Laxminarayan R. The overlooked pandemic of antimicrobial resistance. Lancet. 2022;399(10325):606–7.
Article CAS PubMed PubMed Central Google Scholar
Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, Harbarth S, Hindler JF, Kahlmeter G, Olsson-Liljequist B, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18(3):268–81.
Article CAS PubMed Google Scholar
Serra-Burriel M, Keys M, Campillo-Artero C, Agodi A, Barchitta M, Gikas A, Palos C, López-Casasnovas G. Impact of multi-drug resistant bacteria on economic and clinical outcomes of healthcare-associated infections in adults: systematic review and meta-analysis. PLoS ONE. 2020;15(1):e0227139.
Article CAS PubMed PubMed Central Google Scholar
De Waele JJ, Boelens J, Leroux-Roels I. Multidrug-resistant bacteria in ICU: fact or myth. Curr Opin Anaesthesiol. 2020;33(2):156–61.
Li ZJ, Wang KW, Liu B, Zang F, Zhang Y, Zhang WH, Zhou SM, Zhang YX. The distribution and source of MRDOs infection: a retrospective study in 8 ICUs, 2013–2019. Infect Drug Resist. 2021;14:4983–91.
Article PubMed PubMed Central Google Scholar
Mutters NT, Günther F, Frank U, Mischnik A. Costs and possible benefits of a two-tier infection control management strategy consisting of active screening for multidrug-resistant organisms and tailored control measures. J Hosp Infect. 2016;93(2):191–6.
Article CAS PubMed Google Scholar
Lagier JC, Edouard S, Pagnier I, Mediannikov O, Drancourt M, Raoult D. Current and past strategies for bacterial culture in clinical microbiology. Clin Microbiol Rev. 2015;28(1):208–36.
Article CAS PubMed PubMed Central Google Scholar
Gupta N, Limbago BM, Patel JB, Kallen AJ. Carbapenem-resistant Enterobacteriaceae: epidemiology and prevention. Clin Infect Dis. 2011;53(1):60–7.
Henderson DK. Managing methicillin-resistant staphylococci: a paradigm for preventing nosocomial transmission of resistant organisms. Am J Infect Control. 2006;34(5 Suppl 1):S46–54. discussion S64-73.
Perez S, Innes GK, Walters MS, Mehr J, Arias J, Greeley R, Chew D. Increase in Hospital-Acquired Carbapenem-Resistant Acinetobacter baumannii infection and colonization in an Acute Care Hospital during a Surge in COVID-19 admissions - New Jersey, February-July 2020. MMWR Morb Mortal Wkly Rep. 2020;69(48):1827–31.
Article CAS PubMed PubMed Central Google Scholar
Rajkomar A, Dean J, Kohane I. Machine learning in Medicine. N Engl J Med. 2019;380(14):1347–58.
Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1.
Article CAS PubMed PubMed Central Google Scholar
Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–73.
Liu X, Hu P, Yeung W, Zhang Z, Ho V, Liu C, Dumontier C, Thoral PJ, Mao Z, Cao D, et al. Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation. Lancet Digit Health. 2023;5(10):e657–67.
Article CAS PubMed Google Scholar
Li J, Liu S, Hu Y, Zhu L, Mao Y, Liu J. Predicting Mortality in Intensive Care Unit patients with heart failure using an interpretable machine learning model: Retrospective Cohort Study. J Med Internet Res. 2022;24(8):e38082.
Article PubMed PubMed Central Google Scholar
Fan Z, Jiang J, Xiao C, Chen Y, Xia Q, Wang J, Fang M, Wu Z, Chen F. Construction and validation of prognostic models in critically ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach. J Transl Med. 2023;21(1):406.
Article PubMed PubMed Central Google Scholar
Zhang Z. Multiple imputation with multivariate imputation by chained equation (MICE) package. Ann Transl Med. 2016;4(2):30.
PubMed PubMed Central Google Scholar
Permutation Importance with Multicollinear or Correlated Features. https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance_multicollinear.html.
Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26(10):1340–7.
Article CAS PubMed Google Scholar
LaValley MP. Logistic regression. Circulation. 2008;117(18):2395–9.
Zhang Z. Introduction to machine learning: k-nearest neighbors. Ann Transl Med. 2016;4(11):218.
Article PubMed PubMed Central Google Scholar
Verplancke T, Vanlooy S, Benoit D, Vansteelandt S, Depuydt P, Deturck F. Prediction of hospital mortality by support vector machine versus logistic regression in patients with a haematological malignancy admitted to the ICU. Crit Care 2008, 12(2 Supplement).
Li J, Tian Y, Zhu Y, Zhou T, Li J, Ding K, Li J. A multicenter random forest model for effective prognosis prediction in collaborative clinical research network. Artif Intell Med. 2020;103:101814.
Hou N, Li M, He L, Xie B, Wang L, Zhang R, Yu Y, Sun X, Pan Z, Wang K. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18(1):462.
Article CAS PubMed PubMed Central Google Scholar
Hirano Y, Kondo Y, Sueyoshi K, Okamoto K, Tanaka H. Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models. Resuscitation. 2021;158:49–56.
Jia W, Chen X-Y, Zhang H, Li-Dong. Xiong, Hang, Lei: Hyperparameter optimization for machine learning models based on bayesian optimization. J Electron Sci Technol 2019.
A SK, B DK, C MM. An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier - ScienceDirect. Int J Cogn Comput Eng. 2021;2:40–6.
Fitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. JAMA. 2015;313(4):409–10.
Article CAS PubMed Google Scholar
Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol. 2016;74:167–76.
Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DK, Newman SF, Kim J, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749–60.
Article PubMed PubMed Central Google Scholar
Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–6.
Mahajan P, Uddin S, Hajati F, Moni MA. Ensemble learning for Disease Prediction: a review. Healthc (Basel) 2023, 11(12).
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.
Article PubMed PubMed Central Google Scholar
Roimi M, Neuberger A, Shrot A, Paul M, Geffen Y, Bar-Lavie Y. Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms. Intensive Care Med. 2020;46(3):454–62.
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