Journal: PLoS ONE

Article Title: A machine learning based exploration of COVID-19 mortality risk

doi: 10.1371/journal.pone.0252384

Figure Lengend Snippet: Three machine learning models were developed using the SVM framework with three input groups; invasive, non-invasive, and their combination. The invasive group comprises laboratory results. Non-invasive features comprise patient clinical and demographic data. The joint group comprises the combination of invasive and non-invasive features. P1, P2, and P3 represent the prediction performance provided by the non-invasive, joint, and invasive models, respectively. The non-invasive model displayed good prediction performance in the farther future (P1) whereas the invasive model showed good prediction performance for the near future (P3). Neighborhood Component Analysis (NCA), recursive feature elimination via Support Vector Machine (SVM-RFE), and linear SVM with least absolute shrinkage and selection operator (Lasso) sparsity regularization (Sparse Linear SVM) were utilized for inspection of feature contributions and dynamics with respect to the outcome.

Article Snippet: A custom code was written in MATLAB to implement 100 iterations of Sparse Linear SVM analysis.

Techniques: Plasmid Preparation, Selection

Journal: PLoS ONE

Article Title: A machine learning based exploration of COVID-19 mortality risk

doi: 10.1371/journal.pone.0252384

Figure Lengend Snippet: ( A ) ROC curve of joint, invasive, and non-invasive models. ( B ) Investigation of models’ performance and robustness towards sample size. For each data point, a model was trained and evaluated using 90% of data which was randomly bootstrapped from the main dataset while maintaining the original discharge to expired ratio. The models were robust to the sample size and no significant difference was observed between the performance of invasive and non-invasive models. ( C ) Performance table of invasive, non-invasive, and joint models. Performances are reported as mean along with standard deviations. ( D ) Comparing the dynamics of laboratory and non-invasive features for randomly selected combinations of features. ( E ) Recursive feature elimination. Compared with invasive features, prominent non-invasive features had significant prediction information contents. In general, the first three features with prominent contributions to the improvement of the non-invasive model’s performance were SPO 2 , age, and presence of cardiovascular disorders; the first three invasive features were BUN, LDH, and PTT. ( F ) Sparsity analysis. Sparse linear SVM was utilized to investigate optimal feature combinations for fixed predictor numbers. For a specific sparsity level (features number), the non-invasive model performs better than the invasive model. Green and gray represent non-invasive and invasive modes, respectively.

Article Snippet: A custom code was written in MATLAB to implement 100 iterations of Sparse Linear SVM analysis.

Techniques: