{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:50:41Z","timestamp":1792785041127,"version":"4.5.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003196","name":"Ministero della Salute","doi-asserted-by":"publisher","award":["Ricerca Corrente"],"award-info":[{"award-number":["Ricerca Corrente"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]},{"name":"5x1000","award":["SeeABI"],"award-info":[{"award-number":["SeeABI"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-46040-9","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:47:51Z","timestamp":1775119671000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A machine learning model for post-operative sepsis prediction in acute surgical patients: a multi-centre, prospective study"],"prefix":"10.1038","volume":"16","author":[{"given":"Pietro","family":"Fransvea","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piergiuseppe","family":"Liuzzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gianluca","family":"Costa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Sanguinetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Sganga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"issue":"3","key":"46040_CR1","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1164\/rccm.201504-0781OC","volume":"193","author":"C Fleischmann","year":"2016","unstructured":"Fleischmann, C. et al. Assessment of global incidence and mortality of hospital-treated sepsis. current estimates and limitations. Am. J. Respir Crit. Care Med. 193 (3), 259\u2013272. https:\/\/doi.org\/10.1164\/rccm.201504-0781OC (2016).","journal-title":"Am. J. Respir Crit. Care Med."},{"issue":"6","key":"46040_CR2","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.amjsurg.2009.05.025","volume":"198","author":"LJ Moore","year":"2009","unstructured":"Moore, L. J., Moore, F. A., Jones, S. L., Xu, J. & Bass, B. L. Sepsis in general surgery: A deadly complication. Am. J. Surg. 198 (6), 868\u2013874. https:\/\/doi.org\/10.1016\/j.amjsurg.2009.05.025 (2009).","journal-title":"Am. J. Surg."},{"issue":"1","key":"46040_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1089\/sur.2008.046","volume":"10","author":"TR Vogel","year":"2009","unstructured":"Vogel, T. R., Dombrovskiy, V. Y. & Lowry, S. F. Trends in postoperative sepsis: are we improving outcomes? Surg. Infect. 10 (1), 71\u201378. https:\/\/doi.org\/10.1089\/sur.2008.046 (2009).","journal-title":"Surg. Infect."},{"issue":"9134853","key":"46040_CR4","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/RBME.2020.3007816","volume":"14","author":"F Shamout","year":"2021","unstructured":"Shamout, F., Zhu, T. & Clifton, D. A. Machine learning for clinical outcome prediction. IEEE Rev. Biomed. Eng. 14 (9134853), 116\u2013126. https:\/\/doi.org\/10.1109\/RBME.2020.3007816 (2021).","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"46040_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1097\/SLA.0000000000002693","volume":"268","author":"DA Hashimoto","year":"2018","unstructured":"Hashimoto, D. A., Rosman, G., Rus, D. & Meireles, O. R. Artificial intelligence in surgery: Promises and perils. Ann. Surg. 268 (1), 70\u201376. https:\/\/doi.org\/10.1097\/SLA.0000000000002693 (2018).","journal-title":"Ann. Surg."},{"key":"46040_CR6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s13000-021-01085-4","volume":"16","author":"Z Ahmad","year":"2021","unstructured":"Ahmad, Z., Rahim, S., Zubair, M. & Abdul-Ghafar, J. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagn. Pathol. 16, 24. https:\/\/doi.org\/10.1186\/s13000-021-01085-4 (2021).","journal-title":"Diagn. Pathol."},{"key":"46040_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.5815\/ijisa.2019.06.02","volume":"11","author":"A Pathak","year":"2019","unstructured":"Pathak, A., Pandey, M. & Rautaray, S. Adaptive Model for Dynamic and Temporal Topic Modeling from Big Data using Deep Learning Architecture. Int. J. Intell. Syst. Appl. 11, 13\u201327. https:\/\/doi.org\/10.5815\/ijisa.2019.06.02 (2019).","journal-title":"Int. J. Intell. Syst. Appl."},{"issue":"1","key":"46040_CR8","doi-asserted-by":"publisher","first-page":"e0279540","DOI":"10.1371\/journal.pone.0279540","volume":"18","author":"B Langenberger","year":"2023","unstructured":"Langenberger, B., Schulte, T. & Groene, O. The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data. PLOS ONE. 18 (1), e0279540. https:\/\/doi.org\/10.1371\/journal.pone.0279540 (2023).","journal-title":"PLOS ONE"},{"issue":"10","key":"46040_CR9","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.3390\/healthcare10101920","volume":"10","author":"A Atalan","year":"2022","unstructured":"Atalan, A., \u015eahin, H. & Atalan, Y. A. Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources. Healthcare 10 (10), 1920. https:\/\/doi.org\/10.3390\/healthcare10101920 (2022).","journal-title":"Healthcare"},{"key":"46040_CR10","doi-asserted-by":"publisher","first-page":"106954","DOI":"10.1016\/j.ijsu.2022.106954","volume":"107","author":"P Fransvea","year":"2022","unstructured":"Fransvea, P. et al. Study and validation of an explainable machine learning-based mortality prediction following emergency surgery in the elderly: A prospective observational study. Int. J. Surg. Lond. Engl. 107, 106954. (2022).  https:\/\/doi.org\/10.1016\/j.ijsu.2022.106954","journal-title":"Int. J. Surg. Lond. Engl."},{"key":"46040_CR11","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2015","unstructured":"Zou, H. & Hastie, T. Regularization and Variable Selection via the Elastic Nets. J. R Stat. Soc. B. 67, 301\u2013320 (2015).","journal-title":"J. R Stat. Soc. B"},{"key":"46040_CR12","doi-asserted-by":"publisher","unstructured":"Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning (Springer, 2001). https:\/\/doi.org\/10.1007\/978-0-387-21606-5","DOI":"10.1007\/978-0-387-21606-5"},{"issue":"3","key":"46040_CR13","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.surg.2020.07.045","volume":"169","author":"C Bunn","year":"2021","unstructured":"Bunn, C. et al. Application of machine learning to the prediction of postoperative sepsis after appendectomy. Surgery 169 (3), 671\u2013677. https:\/\/doi.org\/10.1016\/j.surg.2020.07.045 (2021).","journal-title":"Surgery"},{"issue":"3","key":"46040_CR14","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1111\/acem.12876","volume":"23","author":"RA Taylor","year":"2016","unstructured":"Taylor, R. A. et al. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. Acad. Emerg. Med. Off J. Soc. Acad. Emerg. Med. 23 (3), 269\u2013278. https:\/\/doi.org\/10.1111\/acem.12876 (2016).","journal-title":"Acad. Emerg. Med. Off J. Soc. Acad. Emerg. Med."},{"issue":"5","key":"46040_CR15","doi-asserted-by":"publisher","first-page":"e0155705","DOI":"10.1371\/journal.pone.0155705","volume":"11","author":"P Thottakkara","year":"2016","unstructured":"Thottakkara, P. et al. Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications. PLoS ONE. 11 (5), e0155705. https:\/\/doi.org\/10.1371\/journal.pone.0155705 (2016).","journal-title":"PLoS ONE"},{"issue":"6","key":"46040_CR16","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/j.jamcollsurg.2021.02.009","volume":"232","author":"MW El Hechi","year":"2021","unstructured":"El Hechi, M. W. et al. Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients. J. Am. Coll. Surg. 232 (6), 912\u2013919e1. https:\/\/doi.org\/10.1016\/j.jamcollsurg.2021.02.009 (2021).","journal-title":"J. Am. Coll. Surg."},{"issue":"70","key":"46040_CR17","first-page":"2079","volume":"11","author":"GC Cawley","year":"2010","unstructured":"Cawley, G. C. & Talbot, N. L. C. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. J. Mach. Learn. Res. 11 (70), 2079\u20132107 (2010).","journal-title":"J. Mach. Learn. Res."},{"key":"46040_CR18","doi-asserted-by":"publisher","unstructured":"Caruana, R., Munson, A. & Niculescu-Mizil, A. Getting the Most Out of Ensemble Selection. In: Sixth International Conference on Data Mining (ICDM\u201906). :828\u2013833. (2006). https:\/\/doi.org\/10.1109\/ICDM.2006.76","DOI":"10.1109\/ICDM.2006.76"},{"key":"46040_CR19","doi-asserted-by":"publisher","unstructured":"Dietterich, T. G. Ensemble Methods in Machine Learning. In: Multiple Classifier Systems. Lecture Notes in Computer Science. Springer; :1\u201315. https:\/\/doi.org\/10.1007\/3-540-45014-9_1 (2000).","DOI":"10.1007\/3-540-45014-9_1"},{"issue":"1","key":"46040_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach, L. Ensemble-based classifiers. Artif. Intell. Rev. 33 (1), 1\u201339. https:\/\/doi.org\/10.1007\/s10462-009-9124-7 (2010).","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"46040_CR21","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s13304-018-0511-y","volume":"70","author":"G Costa","year":"2018","unstructured":"Costa, G., Massa, G. & Elderly Risk Assessment for Surgical Outcome) Collaborative Study Group. Frailty and emergency surgery in the elderly: protocol of a prospective, multicenter study in Italy for evaluating perioperative outcome (The FRAILESEL Study). Updat Surg. 70 (1), 97\u2013104. https:\/\/doi.org\/10.1007\/s13304-018-0511-y (2018).","journal-title":"Updat Surg."},{"key":"46040_CR22","doi-asserted-by":"publisher","first-page":"106165","DOI":"10.1016\/j.ijsu.2021.106165","volume":"96","author":"G Mathew","year":"2021","unstructured":"Mathew, G. et al. Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery. Int. J. Surg. Lond. Engl. 96, 106165. (2021). https:\/\/doi.org\/10.1016\/j.ijsu.2021.106165","journal-title":"Int. J. Surg. Lond. Engl."},{"issue":"8","key":"46040_CR23","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","volume":"315","author":"M Singer","year":"2016","unstructured":"Singer, M. et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315 (8), 801\u2013810. https:\/\/doi.org\/10.1001\/jama.2016.0287 (2016).","journal-title":"JAMA"},{"key":"46040_CR24","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357. https:\/\/doi.org\/10.1613\/jair.953 (2002).","journal-title":"J. Artif. Intell. Res."},{"key":"46040_CR25","unstructured":"Akiba, T. et al. A Next-generation Hyperparameter Optimization Framework. In: Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (2019)."},{"issue":"1","key":"46040_CR26","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory. 13 (1), 21\u201327. https:\/\/doi.org\/10.1109\/TIT.1967.1053964 (1967).","journal-title":"IEEE Trans. Inf. Theory"},{"key":"46040_CR27","unstructured":"Leo, B. Classification And Regression Trees (1st Ed) (Routledge, 1994)."},{"key":"46040_CR28","doi-asserted-by":"publisher","first-page":"106345","DOI":"10.1016\/j.cmpb.2021.106345","volume":"209","author":"A Mannini","year":"2021","unstructured":"Mannini, A. et al. Data-driven prediction of decannulation probability and timing in patients with severe acquired brain injury. Comput. Methods Programs Biomed. 209, 106345. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106345 (2021).","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46040-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46040-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46040-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T23:15:17Z","timestamp":1779232517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46040-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["46040"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-46040-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"19 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15651"}}