Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping
Fig 5
Scatter plots of variable values versus SHAP values to interpret how specific variable ranges influence treatment predictions. A: Scatter plot of X values on the x-axis versus SHAP values (left y-axis).
SHAP values reflect the importance of each X value in predicting treatment response, with higher positive values indicating greater importance for predicting treatment responsiveness. The colour of plots indicates what the prediction was, with orange indicating a prediction of treatment responsive, and blue non-responsive. Overlayed histograms show the proportion of predictions (right y-axis) for different X values: orange bars denote treatment-responsive predictions, and blue bars denote non-treatment-responsive predictions. B: Scatter plot of Y values on the x-axis versus SHAP values, filtered to include only instances where X is below 90. This plot demonstrates the importance of Y values in the model’s predictions, where lower negative SHAP values suggest higher importance for predicting non-responsive to treatment. Histograms overlayed on the scatter plot represent the proportion of predictions for different Y values, with orange bars for treatment-responsive and blue bars for non-treatment-responsive predictions. C: Scatter plot of Z values on the x-axis versus SHAP values, with data filtered to include only Y values between 50 and 90, as well as X values below 90. SHAP values indicate the importance of Z values in the model’s predictions, considering the constraints on X and Y. The histograms show the proportion of predictions for Z values, with blue bars representing treatment-responsive predictions and blue bars representing non-treatment-responsive predictions.