ArticleDiagnostics (Basel, Switzerland)2021
Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.BMC oral health · 2024Pooled it
- An explainable machine learning model predicts pediatric varicella encephalitis.Frontiers in cellular and infection microbiology · 2026Article
- A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo.PLOS digital health · 2025Article
- Applications of artificial intelligence-machine learning for detection of stress: a critical overview.Molecular psychiatry · 2024Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review.Health science reports · 2024Review
- Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models.Diagnostics (Basel, Switzerland) · 2023Article
- A diagnostic model based on routine blood examination for serious bacterial infections in neonates-a cross-sectional study.Epidemiology and infection · 2023Article
- Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Comparison of Suspected and Confirmed Internal External Ventricular Drain-Related Infections: A Prospective Multicenter United Kingdom Observational Study.Open forum infectious diseases · 2022Article
- Spatial and temporal variation of routine parameters: pitfalls in the cerebrospinal fluid analysis in central nervous system infections.Journal of neuroinflammation · 2022Review
- An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0-14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.
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