{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T08:07:26Z","timestamp":1792742846443,"version":"4.5.0"},"reference-count":52,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["22JR5RA1064"],"award-info":[{"award-number":["22JR5RA1064"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012899","name":"Lanzhou University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012899","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82460379"],"award-info":[{"award-number":["82460379"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018554","name":"Science and Technology Program of Gansu Province","doi-asserted-by":"publisher","award":["24JRRA934"],"award-info":[{"award-number":["24JRRA934"]}],"id":[{"id":"10.13039\/501100018554","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018554","name":"Science and Technology Program of Gansu Province","doi-asserted-by":"publisher","award":["23YFGA0037"],"award-info":[{"award-number":["23YFGA0037"]}],"id":[{"id":"10.13039\/501100018554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intensive Medicine"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.jointm.2025.10.010","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T04:34:38Z","timestamp":1768019678000},"page":"175-184","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU"],"prefix":"10.1016","volume":"6","author":[{"given":"Yunpeng","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhu","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10219","key":"10.1016\/j.jointm.2025.10.010_bib0001","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/S0140-6736(19)32989-7","article-title":"Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study","volume":"395","author":"Rudd","year":"2020","journal-title":"Lancet"},{"issue":"3","key":"10.1016\/j.jointm.2025.10.010_bib0002","doi-asserted-by":"crossref","first-page":"e209","DOI":"10.1097\/CCM.0000000000004155","article-title":"The epidemiology of sepsis in Chinese ICUs: a national cross-sectional survey","volume":"48","author":"Xie","year":"2020","journal-title":"Crit Care Med"},{"issue":"18","key":"10.1016\/j.jointm.2025.10.010_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e37336","article-title":"Risk factors for acute respiratory distress syndrome in sepsis patients: a meta-analysis","volume":"10","author":"Yin","year":"2024","journal-title":"Heliyon"},{"issue":"8","key":"10.1016\/j.jointm.2025.10.010_bib0004","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1001\/jama.2016.0289","article-title":"Developing a new definition and assessing new clinical criteria for septic shock: for the Third International Consensus Definitions for sepsis and Septic shock (Sepsis-3)","volume":"315","author":"Shankar-Hari","year":"2016","journal-title":"JAMA"},{"issue":"5","key":"10.1016\/j.jointm.2025.10.010_bib0005","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s00281-017-0639-8","article-title":"Cytokine storm and sepsis disease pathogenesis","volume":"39","author":"Chousterman","year":"2017","journal-title":"Semin Immunopathol"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0006","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41572-019-0069-0","article-title":"Acute respiratory distress syndrome","volume":"5","author":"Matthay","year":"2019","journal-title":"Nat Rev Dis Primers"},{"key":"10.1016\/j.jointm.2025.10.010_bib0007","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.thromres.2016.11.007","article-title":"Coagulation and sepsis","volume":"149","author":"Levi","year":"2017","journal-title":"Thromb Res"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0008","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1152\/physrev.00003.2019","article-title":"Mechanisms and consequences of oxygen and carbon dioxide sensing in mammals","volume":"100","author":"Cummins","year":"2020","journal-title":"Physiol Rev"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0009","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1093\/bja\/aer093","article-title":"Oxidative stress and mitochondrial dysfunction in sepsis","volume":"107","author":"Galley","year":"2011","journal-title":"Br J Anaesth"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0010","doi-asserted-by":"crossref","first-page":"66","DOI":"10.4161\/viru.26907","article-title":"The role of mitochondrial dysfunction in sepsis-induced multi-organ failure","volume":"5","author":"Singer","year":"2014","journal-title":"Virulence"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0011","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1186\/s13054-024-04948-6","article-title":"A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability","volume":"28","author":"Bomrah","year":"2024","journal-title":"Crit Care"},{"issue":"12","key":"10.1016\/j.jointm.2025.10.010_bib0012","doi-asserted-by":"crossref","DOI":"10.3390\/biomedicines12122892","article-title":"Machine learning models in sepsis outcome prediction for ICU patients: integrating routine laboratory tests-a systematic review","volume":"12","author":"Musat","year":"2024","journal-title":"Biomedicines"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0013","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s00134-023-07288-1","article-title":"Prediction of post-traumatic stress disorder in family members of ICU patients: a machine learning approach","volume":"50","author":"Dupont","year":"2024","journal-title":"Intensive Care Med"},{"issue":"11","key":"10.1016\/j.jointm.2025.10.010_bib0014","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1097\/CCM.0000000000006390","article-title":"Machine learning tools for acute respiratory distress syndrome detection and prediction","volume":"52","author":"Rubulotta","year":"2024","journal-title":"Crit Care Med"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0015","article-title":"Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation","volume":"14","author":"Xu","year":"2024","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0016","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s40001-023-01593-7","article-title":"Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation","volume":"29","author":"Zhou","year":"2024","journal-title":"Eur J Med Res"},{"issue":"3","key":"10.1016\/j.jointm.2025.10.010_bib0017","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s00134-019-05872-y","article-title":"Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy","volume":"46","author":"Fleuren","year":"2020","journal-title":"Intensive Care Med"},{"issue":"7","key":"10.1016\/j.jointm.2025.10.010_bib0018","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1007\/s00134-020-06045-y","article-title":"Machine learning in intensive care medicine: ready for take-off?","volume":"46","author":"Fleuren","year":"2020","journal-title":"Intensive Care Med"},{"issue":"7","key":"10.1016\/j.jointm.2025.10.010_bib0019","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1513\/AnnalsATS.201803-205ED","article-title":"Boosting clinical decision-making: machine learning for intensive care unit discharge","volume":"15","author":"Cosgriff","year":"2018","journal-title":"Ann Am Thorac Soc"},{"key":"10.1016\/j.jointm.2025.10.010_bib0020","doi-asserted-by":"crossref","DOI":"10.2196\/48330","article-title":"Comparing open-access database and traditional intensive care studies using machine learning: bibliometric analysis study","volume":"26","author":"Ke","year":"2024","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.jointm.2025.10.010_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2022.104394","article-title":"Sepsis biomarkers and diagnostic tools with a focus on machine learning","volume":"86","author":"Komorowski","year":"2022","journal-title":"EBioMedicine"},{"key":"10.1016\/j.jointm.2025.10.010_bib0022","doi-asserted-by":"crossref","first-page":"39707","DOI":"10.1109\/ACCESS.2021.3064084","article-title":"Improving the prediction of heart failure patients\u2019 survival using SMOTE and effective data mining techniques","volume":"9","author":"Ishaq","year":"2021","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.jointm.2025.10.010_bib0023","first-page":"1073","article-title":"Summary of research on SMOTE oversampling and its improved algorithms","volume":"14","author":"Shi","year":"2019","journal-title":"CAAI Transact Intelligent Syst"},{"issue":"2","key":"10.1016\/j.jointm.2025.10.010_bib0024","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1097\/CCM.0000000000006135","article-title":"Surviving sepsis campaign research priorities 2023","volume":"52","author":"De Backer","year":"2024","journal-title":"Crit Care Med"},{"issue":"16","key":"10.1016\/j.jointm.2025.10.010_bib0025","doi-asserted-by":"crossref","first-page":"9010","DOI":"10.3390\/ijms25169010","article-title":"Sepsis biomarkers: advancements and clinical applications-a narrative review","volume":"25","author":"He","year":"2024","journal-title":"Int J Mol Sci"},{"key":"10.1016\/j.jointm.2025.10.010_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.intimp.2025.114111","article-title":"Regulated programmed cell death in sepsis associated acute lung injury: from pathogenesis to therapy","volume":"148","author":"Deng","year":"2025","journal-title":"Int Immunopharmacol"},{"issue":"14","key":"10.1016\/j.jointm.2025.10.010_bib0027","doi-asserted-by":"crossref","first-page":"7770","DOI":"10.3390\/ijms25147770","article-title":"From molecular mechanisms to clinical therapy: understanding sepsis-induced multiple organ dysfunction","volume":"25","author":"Srdi\u0107","year":"2024","journal-title":"Int J Mol Sci"},{"issue":"4","key":"10.1016\/j.jointm.2025.10.010_bib0028","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.chest.2020.04.025","article-title":"Fluid response evaluation in sepsis hypotension and shock: a randomized clinical trial","volume":"158","author":"Douglas","year":"2020","journal-title":"Chest"},{"key":"10.1016\/j.jointm.2025.10.010_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpeds.2024.114384","article-title":"Cardiopulmonary physiology of hypoxemic respiratory failure among preterm infants with septic shock","volume":"278","author":"Kharrat","year":"2025","journal-title":"J Pediatr"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0030","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1186\/s12879-023-08614-0","article-title":"Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis","volume":"23","author":"Yang","year":"2023","journal-title":"BMC Infect Dis"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0031","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1186\/s12879-024-10380-6","article-title":"Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis","volume":"24","author":"Lv","year":"2024","journal-title":"BMC Infect Dis"},{"key":"10.1016\/j.jointm.2025.10.010_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106466","article-title":"An explainable knowledge distillation method with XGBoost for ICU mortality prediction","volume":"152","author":"Liu","year":"2023","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jointm.2025.10.010_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2023.101970","article-title":"Subphenotyping heterogeneous patients with chronic critical illness to guide individualised fluid balance treatment using machine learning: a retrospective cohort study","volume":"59","author":"Liu","year":"2023","journal-title":"EClinicalMedicine"},{"key":"10.1016\/j.jointm.2025.10.010_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.iccn.2024.103715","article-title":"Prediction model of pressure injury occurrence in diabetic patients during ICU hospitalization\u2013XGBoost machine learning model can be interpreted based on SHAP","volume":"83","author":"Xu","year":"2024","journal-title":"Intensive Crit Care Nurs"},{"key":"10.1016\/j.jointm.2025.10.010_bib0035","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"Natekin","year":"2013","journal-title":"Front Neurorobot"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0036","doi-asserted-by":"crossref","DOI":"10.1080\/0886022X.2023.2215329","article-title":"Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care","volume":"45","author":"Wang","year":"2023","journal-title":"Ren Fail"},{"issue":"5","key":"10.1016\/j.jointm.2025.10.010_bib0037","doi-asserted-by":"crossref","DOI":"10.1177\/03000605241247696","article-title":"Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns","volume":"52","author":"Liu","year":"2024","journal-title":"J Int Med Res"},{"key":"10.1016\/j.jointm.2025.10.010_bib0038","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.2147\/IJGM.S302795","article-title":"Comparison of prediction models for acute kidney injury among patients with hepatobiliary malignancies based on XGBoost and LASSO-logistic algorithms","volume":"14","author":"Zhang","year":"2021","journal-title":"Int J Gen Med"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0039","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1080\/0886022X.2022.2107542","article-title":"Predicting renal function recovery and short-term reversibility among acute kidney injury patients in the ICU: comparison of machine learning methods and conventional regression","volume":"44","author":"Zhao","year":"2022","journal-title":"Ren Fail"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0040","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"issue":"10","key":"10.1016\/j.jointm.2025.10.010_bib0041","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable machine-learning predictions for the prevention of hypoxaemia during surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.jointm.2025.10.010_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhazmat.2025.137382","article-title":"Beyond XGBoost and SHAP: unveiling true feature importance","volume":"488","author":"Takefuji","year":"2025","journal-title":"J Hazard Mater"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0043","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1186\/s12911-020-01271-2","article-title":"Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU","volume":"20","author":"Kong","year":"2020","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0044","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1038\/s41598-023-49890-9","article-title":"Deep learning-based prediction of in-hospital mortality for sepsis","volume":"14","author":"Yong","year":"2024","journal-title":"Sci Rep"},{"issue":"9","key":"10.1016\/j.jointm.2025.10.010_bib0045","first-page":"e527","article-title":"Linear association between hypoalbuminemia and increased risk of acute respiratory distress syndrome in critically ill adults","volume":"3","author":"McNeil","year":"2021","journal-title":"Crit Care Explor"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0046","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10238-024-01545-3","article-title":"Putative biomarkers of hepatic dysfunction in critically ill sepsis patients","volume":"25","author":"Van Nynatten","year":"2025","journal-title":"Clin Exp Med"},{"issue":"3","key":"10.1016\/j.jointm.2025.10.010_bib0047","doi-asserted-by":"crossref","DOI":"10.1177\/00368504241274023","article-title":"The role of serum albumin in critical illness, predicting poor outcomes, and exploring the therapeutic potential of albumin supplementation","volume":"107","author":"Rabi","year":"2024","journal-title":"Sci Prog"},{"issue":"2","key":"10.1016\/j.jointm.2025.10.010_bib0048","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/jpen.1451","article-title":"Hypoalbuminemia: pathogenesis and clinical significance","volume":"43","author":"Soeters","year":"2019","journal-title":"JPEN J Parenter Enteral Nutr"},{"issue":"9","key":"10.1016\/j.jointm.2025.10.010_bib0049","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000019352","article-title":"Risk of acute respiratory failure among hospitalized patients with various admission serum albumin levels: a cohort study","volume":"99","author":"Thongprayoon","year":"2020","journal-title":"Medicine"},{"key":"10.1016\/j.jointm.2025.10.010_bib0050","article-title":"Mortality time frame variability in septic shock clinical trials: a systematic review","author":"Laserna","year":"2025","journal-title":"Med Intensiva (Engl Ed)"},{"issue":"1","key":"10.1016\/j.jointm.2025.10.010_bib0051","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1038\/s41598-023-28824-5","article-title":"Clinical relevance of timing of assessment of ICU mortality in patients with moderate-to-severe acute respiratory distress syndrome","volume":"13","author":"Villar","year":"2023","journal-title":"Sci Rep"},{"issue":"12","key":"10.1016\/j.jointm.2025.10.010_bib0052","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0112224","article-title":"Long term outcomes following hospital admission for sepsis using relative survival analysis: a prospective cohort study of 1092 patients with 5 year follow up","volume":"9","author":"Davis","year":"2014","journal-title":"PLoS One"}],"container-title":["Journal of Intensive Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667100X25000957?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667100X25000957?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T05:35:47Z","timestamp":1776231347000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667100X25000957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["S2667100X25000957"],"URL":"https:\/\/doi.org\/10.1016\/j.jointm.2025.10.010","relation":{},"ISSN":["2667-100X"],"issn-type":[{"value":"2667-100X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU","name":"articletitle","label":"Article Title"},{"value":"Journal of Intensive Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jointm.2025.10.010","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V. on behalf of Chinese Medical Association.","name":"copyright","label":"Copyright"}]}}