{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:17:30Z","timestamp":1788369450682,"version":"build-2803163510"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.cmpb.2026.109572","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:42:14Z","timestamp":1785307334000},"page":"109572","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Interpretable Machine Learning Model Using Oxygenation and Respiratory Variability to Predict Hemorrhagic Stroke Mortality: A Multicenter Validation Study"],"prefix":"10.1016","volume":"286","author":[{"given":"Jing","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianheng","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengsi","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"San","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baojian","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2026.109572_bib0001","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-90944-x","article-title":"Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients","volume":"15","author":"Abujaber","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.cmpb.2026.109572_bib0002","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.gheart.2014.01.003","article-title":"Global Burden of Diseases, Injuries, and Risk Factors 2010 Study Stroke Expert Group, The global burden of hemorrhagic stroke: a summary of findings from the GBD 2010 study","volume":"9","author":"Krishnamurthi","year":"2014","journal-title":"Glob Heart"},{"key":"10.1016\/j.cmpb.2026.109572_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.clineuro.2021.106796","article-title":"Oxygen desaturation and adverse outcomes in acute stroke: Secondary analysis of the HeadPoST study","volume":"207","author":"Ouyang","year":"2021","journal-title":"Clin Neurol Neurosurg"},{"key":"10.1016\/j.cmpb.2026.109572_bib0004","doi-asserted-by":"crossref","unstructured":"J. Collins, I. Rybinnik, B. Fischberg, J. McCoy, Stroke, Oxygen and Prehospital Care: A Commentary on Current Treatments and Opportunities for Improvement, J Exp Neurol Volume 2 (n.d.) 39\u201346. https:\/\/doi.org\/10.33696\/Neurol.2.034.","DOI":"10.33696\/Neurol.2.034"},{"key":"10.1016\/j.cmpb.2026.109572_bib0005","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1186\/s13054-025-05387-7","article-title":"Neurological outcomes and mortality following hyperoxemia in adult patients with acute brain injury: an updated meta-analysis and meta-regression","volume":"29","author":"Romero-Garcia","year":"2025","journal-title":"Crit Care"},{"key":"10.1016\/j.cmpb.2026.109572_bib0006","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.annemergmed.2022.09.026","article-title":"Effect of Different Early Oxygenation Levels on Clinical Outcomes of Patients Presenting in the Emergency Department With Severe Traumatic Brain Injury","volume":"81","author":"Vrettou","year":"2023","journal-title":"Ann Emerg Med"},{"key":"10.1016\/j.cmpb.2026.109572_bib0007","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2023.1234046","article-title":"Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke","volume":"14","author":"Lee","year":"2023","journal-title":"Front Neurol"},{"key":"10.1016\/j.cmpb.2026.109572_bib0008","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s12028-022-01504-4","article-title":"Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care","volume":"37","author":"Moss","year":"2022","journal-title":"Neurocrit Care"},{"key":"10.1016\/j.cmpb.2026.109572_bib0009","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1186\/s12859-023-05456-0","article-title":"IHCP: interpretable hepatitis C prediction system based on black-box machine learning models","volume":"24","author":"Fan","year":"2023","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.cmpb.2026.109572_bib0010","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-44155-x","article-title":"The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP","volume":"13","author":"Prendin","year":"2023","journal-title":"Sci Rep"},{"key":"10.1016\/j.cmpb.2026.109572_bib0011","article-title":"Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births","volume":"25","author":"Z","year":"2025","journal-title":"BMC Pregnancy and Childbirth"},{"key":"10.1016\/j.cmpb.2026.109572_bib0012","unstructured":"A. Johnson, L. Bulgarelli, T. Pollard, B. Gow, B. Moody, S. Horng, L.A. Celi, R. Mark, MIMIC-IV, (n.d.). https:\/\/doi.org\/10.13026\/07HJ-2A80."},{"key":"10.1016\/j.cmpb.2026.109572_bib0013","doi-asserted-by":"crossref","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":"10.1016\/j.cmpb.2026.109572_bib0014","unstructured":"T.J. Pollard, A.E.W. Johnson, J. Raffa, O. Badawi, The eICU Collaborative Research Database, (2017). https:\/\/doi.org\/10.13026\/C2WM1R."},{"key":"10.1016\/j.cmpb.2026.109572_bib0015","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2018.178","article-title":"The eICU Collaborative Research Database, a freely available multi-center database for critical care research","volume":"5","author":"Pollard","year":"2018","journal-title":"Sci Data"},{"key":"10.1016\/j.cmpb.2026.109572_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110028","article-title":"Machine learning prediction models for multidrug-resistant organism infections in ICU ventilator-associated pneumonia patients: Analysis using the MIMIC-IV database","volume":"190","author":"Cui","year":"2025","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.cmpb.2026.109572_bib0017","series-title":"StatPearls","article-title":"Oxygenation Status and Pulse Oximeter Analysis","author":"Pesola","year":"2025"},{"key":"10.1016\/j.cmpb.2026.109572_bib0018","doi-asserted-by":"crossref","first-page":"6396","DOI":"10.3390\/s20216396","article-title":"The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise","volume":"20","author":"Nicol\u00f2","year":"2020","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cmpb.2026.109572_bib0019","doi-asserted-by":"crossref","first-page":"531","DOI":"10.5694\/j.1326-5377.2008.tb02164.x","article-title":"Respiratory rate: the neglected vital sign","volume":"189","author":"Steichen","year":"2008","journal-title":"Med J Aust"},{"key":"10.1016\/j.cmpb.2026.109572_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2025.105874","article-title":"Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU","volume":"198","author":"Luo","year":"2025","journal-title":"Int J Med Inform"},{"key":"10.1016\/j.cmpb.2026.109572_bib0021","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1002\/sam.11348","article-title":"Random Forest Missing Data Algorithms","volume":"10","author":"Tang","year":"2017","journal-title":"Stat Anal Data Min"},{"key":"10.1016\/j.cmpb.2026.109572_bib0022","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jclinepi.2014.06.018","article-title":"A new framework to enhance the interpretation of external validation studies of clinical prediction models","volume":"68","author":"Debray","year":"2015","journal-title":"J. Clin. Epidemiol."},{"key":"10.1016\/j.cmpb.2026.109572_bib0023","doi-asserted-by":"crossref","first-page":"35","DOI":"10.4266\/acc.2021.01193","article-title":"Brain-lung interaction: a vicious cycle in traumatic brain injury","volume":"37","author":"Chac\u00f3n-Aponte","year":"2022","journal-title":"Acute Crit Care"},{"key":"10.1016\/j.cmpb.2026.109572_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.jstrokecerebrovasdis.2019.104556","article-title":"Association of Early Oxygenation Levels with Mortality in Acute Ischemic Stroke - A Retrospective Cohort Study","volume":"29","author":"Akca","year":"2020","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"10.1016\/j.cmpb.2026.109572_bib0025","article-title":"Association Between Oxygen Partial Pressure Trajectories and Short-Term Outcomes in Patients With Hemorrhagic Brain Injury","volume":"8","author":"Cai","year":"2021","journal-title":"Front Med (Lausanne)"},{"key":"10.1016\/j.cmpb.2026.109572_bib0026","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1089\/neu.2009.0940","article-title":"Both hypoxemia and extreme hyperoxemia may be detrimental in patients with severe traumatic brain injury","volume":"26","author":"Davis","year":"2009","journal-title":"J Neurotrauma"},{"key":"10.1016\/j.cmpb.2026.109572_bib0027","doi-asserted-by":"crossref","first-page":"591","DOI":"10.4103\/jnrp.jnrp_265_17","article-title":"Predictors of In-hospital Mortality of Acute Ischemic Stroke in Adult Population","volume":"8","author":"Shah","year":"2017","journal-title":"J Neurosci Rural Pract"},{"key":"10.1016\/j.cmpb.2026.109572_bib0028","doi-asserted-by":"crossref","DOI":"10.1007\/s12028-024-01970-y","article-title":"Fast and Deep Breathing in Acute Brain Injury","author":"Wijdicks","year":"2024","journal-title":"Neurocrit Care"},{"key":"10.1016\/j.cmpb.2026.109572_bib0029","doi-asserted-by":"crossref","DOI":"10.1183\/13993003.02175-2018","article-title":"Mean nocturnal respiratory rate predicts cardiovascular and all-cause mortality in community-dwelling older men and women","volume":"54","author":"Baumert","year":"2019","journal-title":"Eur Respir J"},{"key":"10.1016\/j.cmpb.2026.109572_bib0030","article-title":"The neuroprotective effects of normobaric oxygen therapy after stroke","volume":"30","author":"Li","year":"2024","journal-title":"CNS Neurosci Ther"},{"key":"10.1016\/j.cmpb.2026.109572_bib0031","first-page":"1312","article-title":"Cerebral Venous Oxygen Saturation in Hypoperfusion Regions May Become a New Imaging Indicator to Predict the Clinical Outcome of Stroke","volume":"12","author":"Cao","year":"2022","journal-title":"Life (Basel)"},{"key":"10.1016\/j.cmpb.2026.109572_bib0032","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.2147\/NDT.S515306","article-title":"The Roles of Heart Rate Variability in Cerebral Stroke","volume":"21","author":"Wang","year":"2025","journal-title":"Neuropsychiatr Dis Treat"},{"key":"10.1016\/j.cmpb.2026.109572_bib0033","doi-asserted-by":"crossref","DOI":"10.3389\/fendo.2025.1558352","article-title":"Unveiling the role of stress hyperglycemia in predicting mortality for critically ill hemorrhagic stroke patients: insights from MIMIC-IV","volume":"16","author":"Yue","year":"2025","journal-title":"Front Endocrinol (Lausanne)"},{"key":"10.1016\/j.cmpb.2026.109572_bib0034","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1016\/j.jstrokecerebrovasdis.2019.02.021","article-title":"Association of Hyponatremia and Risk of Short- and Long-Term Mortality in Patients with Stroke: A Systematic Review and Meta-Analysis","volume":"28","author":"Chen","year":"2019","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"10.1016\/j.cmpb.2026.109572_bib0035","article-title":"Frequency of Hyponatremia and Its Impact on Prognosis in Ischemic Stroke","volume":"15","author":"Khan","year":"2023","journal-title":"Cureus"},{"key":"10.1016\/j.cmpb.2026.109572_bib0036","doi-asserted-by":"crossref","first-page":"285","DOI":"10.14715\/cmb\/2024.70.2.40","article-title":"Clinical efficacy of early microsurgical clipping of tumor neck in the treatment of cerebral aneurysm rupture and bleeding and its impact on oxidative stress response and prognosis of patients","volume":"70","author":"Chen","year":"2024","journal-title":"Cell Mol Biol (Noisy-Le-Grand)"},{"key":"10.1016\/j.cmpb.2026.109572_bib0037","doi-asserted-by":"crossref","DOI":"10.3389\/fnut.2022.801667","article-title":"A High Serum Phosphate and Calcium-Phosphate Product Is Associated With Cerebral Small Vascular Disease in Patients With Stroke: A Real-World Study","volume":"9","author":"Lv","year":"2022","journal-title":"Front Nutr"},{"key":"10.1016\/j.cmpb.2026.109572_bib0038","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-00704-0","article-title":"Relationship of serum phosphorus level with acute cerebrovascular disease","volume":"15","author":"Ataman","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.cmpb.2026.109572_bib0039","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1186\/s13054-019-2662-8","article-title":"Mechanical ventilation in patients with acute ischaemic stroke: from pathophysiology to clinical practice","volume":"23","author":"Robba","year":"2019","journal-title":"Crit Care"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726003214?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726003214?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T16:59:04Z","timestamp":1788368344000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726003214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":39,"alternative-id":["S0169260726003214"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109572","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Interpretable Machine Learning Model Using Oxygenation and Respiratory Variability to Predict Hemorrhagic Stroke Mortality: A Multicenter Validation Study","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109572","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109572"}}