[Construction of interpretable predictive model of acupuncture for methadone reduction in patients undergoing methadone maintenance treatment based on machine learning and SHAP].

To construct a predictive model for the reduction in methadone maintenance treatment (MMT) and evaluate the effects of different interventions and other clinical factors on methadone reduction using Shapley additive explanations (SHAP). Two clinical trials of acupuncture for methadone reduction in MMT patients were analyzed, and the baseline data, MMT related information, intervention measures, the data related to dose-reduction outcomes were collected. The predictive model was constructed by means of 6 machine learning algorithms including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), Naive Bayes (NB), random forest (RF) and categorical-boosting (CatBoost), and 2 integration methods, blending-ensemble method (Blending) and Stacking-ensemble method (Stacking). SHAP was employed for the interpretability analysis of the optimal model. A total of 251 MMT patients were included, 128 cases in the acupuncture group and 123 cases in the non-acupuncture group. CatBoost and Stacking performed optimally in the test set. CatBoost obtained an accuracy of 0.780 0±0.060 8, a precision of 0.500 0±0.120 0, a recall of 0.818 2±0.140 2, F1 score of 0.620 7±0.114 0, and receiver operating characteristic-area under curve (ROC-AUC) of 0.857 8±0.140 2 for the subjects. In MMT patients with acupuncture as an adjunctive therapy, the top 5 important features for methadone reduction, included intervention measures, body mass index (BMI), the duration of MMT, the history of opioid use and occupation; and SHAP values were 1.25, 0.36, 0.21, 0.19 and 0.12, respectively. The SHAP feature dependence plot showed that BMI, MMT duration and the history of opioid use presented a nonlinear negative correlation with the reduction effect. In acupuncture as adjunctive therapy for methadone reduction, the clinical factors should be considered comprehensively; and the interpretable predictive model provides a scientific basis for it, which is conducive to the improvement of clinical strategy of acupuncture for methadone reduction and the development of personalized reduction scheme.