{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:30:23Z","timestamp":1792805423302,"version":"4.5.0"},"reference-count":102,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome, mortality) in ICH, aiming to provide consolidated evidence for future research and clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We systematically searched PubMed, Embase, Web of Science, and Cochrane Library up to September 2025. Studies developing and validating ML models for predicting HE, poor functional outcome (modified Rankin Scale 3\u20136), or mortality in adults with spontaneous ICH were included. Pooled concordance index (C\u2010index), sensitivity, and specificity were calculated using a random\u2010effects or bivariate model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Eighty\u2010three studies (involving at least 136,840 patients) were included. Meta\u2010analysis of model performance, derived predominantly from internal validation set, demonstrated that models integrating both clinical and radiomics features achieved the highest discriminative performance across key prognostic prediction tasks: predicting HE (pooled C\u2010index 0.822, 95% confidence interval [CI] 0.789\u20130.855), poor functional outcome (C\u2010index 0.850, 95% CI 0.830\u20130.869), and mortality (C\u2010index 0.860, 95% CI 0.809\u20130.911). These findings should be interpreted with caution, as true external validation remains sparse. Logistic regression exhibited performance comparable to more complex ML algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML models, particularly integrated clinical\u2010radiomics models, demonstrate strong performance for the prediction of outcomes in ICH. They hold significant potential to enhance risk stratification and guide personalized management, pending further validation in diverse cohorts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.71572","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T07:22:36Z","timestamp":1785482556000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning\u2010Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta\u2010Analysis"],"prefix":"10.1002","volume":"16","author":[{"given":"Qi","family":"Deng","sequence":"first","affiliation":[{"name":"Neurology Department PKUCare Rehabilitation Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weitao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Xuanwu Hospital Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Lu","sequence":"additional","affiliation":[{"name":"Neurology Department Aerospace Central Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Fei","sequence":"additional","affiliation":[{"name":"Neurology Department PKUCare Rehabilitation Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"He","sequence":"additional","affiliation":[{"name":"Neurology Department PKUCare Rehabilitation Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Rehab Department PKUCare Rehabilitation Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlong","family":"Bi","sequence":"additional","affiliation":[{"name":"Intensive Care Unit PKUCare Rehabilitation Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenluo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Neurology Department PKUCare Rehabilitation Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/23969873211046116"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1161\/strokeaha.121.037332"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.2147\/ijgm.S408725"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0140\u20106736(20)31179\u2010x"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.1161\/strokeaha.120.032750"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2023.10.002"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.14336\/ad.2020.0421"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2020.02.021"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.790682"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111081"},{"key":"e_1_2_14_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911\u2010025\u201002865\u20104"},{"key":"e_1_2_14_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/23969873241260154"},{"key":"e_1_2_14_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/08465371231168383"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/nep.13913"},{"key":"e_1_2_14_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280218785504"},{"key":"e_1_2_14_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14242827"},{"key":"e_1_2_14_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00062\u2010021\u201001040\u20102"},{"key":"e_1_2_14_19_1","doi-asserted-by":"crossref","unstructured":"Feng Q. 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