Background and objectives The pandemic of novel coronavirus disease 2019 (COVID-19) has severely impacted human society with a massive death toll worldwide. There is an urgent need for early and reliable screening of COVID-19 patients to provide better and timely patient care and to combat the spread of the disease. In this context, recent studies have reported some key advantages of using routine blood tests for initial screening of COVID-19 patients. In this article, first we present a review of the emerging techniques for COVID-19 diagnosis using routine laboratory and/or clinical data. Then, we propose ERLX which is an ensemble learning model for COVID-19 diagnosis from routine blood tests. Method The proposed model uses three well-known diverse classifiers, extra trees, random forest and logistic regression, which have different architectures and learning characteristics at the first level, and then combines their predictions by using a second level extreme gradient boosting (XGBoost) classifier to achieve a better performance. For data preparation, the proposed methodology employs a KNNImputer algorithm to handle null values in the dataset, isolation forest (iForest) to remove outlier data, and a synthetic minority oversampling technique (SMOTE) to balance data distribution. For model interpretability, features importance are reported by using the SHapley Additive exPlanations (SHAP) technique. Results The proposed model was trained and evaluated by using a publicly available data set from Albert Einstein Hospital in Brazil, which consisted of 5,644 data samples with 559 confirmed COVID-19 cases. The ensemble model achieved outstanding performance with an overall accuracy of 99.88% [95% CI: 99.6 - 100], AUC of 99.38% [95% CI: 97.5 - 100], a sensitivity of 98.72% [95% CI: 94.6 - 100] and a specificity of 99.99% [95% CI: 99.99- 100]. Discussion The proposed model revealed better performance when compared against existing state-of-the-art studies [ 3 , 22 , 56 , 71 ] for the same set of features employed by them. As compared to the best performing Bayes Net model [ 22 ] average accuracy of 95.159%, ERLX achieved an average accuracy of 99.94%. In comparison with AUC of 85% reported by the SVM model [ 56 ], ERLX obtained AUC of 99.77% in addition to improvements in sensitivity, and specificity. As compared with ER-COV model [ 71 ] average sensitivity of 70.25% and specificity of 85.98%, ERLX model achieved sensitivity of 99.47% and specificity of 99.99%. The ERLX model obtained considerable higher score as compared with ANN model [ 3 ] in all performance metrics. Therefore, the model presented is robust and can be deployed for reliable early and rapid screening of COVID-19 patients.
Background and objectives: Self-medication is commonly practiced throughout the world. The aim of this study was to ascertain the use prevalence and knowledge of harmful effects of selfmedication among college students of health professions and non-health professions. Methods: A cross-sectional study was performed among 1,167 students from 12 faculties of a public university and two private universities in Kuwait. Data were collected using a selfadministered pretested questionnaire containing 32 questions. Results: Among the participants, 70.4% (822/1,167) used self-medication. The prevalence of self-medication was significantly higher among students of non-health professions compared with those of health professions (35.9% vs. 25.9%, p = 0.004, 95% CI, 6.28% to 13.73%, respectively). Pain killer medicines (52.9%), vitamins/minerals (13.1%), and antihistamines (9.0%) were the most commonly used non-prescription medications. Antibiotics and sleeping pills were used without a prescription in 2.9% and 2.1%, respectively. Older age, non-Kuwaiti national, and students of 5th to 7th year of study were significant predictors of self-medication. Knowledge scores of harmful effects of self-medication were about two-fold higher among females than their male counterparts. Similarly, students of higher years of study (5th to 7th year) had higher knowledge score compared with others. Conclusions: The prevalence of self-medication was alarmingly high among young adults in Kuwait. People should be informed about adverse effects of self-medication through mass and social media campaign. IMC J Med Sci 2018; 12(2): 57-68
The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.