{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T08:56:33Z","timestamp":1791190593963,"version":"4.1.0"},"reference-count":18,"publisher":"S. Karger AG","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cerebrovasc Dis"],"abstract":"<jats:p>Introduction: Symptomatic intracranial hemorrhage (sICH) remains a devastating complication undermining reperfusion therapy in acute ischemic stroke (AIS). Current prediction models rely on static baseline parameters, neglecting dynamic post-treatment physiological changes. We aimed to develop and validate a dynamic, explainable risk prediction framework integrating high-resolution temporal data. Methods: In this prospective multicenter study, 2,341 AIS patients with anterior circulation large vessel occlusion receiving reperfusion therapy were consecutively enrolled (January 2023\u2013June 2024). High-frequency vital signs and serial laboratory tests were collected over 72 h post-treatment. Time-series features (trend, volatility, stability) were engineered, and XGBoost-based dynamic models were constructed at five timepoints (6, 12, 24, 48, 72 h). Performance was evaluated in a held-out validation cohort (n = 576) against a static baseline model. SHAP was applied for interpretability. Results: Of 2,158 analyzed patients, 218 (10.1%) developed sICH. The dynamic model consistently outperformed the static model across all timepoints (AUC 0.83\u20130.90 vs. 0.71\u20130.75; all p &lt; 0.001). SHAP identified three core risk drivers: hemodynamic volatility (24-h SBP standard deviation ranked highest), metabolic-inflammatory trajectories, and early tissue injury markers. Unsupervised clustering revealed three risk phenotypes: persistent low-risk (71.5%, sICH 0.8%), early rapid-riser (18.2%, 52.3%), and delayed-riser (10.3%, 38.1%). Conclusion: This dynamic, explainable framework shifts sICH risk prediction from static snapshots to continuous, interpretable monitoring. Hemodynamic volatility emerged as the strongest modifiable predictor, offering clinically actionable insights for personalized post-procedural care in AIS.<\/jats:p>","DOI":"10.1159\/ced\/accag010","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T13:03:58Z","timestamp":1786021438000},"page":"1-1","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic, Explainable Artificial Intelligence Framework for Sequential Prediction of Symptomatic Intracranial Hemorrhage after Reperfusion Therapy in Acute Ischemic Stroke"],"prefix":"10.1159","author":[{"given":"Lin","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaocui","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aihua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"127","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clineuro.2022.107184"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-053765"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-022-11046-6"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurad.2023.11.003"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1136\/jnis-2022-019719"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2147\/JMDH.S495952"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2023.1224658"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2023.1320055"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2024.1361035"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.123.045495"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/ene.16296"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2024.1367177"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1186\/s12883-024-03660-w"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2024.108083"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e29998"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1186\/s12883-024-03973-w"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2024.107925"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2024.1449604"}],"container-title":["Cerebrovascular Diseases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/karger.com\/article-pdf\/doi\/10.1159\/ced\/accag010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T08:47:18Z","timestamp":1791190038000},"score":1,"resource":{"primary":{"URL":"https:\/\/karger.com\/article\/doi\/10.1159\/ced\/accag010"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1159\/ced\/accag010","archive":["Portico"],"relation":{"is-supplemented-by":[{"id-type":"doi","id":"10.6084\/m9.figshare.33183948","asserted-by":"subject"}]},"ISSN":["1015-9770","1421-9786"],"issn-type":[{"value":"1015-9770","type":"print"},{"value":"1421-9786","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}