Interpretation of the local model using the SHAP method, including waterfall plots and the evolution of each characteristic’s contribution to individual risk Older adults categorized by low

<p>(A) or high (B) risk of developing delirium: ‘A’ indicates that the individual is not classified under the ‘delirium’ category, whereas ‘B’ indicates that the individual is classified under the ‘delirium’ category.</p>

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Bibliographic Details
Main Author:	Zong-bi Wu (20910827) (author)
Other Authors:	You-li Jiang (20910830) (author), Shuai-shuai Li (3444416) (author), Ao Li (15375) (author)
Published:	2025
Subjects:	Medicine Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified xgboost )&# 8212 shapley additive explanations key features identified intensive care unit indicating superior performance increases hospitalization costs glasgow coma scale extreme gradient boosting evaluated using ten best discriminative ability assist clinical decision fold cross validation evaluated using accuracy 155 patients admitted xgboost model demonstrated machine learning model xlink "> data retrospective study based respiratory failure admitted predict delirium risk feature variable screening elderly icu patients elderly copd patients best subset method iv </ p gcs motor score shap analysis included xlink "> respiratory failure validation set iv database high accuracy 155 ). verbal score predictive model f1 score equation score worsens outcomes study aimed renal disease random forest poor prognosis patient population noninvasive ventilation nearest neighbor logistic regression icu admission hospital stay first day f1 scores delirium incidence
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Interpretation of the local model using the SHAP method, including waterfall plots and the evolution of each characteristic’s contribution to individual risk Older adults categorized by low

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