapid test result. The diagnostic accuracy of the models was compared with that of physicians on the same set of patients. The Bayesian network models provided good diagnostic accuracy of dengue infection, with ROC AUC of 0.80 and 0.75 for models with and without NS1 rapid test result, respectively. The models had approximately 80% specificity and 70% sensitivity, similar to the diagnostic accuracy of the hospital's fellows in infectious disease. Including information on NS1 rapid test improved the specificity, but reduced the sensitivity, both in model and physician diagnoses. The Bayesian network model developed in this study could be useful to assist physicians in diagnosing dengue, particularly in regions where experienced physicians and laboratory confirmation tests are limited.
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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.