{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:06:16Z","timestamp":1789707976376,"version":"4.0.1"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study aimed to develop an interpretable and clinically applicable machine learning prediction model to assess the risk of adverse outcomes within one year following acute ischaemic stroke, and to establish a corresponding web-based risk assessment tool.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective cohort study included 585 patients with acute ischaemic stroke, randomly allocated into training (70%) and validation (30%) datasets. Recursive feature elimination (RFE) was employed to identify pivotal predictors. Ten machine learning algorithms were constructed and evaluated using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The random forest algorithm demonstrated superior discriminative performance, achieving a validation AUC of 0.870 (95% CI 0.816\u20130.923). DCA confirmed optimal clinical net benefit with this model. SHAP analysis identified age, D-dimer, and systemic immune-inflammation index as principal determinants of adverse prognosis. A web-based risk calculator was subsequently deployed (\n                      <jats:ext-link>https:\/\/zxy08887.shinyapps.io\/1YPP-AIS-Model\/<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study established a high-performance, interpretable machine learning model capable of accurately stratifying one-year prognostic risk in patients with acute ischaemic stroke. The web-based tool provides a proof-of-concept prototype for individualised risk assessment. However, strict external validation in independent cohorts is required before it can be considered ready for clinical deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1956859","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:33:35Z","timestamp":1789706015000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and web-based implementation of a machine learning model for predicting one-year adverse outcomes in acute ischaemic stroke"],"prefix":"10.3389","volume":"13","author":[{"given":"ChunYu","family":"Meng","sequence":"first","affiliation":[{"name":"Department of Rehabilitation Medicine, Xuzhou First People's Hospital","place":["Xuzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"FeiFei","family":"Zuo","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Xuzhou 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