Browsing by Subject "Decision tree"

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    Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU
    (Elsevier, 2022) Elhazmi, Alyaa; Al-Omari, Awad; Sallam, Hend; Mufti, Hani N.; Rabie, Ahmed A.; Alshahrani, Mohammed; Mady, Ahmed; Alghamdi, Adnan; Altalaq, Ali; Azzam, Mohamed H.; Sindi, Anees; Kharaba, Ayman; Al-Aseri, Zohair A.; Almekhlafi, Ghaleb A.; Tashkandi, Wail; Alajmi, Saud A.; Faqihi, Fahad; Alharthy, Abdulrahman; Al-Tawfiq, Jaffar A.; Melibari, Rami Ghazi; Al-Hazzani, Waleed; Arabi, Yaseen M.; Medicine, School of Medicine
    Background: Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic. Methods: This was a prospective and multicenter cohort study involving 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses. Results: There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio. Conclusion: DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.