Postgraduate medical journal
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The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Additive exPlanation (SHAP), to develop a predictive model for critically ill patients. ⋯ A transparent ML model for predicting outcomes in critically ill patients using SHAP methodology is feasible and effective. SHAP values significantly improve the explainability of ML models.
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Bar charts of numerical data, often known as dynamite plots, are unnecessary and misleading. Their tendency to alter the perception of mean's position through the within-the-bar bias and their lack of information on the distribution of the data are two of numerous reasons. The machine learning tool, Barzooka, can be used to rapidly screen for different graph types in journal articles.We aim to determine the proportion of original research articles using dynamite plots to visualize data, and whether there has been a change in their use over time. ⋯ Our results show that the use of dynamite plots in surgical research has decreased over time; however, use remains high. More must be done to understand this phenomenon and educate surgical researchers on data visualization practices.