{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:32:39Z","timestamp":1788780759263,"version":"build-2803163510"},"reference-count":16,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T00:00:00Z","timestamp":1681689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Interv Neuroradiol"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Aneurysmal subarachnoid hemorrhage results in significant mortality and disability, which is worsened by the development of delayed cerebral ischemia. Tests to identify patients with delayed cerebral ischemia prospectively are of high interest.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We created a machine learning system based on clinical variables to predict delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients. We also determined which variables have the most impact on delayed cerebral ischemia prediction using SHapley Additive exPlanations method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>500 aneurysmal subarachnoid hemorrhage patients were identified and 369 met inclusion criteria: 70 patients developed delayed cerebral ischemia (delayed cerebral ischemia+) and 299 did not (delayed cerebral ischemia\u2212). The algorithm was trained based upon age, sex, hypertension (HTN), diabetes, hyperlipidemia, congestive heart failure, coronary artery disease, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain placement. Random Forest was selected for this project, and prediction outcome of the algorithm was delayed cerebral ischemia+. SHapley Additive exPlanations was used to visualize each feature's contribution to the model prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Random Forest machine learning algorithm predicted delayed cerebral ischemia: accuracy 80.65% (95% CI: 72.62\u201388.68), area under the curve 0.780 (95% CI: 0.696\u20130.864), sensitivity 12.5% (95% CI: \u22123.7 to 28.7), specificity 94.81% (95% CI: 89.85\u201399.77), PPV 33.3% (95% CI: \u22124.39 to 71.05), and NPV 84.1% (95% CI: 76.38\u201391.82). SHapley Additive exPlanations value demonstrated Age, external ventricular drain placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for delayed cerebral ischemia. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and external ventricular drain placement increased risk of delayed cerebral ischemia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning models based upon clinical variables predict delayed cerebral ischemia with high specificity and good accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15910199231170411","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T02:03:53Z","timestamp":1681783433000},"page":"375-380","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach"],"prefix":"10.1177","volume":"31","author":[{"given":"Reza M","family":"Taghavi","sequence":"first","affiliation":[{"name":"Department of Medicine, University of California at Davis Medical School, Sacramento, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Wintermark","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, MD Anderson, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriella M","family":"Kuraitis","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric S","family":"Sussman","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Connecticut, Hartford, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Pulli","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brook","family":"Biniam","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Ottawa Medical School, Ottawa, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Ostmeier","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gary K","family":"Steinberg","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1055-8000","authenticated-orcid":false,"given":"Jeremy J","family":"Heit","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181dd42b3"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.28.3.660"},{"key":"e_1_3_3_4_2","first-page":"77","article-title":"Management of delayed cerebral ischemia after subarachnoid hemorrhage","volume":"20","author":"Francoeur CL","unstructured":"Francoeur CL, Mayer SA. Management of delayed cerebral ischemia after subarachnoid hemorrhage. Crit Care 2016 Oct 14; 20: 77. PMID: 27737684; PMCID: PMC5064957.","journal-title":"Crit Care"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jns.2012.01.003"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0b013e3182587839"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-011-9605-9"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000011211"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1136\/neurintsurg-2018-014258"},{"key":"e_1_3_3_10_2","article-title":"Prediction models in aneurysmal subarachnoid hemorrhage: forecasting clinical outcome with artificial intelligence","volume":"88","author":"de Jong G","unstructured":"de Jong G, Aquarius R, Sanaan B, et al. Prediction models in aneurysmal subarachnoid hemorrhage: forecasting clinical outcome with artificial intelligence. Neurosurgery 2021 Apr 15; 88: E427\u2013E434. PMID: 33548918.","journal-title":"Neurosurgery"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.791547"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.110.589275"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-016-1447-6"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.18700\/jnc.200002"},{"key":"e_1_3_3_15_2","article-title":"Transcranial Doppler Monitoring in Subarachnoid Hemorrhage","volume":"14","author":"Park S-H","year":"2022","unstructured":"Park S-H, Kim TJ, Ko S-B. Transcranial Doppler Monitoring in Subarachnoid Hemorrhage. Neurosonol Neuroimag 2022; 14: \u20139.","journal-title":"Neurosonol Neuroimag"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A2246"},{"key":"e_1_3_3_17_2","first-page":"26","article-title":"Vasospasm after subarachnoid hemorrhage: utility of perfusion CT and CT angiography on diagnosis and management","volume":"27","author":"Wintermark M","unstructured":"Wintermark M, Ko NU, Smith WS, et al. Vasospasm after subarachnoid hemorrhage: utility of perfusion CT and CT angiography on diagnosis and management. AJNR Am J Neuroradiol 2006 Jan; 27: 26\u201334. PMID: 16418351; PMCID: PMC7976085.","journal-title":"AJNR Am J Neuroradiol"}],"container-title":["Interventional Neuroradiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15910199231170411","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15910199231170411","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15910199231170411","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:56:29Z","timestamp":1777460189000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15910199231170411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,17]]},"references-count":16,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10.1177\/15910199231170411"],"URL":"https:\/\/doi.org\/10.1177\/15910199231170411","relation":{},"ISSN":["1591-0199","2385-2011"],"issn-type":[{"value":"1591-0199","type":"print"},{"value":"2385-2011","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,17]]}}}