Predictors of Mortality Using Machine Learning Decision Tree Algorithm in Critically Ill Adult Patients with COVID-19 Admitted to the ICU.
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Y. Arabi | J. Al-Tawfiq | Waleed Al-Hazzani | A. Alharthy | F. Faqihi | A. Mady | H. Mufti | A. Kharaba | G. Almekhlafi | A. Sindi | M. Alshahrani | Mohamed H. Azzam | W. Tashkandi | A. Rabie | A. Elhazmi | H. Sallam | Adnan Alghamdi | Ali Altalaq | Zohair A Al-Aseri | Saud A Alajmi | Rami Ghazi Melibari | A. Alomari | Zohair A. Al-Aseri | Saud A. Alajmi | Fahad Faqihi | Wail Tashkandi | Awad Alomari
[1] Xiaocheng Wu,et al. Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients , 2021, BMC Infectious Diseases.
[2] M. Antonelli,et al. Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units , 2021, Intensive Care Medicine.
[3] O. Gevaert,et al. AI-based analysis of CT images for rapid triage of COVID-19 patients , 2021, npj Digital Medicine.
[4] Dandan Li,et al. A Systematic Review and Meta-Analysis of Risk Factors Associated with Severity and Death in COVID-19 Patients , 2021, The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale.
[5] P. Horby,et al. How the COVID-19 pandemic will change the future of critical care , 2021, Intensive Care Medicine.
[6] T. Cook,et al. Mortality in patients admitted to intensive care with COVID‐19: an updated systematic review and meta‐analysis of observational studies , 2021, Anaesthesia.
[7] M. Curley,et al. Phenotypes and personalized medicine in the acute respiratory distress syndrome , 2020, Intensive Care Medicine.
[8] Aurélie Godard,et al. Critical care for COVID-19 during a humanitarian crisis—lessons learnt from Yemen , 2020, Critical Care.
[9] A. Robert,et al. A meta-analysis of potential biomarkers associated with severity of coronavirus disease 2019 (COVID-19) , 2020, Biomarker research.
[10] A. J. Agulló,et al. Clinical features, ventilatory management, and outcome of ARDS caused by COVID-19 are similar to other causes of ARDS , 2020, Intensive Care Medicine.
[11] Jun Yan,et al. Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method , 2020, Neural Computing and Applications.
[12] K. Bhaskaran,et al. Factors associated with COVID-19-related death using OpenSAFELY , 2020, Nature.
[13] S. Al-Youha,et al. Characteristics, risk factors and outcomes among the first consecutive 1096 patients diagnosed with COVID-19 in Kuwait , 2020, EClinicalMedicine.
[14] K. Ferdinand,et al. Association of cardiac biomarkers and comorbidities with increased mortality, severity, and cardiac injury in COVID‐19 patients: A meta‐regression and decision tree analysis , 2020, Journal of medical virology.
[15] M. Debray,et al. Admission chest CT score predicts 5-day outcome in patients with COVID-19 , 2020, Intensive Care Medicine.
[16] D. D. de Lange,et al. ICU beds: less is more? No , 2020, Intensive Care Medicine.
[17] V. Siguret,et al. Understanding pathophysiology of hemostasis disorders in critically ill patients with COVID-19 , 2020, Intensive Care Medicine.
[18] Mickaël Ohana,et al. High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter prospective cohort study , 2020, Intensive Care Medicine.
[19] M. Moghadami,et al. Epidemiological and clinical features of 2019 novel coronavirus diseases (COVID-19) in the South of Iran , 2020, BMC Infectious Diseases.
[20] J. Vincent,et al. Ethical aspects of the COVID-19 crisis: How to deal with an overwhelming shortage of acute beds , 2020, European heart journal. Acute cardiovascular care.
[21] D. Cucinotta,et al. WHO Declares COVID-19 a Pandemic , 2020, Acta bio-medica : Atenei Parmensis.
[22] Qiurong Ruan,et al. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China , 2020, Intensive Care Medicine.
[23] L. Mombaerts,et al. A machine learning-based model for survival prediction in patients with severe COVID-19 infection , 2020, medRxiv.
[24] G. Gao,et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.
[25] Dimitar Vassilev,et al. Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies , 2019, Inf..
[26] S. Deppen,et al. Developing prediction models for clinical use using logistic regression: an overview. , 2019, Journal of thoracic disease.
[27] Kevin Read,et al. A new hat for librarians: providing REDCap support to establish the library as a central data hub , 2018, Journal of the Medical Library Association : JMLA.
[28] Abbas Toloie Eshlaghy,et al. Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence , 2013 .
[29] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[30] K. Polderman,et al. Inter-observer variability in APACHE II scoring: effect of strict guidelines and training , 2001, Intensive Care Medicine.
[31] M. Capuzzo,et al. Validation of severity scoring systems SAPS II and APACHE II in a single-center population , 2000, Intensive Care Medicine.
[32] D. Cho,et al. Comparison of the APACHE III, APACHE II and Glasgow Coma Scale in acute head injury for prediction of mortality and functional outcome , 1997, Intensive Care Medicine.
[33] J. Vincent,et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.
[34] John Rand,et al. Using neural networks to diagnose cancer , 1991, Journal of Medical Systems.
[35] L. D. Shabas. Training algorithms for the decision tree method of pattern recognition , 1976, Cybernetics.
[36] Xi Chen,et al. A deep learning-based multi-model ensemble method for cancer prediction , 2018, Comput. Methods Programs Biomed..
[37] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[38] A. Mancini,et al. Validation of a severity of illness score (APACHE II) in a surgical intensive care unit , 2004, Intensive Care Medicine.
[39] R. Simes,et al. Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer. , 1985, Journal of chronic diseases.