Wrong Prediction Case Analysis
death
EVENTS
We further examined the worst case of the XGBoost model prediction. First, we examined the distribution of “time before death events” in our testing set and identified that all the wrong predictions fell within 2 ranges of time: 1 to 7 days (false negatives) and 7 to 12 days (false positives) before death, as shown in During the review of the SHAP value analysis for incorrect predictions, 2 main types of explanations emerged, with the key feature being the “average heart rate.” Among the 6 false-positive predictions, 3 (50%) were primarily influenced by an elevated average HR, whereas the other 3 (50%) were driven by factors such as a decreased appetite or deteriorating care phase. A similar pattern was observed for the false-negative predictions within 1 to 7 days before death. In approximately half of the cases, a relatively low average HR played a significant role in predicting a negative outcome, accompanied by other parameters, including “care phase,” “appetite,” “consciousness,” and “steps,” aligning in the same direction. In the remaining half of the cases, the average HR had a small positive impact, whereas the other parameters contributed to a negative prediction. Some case results are depicted in Our analysis suggests that the average HR is an important indicator but not an absolute one in the 7-day death prediction model. In a vital sign study conducted in a palliative care unit, an increasing trend in HR was observed up to 2 weeks before death [