Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study

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.

Special-Purpose English Teaching Reform and Model Design in the Era of Artificial Intelligence

“…In addition, the experimental subjects selected in this study are primary school students. Whether the type of network resources selected by teachers before class can stimulate students' interest in learning is also one of the issues to be paid attention to in this investigation and research [23,24]. At the same time, the duration of online resources and videos expected by students is the focus of this questionnaire.…”

Comparison of Suspected and Confirmed Internal External Ventricular Drain-Related Infections: A Prospective Multicenter United Kingdom Observational Study

“…16S rRNA has had a number of studies evaluating its diagnostic value, and it may be of use for culture-negative suspected iERIs [ 31 , 32 ]. Future research could also include work on developing and validating scoring systems, diagnostic trees [ 11 ], or predictive algorithms, which have previously been found to have high accuracy in differentiating between bacterial and viral meningitides [ 33 ]. This would be significant not only from a diagnostic standpoint, but for promotion of antimicrobial stewardship, to reduce unnecessary use of broad-spectrum antimicrobials, especially meropenem.…”

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

“…The ML algorithms used in the studies include logistic regression (LR) (25%), 46,48,51,54 multiple logistic regression (MLR) (25%), 50,52,53,61 support vector machine (SVM) (19%), 48,54,56 artificial neural network (ANN) (19%), 47,54,60 random forest (RF) (19%), 52,54,56 decision tree (DT) (12%), 57,58 nave-Bayes (NB) (12%), 52,54 fast-and-frugal trees (FFTs) algorithm (6%), 56 and unsupervised ML approach (6%). 59 The target meningitis diseases in the studies included bacterial meningitis (BM) (37%) [51][52][53]56,57,61 or enteroviral meningitis (EVM) (6%), 56 tuberculous meningitis (TBM) (25%), 48,54,55,61 viral meningitis (VM) (19%), 52,54,55 Neisseria meningitides (6%), 59 lumbar drainagerelated meningitis (LDRM) (6%), 47 healthcare-associated ventriculitis and meningitis (HAVM) (6%), 58 cryptococcal meningitis (CM) (6%), 50 PM (6%), 46 and pediatric purulent meningitis (PPM) (6%). 49…”