{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T19:59:27Z","timestamp":1793735967901,"version":"4.6.1"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The incidence of ventilator-associated pneumonia (VAP) in ischemic stroke (IS) patients is linked to a variety of detrimental outcomes. Current approaches for the early identification of individuals at high risk for developing VAP are limited and often lack clinical interpretability. The goal of this study is to develop and validate an interpretable machine learning (ML) model for early predicting VAP risk in IS patients in the intensive care unit (ICU).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data on IS patients were extracted from versions 2.2 and 3.0 of the Medical Information Mart for Intensive Care-IV database, with version 2.2 being used for model training and internal validation and version 3.0 for external testing. The primary outcome was the incidence of VAP post-ICU admission. The Boruta algorithm was used to select features prior to developing 10 ML models. The Shapley Additive Explanation (SHAP) method was employed to assess the global and local interpretability of the model\u2019s decision-making process. The final model and Streamlit were used for developing and launching an online web application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 419 IS patients were included, with 401 in the derivation and 118 in the test group. Following feature selection, seven clinical characteristics were incorporated in the ML model: systolic and diastolic blood pressure, international normalized ratio, length of stay before mechanical ventilation, dysphagia, antibiotic counts and suctioning counts. Among the 10 evaluated ML models, the Random Forest (RF) model outperformed the others, achieving an internal validation AUC of 0.776, accuracy of 0.704, sensitivity of 0.900, and specificity of 0.588. In external testing, performance dropped to an AUC of 0.644, accuracy of 0.610, sensitivity of 0.688, and specificity of 0.519, raising concerns about the model\u2019s generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The RF model is reliable in early identifying high-risk IS patients for VAP. The SHAP method offers clear and intuitive explanations for individual risk assessment. The web-based tool has the potential to improve clinical outcomes by promptly recognizing patients at increased VAP risk and facilitating early intervention, further multicenter prospective studies are required to validate its generalizability and practical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1513732","type":"journal-article","created":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:10:30Z","timestamp":1746576630000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Interpretable machine learning for early predicting the risk of ventilator-associated pneumonia in ischemic stroke patients in the intensive care unit"],"prefix":"10.3389","volume":"16","author":[{"given":"Heshan","family":"Cao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junying","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songran","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,7]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1177\/17474930211065917","article-title":"World stroke organization (WSO): global stroke fact sheet 2022","volume":"17","author":"Feigin","year":"2022","journal-title":"Int J Stroke"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2820","DOI":"10.1016\/S0140-6736(24)00642-1","article-title":"Stroke","volume":"403","author":"Hilkens","year":"2024","journal-title":"Lancet"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1007\/s00134-020-05980-0","article-title":"Ventilator-associated pneumonia in adults: a narrative review","volume":"46","author":"Papazian","year":"2020","journal-title":"Intensive Care Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.jcrc.2010.09.006","article-title":"Ventilator-associated pneumonia in critically ill stroke patients: frequency, risk factors, and outcomes","volume":"26","author":"Kasuya","year":"2011","journal-title":"J Crit Care"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1186\/1472-6963-11-289","article-title":"Long-term medical utilization following ventilator-associated pneumonia in acute stroke and traumatic brain injury patients: a case-control study","volume":"11","author":"Yang","year":"2011","journal-title":"BMC Health Serv Res"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"4355","DOI":"10.3390\/s24134355","article-title":"Comprehensive review: machine and deep learning in brain stroke diagnosis","volume":"24","author":"Fernandes","year":"2024","journal-title":"Sensors"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"769","DOI":"10.4103\/1673-5374.382228","article-title":"Machine learning applications in stroke medicine: advancements, challenges, and future prospectives","volume":"19","author":"Daidone","year":"2024","journal-title":"Neural Regen Res"},{"key":"ref8","author":"Lundberg","year":"2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.13026\/hxp0-hg59","article-title":"MIMIC-IV (version 3.0)","author":"Johnson","year":"2024","journal-title":"PhysioNet"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J Stat Softw"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1212\/WNL.0b013e31823152b1","article-title":"Canadian stroke network; stroke outcome research Canada (SORCan) working group. 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