Paper Info
Title
A Post-Hoc Interpretable Ensemble Model to Feature Effect Analysis in Warfarin Dose Prediction for Chinese Patients
Abstract
To interprete the importance of clinical features and genotypes for warfarin daily dose prediction, we developed a post-hoc interpretable framework based on an ensemble predictive model. This framework includes permutation importance for global interpretation and local interpretable model-agnostic explanation (LIME) and shapley additive explanations (SHAP) for local explanation. The permutation importance globally ranks the importance of features on the whole data set. This can guide us to build a predictive model with less variables and the complexity of final predictive model can be reduced. LIME and SHAP together explain how the predictive model give the predicted dosage for specific samples. This help clinicians prescribe accurate doses to patients using more effective clinical variables. Results showed that both the permutation importance and SHAP demonstrated that VKORC1, age, serum creatinine (SCr), left atrium (LA) size, CYP2C9 and weight were the most important features on the whole data set. In specific samples, both SHAP and LIME discovered that in Chinese patients, wild-type VKORC1-AA, mutant-type CYP2C9*3, age over 60, abnormal LA size, SCr within the normal range, and using amiodarone definitely required dosage reduction, whereas mutant-type VKORC1-AG/GG, small age, SCr out of normal range, normal LA size, diabetes and heavy weight required dosage enhancementt.
Year
DOI
Venue
2022
10.1109/JBHI.2021.3092170
IEEE Journal of Biomedical and Health Informatics
Keywords
DocType
Volume
Warfarin dose prediction,ensemble models,feature importance,interpretation,oversampling
Journal
26
Issue
ISSN
Citations 
2
2168-2194
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yuzhen Zhang158.86
Cheng Xie200.34
Ling Xue311.02
Yanyun Tao401.69
Guoqi Yue500.34
Bin Jiang611.02