{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:40:56Z","timestamp":1786120856859,"version":"build-2736575974"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":66,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.82304238"],"award-info":[{"award-number":["No.82304238"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key R&D Plan of Shaanxi Province","award":["No. 2025SF-YBXM-025"],"award-info":[{"award-number":["No. 2025SF-YBXM-025"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The pathological heterogeneity of sepsis makes it challenging for traditional scoring systems to balance early-warning sensitivity, dynamic progression characterization, and mechanistic interpretability. Developing a multi-objective optimization algorithm incorporating rheological properties to screen novel composite parameters from routine indicators, thereby constructing a precise sepsis assessment tool with both clinical feasibility and pathological interpretability, offers a new approach to inform clinical decisions and prognosis prediction for sepsis patients. This retrospective prognostic model development and validation study aimed to develop and externally validate novel composite indices for sepsis mortality prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using data from the eICU (derivation cohort,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20093,965) and MIMIC-IV (temporal validation cohort,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092,917) databases, we developed the Multi-Objective Non-Newtonian Fluid Optimization (MONNF) algorithm through physically inspired dynamic viscoelastic regulation, multi-target synergistic constraints, and medical knowledge-embedding strategies integrated with key pathological dimensions of sepsis. This algorithm screened novel indicators capturing the pathophysiological interplay among circulatory compromise, coagulopathy, and metabolic derangement. Subsequently, an Under-sampling Synchronous Evolutionary Ensemble (USEE) prediction model was established to validate its cross-center generalizability and clinical utility.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The initial phase of MONNF identified the Hypoxia-Coagulation-Hemoglobin Index (HCH; components: lactate, INR, hemoglobin) with a 28-day mortality ROC-AUC of 0.67, superior to SOFA (ROC-AUC\u2009=\u20090.63). The later phase constructed the Hepatic-Pulmonary-Metabolic Synergistic Index (HPMS; components: total bilirubin, PaO\u2082\/FiO\u2082, bicarbonate, albumin) with a ROC-AUC of 0.64. Risk inflection points of the new indicators revealed critical intervention windows (pnonlinear\u2009&lt;\u20090.001), while Boruta feature importance ranking confirmed their higher decision weight than traditional variables. The USEE model demonstrated optimal predictive performance in independent validation (ROC-AUC\u2009=\u20090.84, PR-AUC\u2009=\u20090.71).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this retrospective development and validation study, a novel optimization algorithm based on dynamic non-Newtonian fluid properties successfully uncovered composite indicators that profoundly characterize core pathological features of sepsis. This work provides an innovative solution to simplify clinical stratification and address the tripartite paradox in prognostic assessment: efficiency versus sensitivity versus interpretability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03609-8","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:55:05Z","timestamp":1780433705000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and external validation of the HCH and HPMS prognostic indices for sepsis: a retrospective model development study using a Multi-Objective Non-Newtonian Fluid optimization algorithm"],"prefix":"10.1186","volume":"26","author":[{"given":"Chengcheng","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfang","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"issue":"2","key":"3609_CR1","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1097\/ACO.0000000000000958","volume":"34","author":"C Chiu","year":"2021","unstructured":"Chiu C, Legrand M. Epidemiology of sepsis and septic shock. Curr Opin Anaesthesiol. 2021;34(2):71\u20136.","journal-title":"Curr Opin Anaesthesiol"},{"issue":"Suppl 2","key":"3609_CR2","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/s00063-023-01088-7","volume":"118","author":"C Fleischmann-Struzek","year":"2023","unstructured":"Fleischmann-Struzek C, Rudd K. Challenges of assessing the burden of sepsis. Med Klin Intensivmed Notfmed. 2023;118(Suppl 2):68\u201374.","journal-title":"Med Klin Intensivmed Notfmed"},{"issue":"Suppl 1","key":"3609_CR3","first-page":"67","volume":"61","author":"I Srzi\u0107","year":"2022","unstructured":"Srzi\u0107 I, Nesek Adam V, Tunji\u0107 Pejak D. Sepsis definition: what\u2019s new in the treatment guidelines. Acta Clin Croat. 2022;61(Suppl 1):67\u201372.","journal-title":"Acta Clin Croat"},{"issue":"8","key":"3609_CR4","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1080\/14787210.2023.2237192","volume":"21","author":"X Qiu","year":"2023","unstructured":"Qiu X, Lei YP, Zhou RX. SIRS, SOFA, qSOFA, and NEWS in the diagnosis of sepsis and prediction of adverse outcomes: a systematic review and meta-analysis. Expert Rev Anti Infect Ther. 2023;21(8):891\u2013900.","journal-title":"Expert Rev Anti Infect Ther"},{"issue":"1","key":"3609_CR5","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13049-019-0609-3","volume":"27","author":"Z Liu","year":"2019","unstructured":"Liu Z, Meng Z, Li Y, Zhao J, Wu S, Gou S, Wu H. Prognostic accuracy of the serum lactate level, the SOFA score and the qSOFA score for mortality among adults with Sepsis. Scand J Trauma Resusc Emerg Med. 2019;27(1):51.","journal-title":"Scand J Trauma Resusc Emerg Med"},{"issue":"1","key":"3609_CR6","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1186\/s12967-022-03364-0","volume":"20","author":"S Yue","year":"2022","unstructured":"Yue S, Li S, Huang X, Liu J, Hou X, Zhao Y, Niu D, Wang Y, Tan W, Wu J. Machine learning for the prediction of acute kidney injury in patients with sepsis. J Transl Med. 2022;20(1):215.","journal-title":"J Transl Med"},{"key":"3609_CR7","doi-asserted-by":"crossref","unstructured":"Kanadath A, Angel Arul Jothi J, Urolagin S. Multilevel multiobjective particle swarm optimization guided superpixel algorithm for histopathology image detection and segmentation. J Imaging. 2023;9(4).","DOI":"10.3390\/jimaging9040078"},{"issue":"6","key":"3609_CR8","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s10822-022-00455-4","volume":"36","author":"J Li","year":"2022","unstructured":"Li J, Li C, Sun J, Palade V. RDPSOVina: the random drift particle swarm optimization for protein-ligand docking. J Comput Aided Mol Des. 2022;36(6):415\u201325.","journal-title":"J Comput Aided Mol Des"},{"key":"3609_CR9","doi-asserted-by":"crossref","unstructured":"Habib M, Aljarah I, Faris H, Mirjalili S. Multi-objective particle swarm optimization: theory, literature review, and application in feature selection for medical diagnosis. In: Mirjalili S, Faris H, Aljarah I, editors. Evolutionary machine learning techniques: algorithms and applications. Singapore: Springer Singapore; 2020. p. 175\u2013201.","DOI":"10.1007\/978-981-32-9990-0_9"},{"key":"3609_CR10","doi-asserted-by":"crossref","unstructured":"Cao L, Chen H, Chen Y, Yue Y, Zhang X. Bio-inspired swarm intelligence optimization algorithm-aided hybrid TDOA\/AOA-based localization. Biomimetics (Basel). 2023;8(2).","DOI":"10.3390\/biomimetics8020186"},{"key":"3609_CR11","doi-asserted-by":"publisher","first-page":"5143757","DOI":"10.1155\/2022\/5143757","volume":"2022","author":"J Xu","year":"2022","unstructured":"Xu J, Tian F, Wang L, Miao Z. Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor. Contrast Media Mol Imaging. 2022;2022:5143757.","journal-title":"Contrast Media Mol Imaging"},{"key":"3609_CR12","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.eswa.2019.02.037","volume":"127","author":"M Alirezaei","year":"2019","unstructured":"Alirezaei M, Niaki STA, Niaki SAA. A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines. Expert Syst Appl. 2019;127:47\u201357.","journal-title":"Expert Syst Appl"},{"key":"3609_CR13","doi-asserted-by":"crossref","unstructured":"Mehta H, Parekh N, Mangrulkar R, Gawade A, Chavan P, Goje N. Enhancing feature selection using multi-objective optimization concept. In: Dey N, editor. Optimizing solutions for real-life problems. Singapore: Springer Nature Singapore; 2025. p. 257\u2013283.","DOI":"10.1007\/978-981-96-3216-9_10"},{"key":"3609_CR14","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, et al. TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.","journal-title":"BMJ"},{"key":"3609_CR15","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1186\/1471-2407-13-158","volume":"13","author":"SH Jafri","year":"2013","unstructured":"Jafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer. 2013;13:158.","journal-title":"BMC Cancer"},{"issue":"6","key":"3609_CR16","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1002\/hep.21178","volume":"43","author":"RK Sterling","year":"2006","unstructured":"Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, Torriani MSS, Dieterich FJ, Thomas DT. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV\/HCV coinfection. Hepatology. 2006;43(6):1317\u201325.","journal-title":"Hepatology"},{"issue":"1","key":"3609_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/102490790601300102","volume":"13","author":"TS Lam","year":"2006","unstructured":"Lam TS, Mak PSK, Siu WS, Lam MY, Cheung TF, Rainer TH. Validation of a Modified Early Warning Score (Mews) in Emergency Department Observation Ward Patients. Hong Kong J Emerg Med. 2006;13(1):24\u201330.","journal-title":"Hong Kong J Emerg Med"},{"issue":"10","key":"3609_CR18","doi-asserted-by":"publisher","first-page":"e1042","DOI":"10.1002\/iid3.1042","volume":"11","author":"Z Sun","year":"2023","unstructured":"Sun Z, Song Y, Li J, Li Y, Yu Y, Wang X. Potential biomarker for diagnosis and therapy of sepsis: Lactylation. Immun Inflamm Dis. 2023;11(10):e1042.","journal-title":"Immun Inflamm Dis"},{"issue":"1","key":"3609_CR19","doi-asserted-by":"publisher","first-page":"2482024","DOI":"10.1080\/07853890.2025.2482024","volume":"57","author":"CP Acharya","year":"2025","unstructured":"Acharya CP, Yadav A, Pokhrel S, Bastola S, Jha S. Prognostic significance of lactate\/albumin ratio in respiratory failure and sepsis. Ann Med. 2025;57(1):2482024.","journal-title":"Ann Med"},{"issue":"1","key":"3609_CR20","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1186\/s12933-023-02041-w","volume":"22","author":"R Zheng","year":"2023","unstructured":"Zheng R, Qian S, Shi Y, Lou C, Xu H, Pan J. Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):307.","journal-title":"Cardiovasc Diabetol"},{"key":"3609_CR21","doi-asserted-by":"publisher","first-page":"857368","DOI":"10.3389\/fpubh.2022.857368","volume":"10","author":"Z Lu","year":"2022","unstructured":"Lu Z, Tao G, Sun X, Zhang Y, Jiang M, Liu Y, Ling M, Zhang J, Xiao W, Hua T, et al. Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization. Front Public Health. 2022;10:857368.","journal-title":"Front Public Health"},{"issue":"1","key":"3609_CR22","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1515\/cclm-2022-0656","volume":"61","author":"SF Lin","year":"2023","unstructured":"Lin SF, Lin HA, Pan YH, Hou SK. A novel scoring system combining Modified Early Warning Score with biomarkers of monocyte distribution width, white blood cell counts, and neutrophil-to-lymphocyte ratio to improve early sepsis prediction in older adults. Clin Chem Lab Med. 2023;61(1):162\u201372.","journal-title":"Clin Chem Lab Med"},{"issue":"12","key":"3609_CR23","doi-asserted-by":"publisher","first-page":"2979","DOI":"10.1007\/s00345-022-04182-1","volume":"40","author":"X Hao","year":"2022","unstructured":"Hao X, Wang X, Ding H, Zheng S, Li Z, Yin H, Wang L, Luo J, Wei H. A model for sepsis prediction after retrograde intrarenal surgery and the use of the preoperative\/postoperative white blood cell ratio to predict progression from sepsis to septic shock. World J Urol. 2022;40(12):2979\u201390.","journal-title":"World J Urol"},{"issue":"3","key":"3609_CR24","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.ajem.2019.10.023","volume":"38","author":"Z Huang","year":"2020","unstructured":"Huang Z, Fu Z, Huang W, Huang K. Prognostic value of neutrophil-to-lymphocyte ratio in sepsis: A meta-analysis. Am J Emerg Med. 2020;38(3):641\u20137.","journal-title":"Am J Emerg Med"},{"key":"3609_CR25","doi-asserted-by":"publisher","first-page":"1336456","DOI":"10.3389\/fimmu.2024.1336456","volume":"15","author":"H Wu","year":"2024","unstructured":"Wu H, Cao T, Ji T, Luo Y, Huang J, Ma K. Predictive value of the neutrophil-to-lymphocyte ratio in the prognosis and risk of death for adult sepsis patients: a meta-analysis. Front Immunol. 2024;15:1336456.","journal-title":"Front Immunol"},{"issue":"1","key":"3609_CR26","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1186\/s13054-024-04935-x","volume":"28","author":"K Takkavatakarn","year":"2024","unstructured":"Takkavatakarn K, Oh W, Chan L, Hofer I, Shawwa K, Kraft M, Shah N, Kohli-Seth R, Nadkarni GN, Sakhuja A. Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis. Crit Care. 2024;28(1):156.","journal-title":"Crit Care"},{"issue":"9","key":"3609_CR27","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1007\/s00134-023-07138-0","volume":"49","author":"KC White","year":"2023","unstructured":"White KC, Serpa-Neto A, Hurford R, Clement P, Laupland KB, See E, McCullough J, White H, Shekar K, Tabah A, et al. Sepsis-associated acute kidney injury in the intensive care unit: incidence, patient characteristics, timing, trajectory, treatment, and associated outcomes. A multicenter, observational study. Intensive Care Med. 2023;49(9):1079\u201389.","journal-title":"Intensive Care Med"},{"issue":"10","key":"3609_CR28","doi-asserted-by":"publisher","first-page":"4235","DOI":"10.1007\/s00431-024-05682-5","volume":"183","author":"H Zhou","year":"2024","unstructured":"Zhou H. Total bilirubin level is associated with acute kidney injury in neonates admitted to the neonatal intensive care units: based on MIMIC-III database. Eur J Pediatr. 2024;183(10):4235\u201341.","journal-title":"Eur J Pediatr"},{"issue":"1","key":"3609_CR29","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.jcrc.2015.09.033","volume":"31","author":"S Yamano","year":"2016","unstructured":"Yamano S, Shimizu K, Ogura H, Hirose T, Hamasaki T, Shimazu T, Tasaki O. Low total cholesterol and high total bilirubin are associated with prognosis in patients with prolonged sepsis. J Crit Care. 2016;31(1):36\u201340.","journal-title":"J Crit Care"},{"issue":"6","key":"3609_CR30","doi-asserted-by":"publisher","first-page":"300060522110075","DOI":"10.1177\/03000605221100755","volume":"50","author":"T Li","year":"2022","unstructured":"Li T, Hu WQ, Li X, Zhang JP, Tan LZ, Yu LX, Gu HR, Shi ZY. Prognostic value of PaO(2)\/FiO(2), SOFA and D-dimer in elderly patients with sepsis. J Int Med Res. 2022;50(6):3000605221100755.","journal-title":"J Int Med Res"},{"issue":"1","key":"3609_CR31","doi-asserted-by":"publisher","first-page":"102","DOI":"10.2215\/CJN.04500422","volume":"18","author":"A Achanti","year":"2023","unstructured":"Achanti A, Szerlip HM. Acid-Base Disorders in the Critically Ill Patient. Clin J Am Soc Nephrol. 2023;18(1):102\u201312.","journal-title":"Clin J Am Soc Nephrol"},{"issue":"1","key":"3609_CR32","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s12882-021-02629-y","volume":"23","author":"S Sheng","year":"2022","unstructured":"Sheng S, Zhang YH, Ma HK, Huang Y. Albumin levels predict mortality in sepsis patients with acute kidney injury undergoing continuous renal replacement therapy: a secondary analysis based on a retrospective cohort study. BMC Nephrol. 2022;23(1):52.","journal-title":"BMC Nephrol"},{"key":"3609_CR33","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.hrtlng.2023.12.001","volume":"64","author":"J Zhu","year":"2024","unstructured":"Zhu J, Dong Y, Liao P, Yin X, He J, Guo L. Prognostic value of hemoglobin in patients with sepsis: A systematic review and meta-analysis. Heart Lung. 2024;64:93\u20139.","journal-title":"Heart Lung"},{"issue":"1","key":"3609_CR34","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10238-024-01450-9","volume":"24","author":"A He","year":"2024","unstructured":"He A, Liu J, Qiu J, Zhu X, Zhang L, Xu L, Xu J. Risk and mediation analyses of hemoglobin glycation index and survival prognosis in patients with sepsis. Clin Exp Med. 2024;24(1):183.","journal-title":"Clin Exp Med"},{"key":"3609_CR35","doi-asserted-by":"publisher","first-page":"1210219","DOI":"10.3389\/fimmu.2023.1210219","volume":"14","author":"D Cox","year":"2023","unstructured":"Cox D. Sepsis - it is all about the platelets. Front Immunol. 2023;14:1210219.","journal-title":"Front Immunol"},{"issue":"4","key":"3609_CR36","doi-asserted-by":"publisher","first-page":"741","DOI":"10.17305\/bb.2023.10135","volume":"24","author":"X Xu","year":"2024","unstructured":"Xu X, Wang Y, Tao Y, Dang W, Yang B, Li Y. The role of platelets in sepsis: A review. Biomol Biomed. 2024;24(4):741\u201352.","journal-title":"Biomol Biomed"},{"issue":"1","key":"3609_CR37","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s40560-023-00672-5","volume":"11","author":"T Iba","year":"2023","unstructured":"Iba T, Helms J, Connors JM, Levy JH. The pathophysiology, diagnosis, and management of sepsis-associated disseminated intravascular coagulation. J Intensive Care. 2023;11(1):24.","journal-title":"J Intensive Care"},{"issue":"1","key":"3609_CR38","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1038\/s41746-022-00704-8","volume":"5","author":"C Gao","year":"2022","unstructured":"Gao C, Zhang R, Chen X, Yao T, Song Q, Ye W, Li P, Wang Z, Yi D, Wu Y. Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission. NPJ Digit Med. 2022;5(1):161.","journal-title":"NPJ Digit Med"},{"issue":"12","key":"3609_CR39","doi-asserted-by":"publisher","first-page":"100916","DOI":"10.1016\/j.landig.2025.100916","volume":"7","author":"B Van Calster","year":"2025","unstructured":"Van Calster B, Collins GS, Vickers AJ, Wynants L, Kerr KF, Barre\u00f1ada L, Varoquaux G, Singh K, Moons KG, Hernandez-Boussard T, et al. Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance. Lancet Digit Health. 2025;7(12):100916.","journal-title":"Lancet Digit Health"},{"issue":"1","key":"3609_CR40","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1038\/s41746-025-01958-8","volume":"8","author":"S Hur","year":"2025","unstructured":"Hur S, Lee Y, Park J, Jeon YJ, Cho JH, Cho D, Lim D, Hwang W, Cha WC, Yoo J. Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour. NPJ Digit Med. 2025;8(1):578.","journal-title":"NPJ Digit Med"},{"issue":"11","key":"3609_CR41","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745\u201350.","journal-title":"Lancet Digit Health"},{"issue":"5","key":"3609_CR42","doi-asserted-by":"publisher","first-page":"616","DOI":"10.2105\/AJPH.2018.304337","volume":"108","author":"MA Hern\u00e1n","year":"2018","unstructured":"Hern\u00e1n MA. The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data. Am J Public Health. 2018;108(5):616\u20139.","journal-title":"Am J Public Health"},{"key":"3609_CR43","doi-asserted-by":"crossref","unstructured":"Nedeva C. Inflammation and cell death of the innate and adaptive immune system during sepsis. Biomolecules. 2021;11(7).","DOI":"10.3390\/biom11071011"},{"key":"3609_CR44","doi-asserted-by":"publisher","first-page":"637434","DOI":"10.3389\/fmed.2020.637434","volume":"7","author":"QY Zhao","year":"2020","unstructured":"Zhao QY, Liu LP, Luo JC, Luo YW, Wang H, Zhang YJ, Gui R, Tu GW, Luo Z. A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis. Front Med (Lausanne). 2020;7:637434.","journal-title":"Front Med (Lausanne)"},{"key":"3609_CR45","doi-asserted-by":"publisher","first-page":"107602962311950","DOI":"10.1177\/10760296231195089","volume":"29","author":"Y Li","year":"2023","unstructured":"Li Y, Li H, Wang Y, Guo J, Zhang D. Potential Biomarkers for Early Diagnosis, Evaluation, and Prognosis of Sepsis-Induced Coagulopathy. Clin Appl Thromb Hemost. 2023;29:10760296231195089.","journal-title":"Clin Appl Thromb Hemost"},{"issue":"1","key":"3609_CR46","doi-asserted-by":"publisher","first-page":"3662","DOI":"10.1038\/s41598-023-30235-5","volume":"13","author":"L Cui","year":"2023","unstructured":"Cui L, Bao J, Yu C, Zhang C, Huang R, Liu L, Shao M. Development of a nomogram for predicting 90-day mortality in patients with sepsis-associated liver injury. Sci Rep. 2023;13(1):3662.","journal-title":"Sci Rep"},{"issue":"1","key":"3609_CR47","doi-asserted-by":"publisher","first-page":"20881","DOI":"10.1038\/s41598-025-05876-3","volume":"15","author":"K Cai","year":"2025","unstructured":"Cai K, Yang X, Wang Z, Fu W, Liu H, Mahmoudi F. Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling. Sci Rep. 2025;15(1):20881.","journal-title":"Sci Rep"},{"key":"3609_CR48","doi-asserted-by":"publisher","first-page":"e55492","DOI":"10.2196\/55492","volume":"27","author":"A Campagner","year":"2025","unstructured":"Campagner A, Agnello L, Carobene A, Padoan A, Del Ben F, Locatelli M, Plebani M, Ognibene A, Lorubbio M, De Vecchi E, et al. Complete Blood Count and Monocyte Distribution Width-Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study. J Med Internet Res. 2025;27:e55492.","journal-title":"J Med Internet Res"},{"issue":"1","key":"3609_CR49","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1186\/s12911-024-02630-z","volume":"24","author":"J Gao","year":"2024","unstructured":"Gao J, Lu Y, Ashrafi N, Domingo I, Alaei K, Pishgar M. Prediction of sepsis mortality in ICU patients using machine learning methods. BMC Med Inf Decis Mak. 2024;24(1):228.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"1","key":"3609_CR50","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1186\/s12911-024-02655-4","volume":"24","author":"MS Rahman","year":"2024","unstructured":"Rahman MS, Islam KR, Prithula J, Kumar J, Mahmud M, Alam MF, Reaz MBI, Alqahtani A, Chowdhury MEH. Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3. BMC Med Inf Decis Mak. 2024;24(1):249.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"1","key":"3609_CR51","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1186\/s12911-023-02383-1","volume":"23","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Xu W, Yang P, Zhang A. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis. BMC Med Inf Decis Mak. 2023;23(1):283.","journal-title":"BMC Med Inf Decis Mak"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03609-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03609-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03609-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:19:27Z","timestamp":1786065567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03609-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,2]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3609"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03609-8","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,2]]},"assertion":[{"value":"6 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The eICU-CRD and MIMIC databases are publicly available and have been anonymized. They do not require further approval from an ethical committee.","order":1,"name":"Ethics","label":"Ethical approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"298"}}