{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:20:12Z","timestamp":1772166012097,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-03113-5","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:17:25Z","timestamp":1754903845000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region"],"prefix":"10.1186","volume":"25","author":[{"given":"Radu","family":"Spaimoc","sequence":"first","affiliation":[]},{"given":"Jordi","family":"Mateo","sequence":"additional","affiliation":[]},{"given":"Francesc","family":"Solsona","sequence":"additional","affiliation":[]},{"given":"Alfredo","family":"Jover-S\u00e1enz","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"Barcenilla","sequence":"additional","affiliation":[]},{"given":"Mar\u00eda","family":"Ram\u00edrez-Hidalgo","sequence":"additional","affiliation":[]},{"given":"Marcos","family":"Serrano","sequence":"additional","affiliation":[]},{"given":"Miquel","family":"Mesas","sequence":"additional","affiliation":[]},{"given":"D\u00eddac","family":"Florensa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"issue":"12","key":"3113_CR1","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1111\/j.1469-0691.2010.03332.x","volume":"16","author":"G De Angelis","year":"2010","unstructured":"De Angelis G, Murthy A, Beyersmann J, Harbarth S. Estimating the impact of healthcare-associated infections on length of stay and costs. Clin Microbiol and Infect. 2010;16(12):1729\u201335.","journal-title":"Clin Microbiol And Infect"},{"issue":"2","key":"3113_CR2","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1097\/NAN.0000000000000313","volume":"42","author":"IR Douglas Scott","year":"2019","unstructured":"Douglas Scott IR, Steven DC, Kimberly JR. Understanding the economic impact of health care-associated infections, a cost perspective analysis. J Appl Psychol Infusion Nursing. 2019;42(2):61\u201369.","journal-title":"J Appl Psychol Infusion Nursing"},{"key":"3113_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40635-014-0035-9","volume":"3","author":"X Nuvials","year":"2015","unstructured":"Nuvials X, Palomar M, Alvarez-Lerma F, Olaechea P, Otero S, Uriona S, Catal\u00e1n M, Gimeno R, Gracia MP, Seijas I, et al. Health-care associated infections. patient characteristics and influence on the clinical outcome of patients admitted to icu. envin-helics registry data. Intensive Care Med Exp. 2015;3:1\u20132.","journal-title":"Intensive Care Med Exp"},{"issue":"1","key":"3113_CR4","first-page":"65","volume":"92","author":"KW Lobdell","year":"2012","unstructured":"Lobdell KW, Stamou S, Sanchez JA. Hospital-acquired infections. Surg Clinics. 2012;92(1):65\u201377.","journal-title":"Surg Clinics"},{"issue":"46","key":"3113_CR5","first-page":"20316","volume":"17","author":"BC Peter Zarb","year":"2012","unstructured":"Peter Zarb BC, Griskeviciene J, Muller A, Vankerckhoven V, Weist K, Goossens MM, Vaerenberg S, Hopkins S, Catry B, et al. The European centre for disease prevention and control (ecdc) pilot point prevalence survey of healthcare-associated infections and antimicrobial use. Eurosurveillance. 2012;17(46):20316.","journal-title":"Eurosurveillance"},{"issue":"19","key":"3113_CR6","doi-asserted-by":"publisher","first-page":"2100610","DOI":"10.2807\/1560-7917.ES.2022.27.19.2100610","volume":"27","author":"L Badia-Cebada","year":"2022","unstructured":"Badia-Cebada L, Pe\u00f1afiel J, Saliba P, Andr\u00e9s M, C\u00e0mara J, Domenech D, Jim\u00e9nez-Mart\u00efnez E, Marr\u00f3n A, Moreno E, Pomar V, et al. Trends in the epidemiology of catheter-related bloodstream infections; towards a paradigm shift, Spain, 2007 to 2019. Eurosurveillance. 2022;27(19):2100610.","journal-title":"Eurosurveillance"},{"key":"3113_CR7","doi-asserted-by":"crossref","unstructured":"Werneburg GT. Catheter-associated urinary tract infections, current challenges and future prospects. Res and Rep in Urology. 2022;109\u201333.","DOI":"10.2147\/RRU.S273663"},{"issue":"1","key":"3113_CR8","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/1460458216656471","volume":"24","author":"C Ehrentraut","year":"2018","unstructured":"Ehrentraut C, Ekholm M, Tanushi H, Tiedemann J, Dalianis H. Detecting hospital-acquired infections, a document classification approach using support vector machines and gradient tree boosting. Health Inf J. 2018;24(1):24\u201342.","journal-title":"Health Inf J"},{"issue":"1","key":"3113_CR9","doi-asserted-by":"publisher","first-page":"031","DOI":"10.1055\/s-0039-1677692","volume":"58","author":"S Rabhi","year":"2019","unstructured":"Rabhi S, Jakubowicz J, Metzger M-H. Deep learning versus conventional machine learning for detection of healthcare-associated infections in French clinical narratives. Methods Inf Med. 2019;58(1):031\u2013041.","journal-title":"Methods Inf Med"},{"key":"3113_CR10","doi-asserted-by":"crossref","unstructured":"Jacobson O, Dalianis H. Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections. Proceedings of the 15th workshop on biomedical natural language processing. 2016; 191\u201395.","DOI":"10.18653\/v1\/W16-2926"},{"key":"3113_CR11","unstructured":"Revuelta-Zamorano P, S\u00e1nchez A, Luis Rojo-\u00c1lvarez J, \u00c1lvarez-Rodr\u00efguez J, Ramos-L\u00f3pez J, Soguero-Ruiz C. Prediction of healthcare associated infections in an intensive care unit using machine learning and big data tools. Springer; 2016840\u201345; In XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, MEDICON 2016, March 31 April-31st 2nd 2016, Paphos, Cyprus."},{"issue":"24","key":"3113_CR12","doi-asserted-by":"publisher","first-page":"5287","DOI":"10.3390\/app9245287","volume":"9","author":"F S\u00e1nchez-Hern\u00e1ndez","year":"2019","unstructured":"S\u00e1nchez-Hern\u00e1ndez F, Ballesteros-Herr\u00e1ez JC, Kraiem MS, S\u00e1nchez-Barba M, Moreno-Garc\u00efa MN. Predictive modeling of icu healthcare-associated infections from imbalanced data. Using ensembles and a clustering-based undersampling approach. Appl Sci. 2019;9(24):5287.","journal-title":"Appl Sci"},{"issue":"2","key":"3113_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2431211.2431215","volume":"45","author":"S Lomax","year":"2013","unstructured":"Lomax S, Vadera S. A survey of cost-sensitive decision tree induction algorithms. ACM Comput Surv (CSUR). 2013;45(2):1\u201335.","journal-title":"ACM Comput Surv (CSUR)"},{"key":"3113_CR14","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.patcog.2016.08.023","volume":"62","author":"X Zhang","year":"2017","unstructured":"Zhang X, Yuxuan L, Kotagiri R, Lifang W, Tari Z, Cheriet M. Krnn, k rare-class nearest neighbour classification. Pattern Recognit. 2017;62:33\u201344.","journal-title":"Pattern Recognit"},{"key":"3113_CR15","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. Smote, synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"key":"3113_CR16","unstructured":"Han H, Wang W-Y, Mao B-H. Borderline-smote, a new over-sampling method in imbalanced data sets learning. International conference on intelligent computing. Springer; 2005878\u201387."},{"key":"3113_CR17","doi-asserted-by":"crossref","unstructured":"Haibo H, Bai Y, Garcia EA, Adasyn SL. Adaptive synthetic sampling approach for imbalanced learning. 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). Ieee; 20081322\u201328.","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"3113_CR18","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1177\/0962280220980484","volume":"30","author":"O Lyashevska","year":"2020","unstructured":"Lyashevska O, Malone F, Mccarthy E, Jens Fiehler J-HB, Morris L. Class imbalance in gradient boosting classification algorithms, application to experimental stroke data. Stat Methods Med Res. 2020;30:916\u201325, 12. https:\/\/doi.org\/10.1177\/0962280220980484.","journal-title":"Stat Methods Med Res"},{"issue":"70","key":"3113_CR19","doi-asserted-by":"publisher","first-page":"09","DOI":"10.1186\/s40537-020-00349-y","volume":"7","author":"J Tanha","year":"2020","unstructured":"Tanha J, Abdi Y, Samadi N, Razzaghi N, Asadpour M. Boosting methods for multi-class imbalanced data classification, an experimental review. J Big Data. 2020;7(70):09. https:\/\/doi.org\/10.1186\/s40537-020-00349-y.","journal-title":"J Big Data"},{"key":"3113_CR20","doi-asserted-by":"publisher","first-page":"09","DOI":"10.1016\/j.idc.2024.07.006","volume":"38","author":"E Scruggs-Wodkowski","year":"2024","unstructured":"Scruggs-Wodkowski E, Kidder I, Meddings J, Patel P. Urinary catheter-associated infections. Infect Disease Clinics Of North Am. 2024;38:09. https:\/\/doi.org\/10.1016\/j.idc.2024.07.006.","journal-title":"Infect Disease Clinics Of North Am"},{"issue":"5","key":"3113_CR21","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1086\/650482","volume":"50","author":"TM Hooton","year":"2010","unstructured":"Hooton TM, Bradley SF, Cardenas DD, Colgan R, Geerlings SE, Rice JC, Saint S, Schaeffer AJ, Tambayh PA, Tenke P, Nicolle LE. Diagnosis, prevention, and treatment of catheter-associated urinary tract infection in adults, 2009 international clinical practice guidelines from the infectious diseases society of America. Clin Infect Dis. 2010;50(5):625\u201363. https:\/\/doi.org\/10.1086\/650482, https:\/\/academic.oup.com\/cid\/article-pdf\/50\/5\/625\/34128221\/50-5-625.pdf.","journal-title":"Clin Infect Dis"},{"key":"3113_CR22","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1086\/651091","volume":"31","author":"C Gould","year":"2010","unstructured":"Gould C, Umscheid C, Agarwal R, Kuntz G, Pegues D. Guideline for prevention of catheter-associated urinary tract infections 2009. Infect Control Hosp Epidemiol. 2010;02;31:319\u201326. https:\/\/doi.org\/10.1086\/651091.","journal-title":"Infect Control Hosp Epidemiol"},{"issue":"1","key":"3113_CR23","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1109\/TVCG.2018.2865043","volume":"25","author":"D Dingen","year":"2018","unstructured":"Dingen D, van\u2019t Veer M, Houthuizen P, Mestrom EH, Korsten EH, Bouwman AR, Regressionexplorer JVW. Interactive exploration of logistic regression models with subgroup analysis. IEEE Trans Visual Comput Graphics. 2018;25(1):246\u201355.","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"23","key":"3113_CR24","doi-asserted-by":"publisher","first-page":"e26246","DOI":"10.1097\/MD.0000000000026246","volume":"100","author":"C Giang","year":"2021","unstructured":"Giang C, Calvert J, Rahmani K, Barnes G, Siefkas A, Green-Saxena A, Hoffman J, Mao Q, Das R. Predicting ventilator-associated pneumonia with machine learning. Medicine (Baltimore). 2021;100(23):e26246.","journal-title":"Medicine (Baltimore)"},{"key":"3113_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-0377-4","volume-title":"Interdisciplinary computing in Java programming volume 743","author":"S-C Wang","year":"2003","unstructured":"Wang S-C. Interdisciplinary computing in Java programming volume 743. Springer Science & Business Media; 2003."},{"issue":"1","key":"3113_CR26","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1177\/0165551516677946","volume":"44","author":"X Shuo","year":"2018","unstructured":"Shuo X. Bayesian na\u00efve bayes classifiers to text classification. J Inf Sci. 2018;44(1):48\u201359.","journal-title":"J Inf Sci"},{"key":"3113_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-78189-1","volume-title":"Modern multivariate statistical techniques volume 1","author":"AJ Izenman","year":"2008","unstructured":"Izenman AJ. Modern multivariate statistical techniques volume 1. Springer; 2008."},{"key":"3113_CR28","first-page":"1","volume":"36","author":"S Suthaharan","year":"2016","unstructured":"Suthaharan S. Machine learning models and algorithms for big data classification. Integr Ser Inf Syst. 2016;36:1\u201312.","journal-title":"Integr Ser Inf Syst"},{"key":"3113_CR29","doi-asserted-by":"crossref","unstructured":"Natekin A, Knoll A. Gradient boosting machines, a tutorial. Frontiers in neurorobotics. 2013;7:21.","DOI":"10.3389\/fnbot.2013.00021"},{"key":"3113_CR30","doi-asserted-by":"crossref","unstructured":"Chawla NV, Lazarevic A, Hall LO, Bowyer KW. Smoteboost, improving prediction of the minority class in boosting. Knowledge Discovery in Databases, PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik. Croatia: Springer; 2003. Proceedings 7 pages 107\u2013119, September 22\u201326, 2003.","DOI":"10.1007\/978-3-540-39804-2_12"},{"key":"3113_CR31","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost, a scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016; 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"issue":"4","key":"3113_CR32","first-page":"765","volume":"32","author":"ME Rupp","year":"2018","unstructured":"Rupp ME, Karnatak R. Intravascular catheter\u2013related bloodstream infections. Infect Disease Clinics. 2018;32(4):765\u201387.","journal-title":"Infect Disease Clinics"},{"issue":"1","key":"3113_CR33","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1093\/cid\/ciu239","volume":"59","author":"K Blot","year":"2014","unstructured":"Blot K, Bergs J, Vogelaers D, Blot S, Vandijck D. Prevention of central line\u2013associated bloodstream infections through quality improvement interventions, a systematic review and meta-analysis. Clin Infect Dis. 2014;59(1):96\u2013105.","journal-title":"Clin Infect Dis"},{"issue":"2","key":"3113_CR34","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.ajic.2013.09.023","volume":"42","author":"F Hammarskj\u00f6ld","year":"2014","unstructured":"Hammarskj\u00f6ld F, Berg S, Hanberger H, Taxbro K, Malmvall B-E. Sustained low incidence of central venous catheter-related infections over six years in a Swedish hospital with an active central venous catheter team. Am J Infect Control. 2014;42(2):122\u201328.","journal-title":"Am. J. Infect. Control"},{"issue":"3","key":"3113_CR35","doi-asserted-by":"publisher","first-page":"e0248636","DOI":"10.1371\/journal.pone.0248636","volume":"16","author":"J Kj\u00f8lseth M\u00f8ller","year":"2021","unstructured":"Kj\u00f8lseth M\u00f8ller J, S\u00f8rensen M, Hardahl C. Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning, a retrospective cohort study. PLoS One. 2021;16(3):e0248636.","journal-title":"PLoS One"},{"key":"3113_CR36","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.jcrc.2018.02.010","volume":"45","author":"JP Parreco","year":"2018","unstructured":"Parreco JP, Hidalgo AE, Badilla AD, Ilyas O, Rattan R. Predicting central line-associated bloodstream infections and mortality using supervised machine learning. J Crit Care. 2018;45:156\u201362.","journal-title":"J Crit Care"},{"issue":"1","key":"3113_CR37","first-page":"28","volume":"38","author":"JI Park","year":"2020","unstructured":"Park JI, Bliss DZ, Chi C-L, Delaney CW, Westra BL. Knowledge discovery with machine learning for hospital-acquired catheter-associated urinary tract infections. CIN Comput Inf Nurs. 2020;38(1):28\u201335.","journal-title":"CIN Comput Inf Nurs"},{"key":"3113_CR38","doi-asserted-by":"publisher","first-page":"e604","DOI":"10.7717\/peerj-cs.604","volume":"7","author":"P Gnip","year":"2021","unstructured":"Gnip P, Vokorokos L, Drot\u00e1r P. Selective oversampling approach for strongly imbalanced data. PeerJ Comput Sci. 2021;7:e604.","journal-title":"PeerJ Comput Sci"},{"issue":"12","key":"3113_CR39","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1046\/j.1365-2044.1998.00615.x","volume":"53","author":"S Ridley","year":"1998","unstructured":"Ridley S. Severity of illness scoring systems and performance appraisal. Anaesthesia. 1998;53(12):1185\u201394.","journal-title":"Anaesthesia"},{"issue":"107417","key":"3113_CR40","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.bspc.2024.107417","volume":"102","author":"Z Tarek","year":"2024","unstructured":"Tarek Z, Alhussan A, Khafaga D, El-Kenawy E-S, Elshewey A. A snake optimization algorithm-based feature selection framework for rapid detection of cardiovascular disease in its early stages. Biomed Signal Process Control. 2024;102(107417):12. https:\/\/doi.org\/10.1016\/j.bspc.2024.107417.","journal-title":"Biomed Signal Process Control"},{"key":"3113_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s11540-024-09760-x","author":"S Alzakari","year":"2024","unstructured":"Alzakari S, Alhussan A, El-Kenawy E-S, Elshewey A. Early detection of potato disease using an enhanced convolutional neural network-long short-term memory deep learning model. Eur Potato J. 2024, 07. https:\/\/doi.org\/10.1007\/s11540-024-09760-x.","journal-title":"Eur Potato J"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03113-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03113-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03113-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T20:13:01Z","timestamp":1757362381000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03113-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,11]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3113"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03113-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5791717\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,11]]},"assertion":[{"value":"8 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2025","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 study was approved by the Institutional Review Board (IRB), specifically the Clinical Investigation Ethical Committee (CEIC 21\/190-P, approval 23 March 2023) of IDIAP Jordi Gol. As it was a retrospective cohort study and the patients were blinded to the investigators, no written informed consent was necessary according to the CEIC. All statistical calculations were carried out in accordance with the relevant guidelines and regulations. The study was conducted in accordance with the ethical standards outlined in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"299"}}