{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:26:01Z","timestamp":1778894761656,"version":"3.51.4"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:sec id=\"sec020\">\n<jats:title>Introduction<\/jats:title>\n<jats:p>Meningitis, an inflammatory condition of the membranes surrounding the brain and spinal cord, can be caused by various agents. Bacterial meningitis is particularly severe due to its high morbidity and mortality rates. This study aims to develop machine learning (ML) models to classify the aetiology of bacterial meningitis using data from the Notifiable Diseases Information System (SINAN) in S\u00e3o Paulo State, Brazil.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec021\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Data were collected from the SINAN database, including sociodemographic variables, clinical symptoms, and cerebrospinal fluid (CSF) analyses. Five ML models Random Forest, LightGBM, XGBoost, CatBoost, and AdaBoost were applied to classify meningitis cases into bacterial, fungal, viral, and other types. Models were evaluated using metrics such as AUC-ROC, accuracy, precision, recall, F1-score, and MCC.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec022\">\n<jats:title>Results<\/jats:title>\n<jats:p>The CatBoost model demonstrated superior performance, achieving an AUC-ROC of 0.95 for binary classification (bacterial vs. non-bacterial) and 0.85 for multiclass classification (Neisseria meningitidis, Streptococcus pneumoniae, and Haemophilus influenzae). XGBoost and LightGBM also showed promising results with AUC-ROC scores of 0.94 and 0.92, respectively, for binary classification. The CatBoost model exhibited high sensitivity and reasonable specificity, highlighting its applicability in the rapid and accurate diagnosis of meningitis. SHAP analysis identified variables such as leukocyte count and the presence of petechiae as influential predictors in the models.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec023\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>ML algorithms, particularly CatBoost, XGBoost, and LightGBM, proved highly effective in the differential diagnosis of meningitis, offering a valuable tool for the rapid identification of meningitis types and bacterial serogroups. These techniques can be integrated into public health protocols to improve meningitis outbreak responses and optimize patient treatment.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pdig.0000925","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T17:53:03Z","timestamp":1752169983000},"page":"e0000925","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving meningitis surveillance and diagnosis with machine learning: Insights from S\u00e3o Paulo"],"prefix":"10.1371","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8161-3639","authenticated-orcid":true,"given":"Aud\u00eancio","family":"Victor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5250-9453","authenticated-orcid":true,"given":"Diego Augusto","family":"Medeiros Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo Koerich","family":"Nery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danilo Pereira","family":"Mori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pamella Cristina","family":"de Carvalho Lucas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denise","family":"Cammarota","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6617-4095","authenticated-orcid":true,"given":"Guillermo Leonardo","family":"Florez Montero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabiano Novaes","family":"Barcellos Filho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana L\u00facia","family":"Frugis Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Telma Regina Marques Pinto","family":"Carvalhanas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"issue":"4","key":"pdig.0000925.ref001","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1212\/CON.0000000000001016","article-title":"Meningitis","volume":"27","author":"AJ Aksamit","year":"2021","journal-title":"Continuum (Minneap Minn)"},{"key":"pdig.0000925.ref002","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmi.2016.01.007","article-title":"ESCMID guideline: diagnosis and treatment of acute bacterial meningitis","volume":"22","author":"D van de Beek","year":"2016","journal-title":"Clin Microbiol Infect"},{"issue":"8","key":"pdig.0000925.ref003","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/S1474-4422(23)00195-3","article-title":"Global, regional, and national burden of meningitis and its aetiologies, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019","volume":"22","author":"HY Wunrow","year":"2023","journal-title":"Lancet Neurol"},{"key":"pdig.0000925.ref004","article-title":"Geografia e sa\u00fade coletiva: an\u00e1lise da din\u00e2mica epidemiol\u00f3gica das meningites no Brasil, entre os anos de 2010 e 2019","volume":"27","author":"LR Silva da","year":"2024","journal-title":"Rev Bras Epidemiol"},{"key":"pdig.0000925.ref005","author":"Minist\u00e9rio da Sa\u00fade","year":"2024"},{"issue":"16","key":"pdig.0000925.ref006","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1016\/j.vaccine.2017.03.010","article-title":"Impact of meningococcal C conjugate vaccination four years after introduction of routine childhood 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