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Fig. 3 | BMC Medical Informatics and Decision Making

Fig. 3

From: Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis

Fig. 3

Summary of COVID-19 patient clustering using SOM. a Plot of topographic error of the 2D SOM grid vs. size of the grid. b 2D plot of SOM neurons after retaining only the most significant clinical variable for analysis. Each small grid represents a neuron, and the size of the square in each grid represents the number of patients associated with each neuron. The color code corresponds to superclusters presented in panel (d). c Plot of number of patients in each neuron. d 3D dendrogram summarizing the neurons into superclusters. e 2D dendrogram with the same information as the dendrogram in panel (d). In both dendrograms, the vertical axis represents the relative distance between clusters, which can be known between any two clusters by looking at the branch point where they diverge. f Gradient map where light blue regions of the SOM depict higher similarity of neurons with each other. g Boxplots of immune-associated clinical variables that differentiate superclusters. h Boxplots in which superclusters 1 and 3 display similar trends. i Boxplots in which only one supercluster has a median at a different value from the other three. All variables have been previously normalized. For binary variables, only three possible positions on the vertical axis is possible: the bottom one being no, the middle one being yes, and the top one being missing. For the gender (sex) variable, the bottom position is female, the middle is male, and the top one is missing

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