Fig. 3: SHAP global feature importance outcomes for the RF model on the ITU training set. | npj Digital Medicine

Fig. 3: SHAP global feature importance outcomes for the RF model on the ITU training set.

From: AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery

Fig. 3

The top features for ITU admission are sorted by their mean absolute SHAP importance values. Each row indicates the impact of each concept on predictions, with each data sample represented as a dot. The colour of the dots ranges from blue (lower concept frequency values) to red (higher frequency values). The horizontal position of a dot (SHAP value) shows the concept’s contribution to the prediction, with right indicating ITU admission and left indicating ward. For example, in the top row, less frequent mentions of osteoporosis increase the chances of ward stay, while frequent mentions of intracranial meningioma increase the probability of ITU admission.

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