Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine

Abstract

Background: Acupuncture is safe and effective for functional dyspepsia (FD), while its efficacy varies among individuals. Predicting the response of different FD patients to acupuncture treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). In the current study, the individual efficacy prediction models were developed based on the support vector machine (SVM) algorithm and routine clinical features, aiming to predict the efficacy of acupuncture in treating FD and identify the FD patients who were appropriate to acupuncture treatment.

Methods: A total of 745 FD patients were collected from two clinical trials. All the patients received a 4-week acupuncture treatment. Based on the demographic and baseline clinical features of 80% of patients in trial 1, the SVM models were established to predict the acupuncture response and improvements of symptoms and quality of life (QoL) at the end of treatment. Then, the left 20% of patients in trial 1 and 193 patients in trial 2 were respectively applied to evaluate the internal and external generalizations of these models.

Results: These models could predict the efficacy of acupuncture successfully. In the internal test set, models achieved an accuracy of 0.773 in predicting acupuncture response and an R ² of 0.446 and 0.413 in the prediction of QoL and symptoms improvements, respectively. Additionally, these models had well generalization in the independent validation set and could also predict, to a certain extent, the long-term efficacy of acupuncture at the 12-week follow-up. The gender, subtype of disease, and education level were finally identified as the critical predicting features.

Conclusion: Based on the SVM algorithm and routine clinical features, this study established the models to predict acupuncture efficacy for FD patients. The prediction models developed accordingly are promising to assist doctors in judging patients' responses to acupuncture in advance, so that they could tailor and adjust acupuncture treatment plans for different patients in a prospective rather than the reactive manner, which could greatly improve the clinical efficacy of acupuncture treatment for FD and save medical expenditures.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00271-8.

Keywords: Acupuncture; Artificial intelligence; Efficacy prediction; Functional dyspepsia; Healthcare; Machine learning; Precision medicine; Predictive preventive personalized medicine (PPPM/3PM); Support vector machine.

Fig. 1

The number of participants who survived in each stage

Fig. 2

The performance of prediction models in the internal test set. (A-1) illustrates the overall performance of the SVC model in predicting acupuncture response at the end of treatment. (A-2) illustrates the receiver operating characteristic curve corresponding to the AUC. (B-1) and (C-1) show the scatter plots between the actual and the predicted improvements of NDLQI and NDSI of the internal test samples, respectively. (B-2) and (C-2) illustrate the predicted labels and their corresponding actual labels of these test samples. (A/B/C-3, A/B/C-4) displays the results of permutation tests for these predication analyses. Abbreviation: Acc, accuracy; Sen, sensitivity; Spe, specificity; AUC, area under the receiver operating characteristic curve; NDSI, Nepean Dyspepsia Symptom Index; NDLQI, Nepean Dyspepsia Life Quality Index; R², coefficient of determination; MSE, mean squared error

Fig. 3

The weight of each feature in predicting acupuncture efficacy. (A) illustrates the weight of each feature in predicting acupuncture response; (B) and (C) show the weight of each feature in predicting NDSI and NDLQI improvements. The ranking order of the features is consistent with Table 1. The red dotted lines represent the mean + standard deviation of features’ weights

Fig. 4

Performance comparison among linear SVM and other ML algorithms in prediction acupuncture efficacy. (A), (B), and (C) illustrate the results of performance comparison in predicting acupuncture response, NDLQI improvements, and NDSI improvements among these six algorithms in the internal validation set (the left panels) and independent validation set (the right panels). Abbreviation: Acc, accuracy; Sen, sensitivity; Spe, specificity; NDSI, Nepean Dyspepsia Symptom Index; NDLQI, Nepean Dyspepsia Life Quality Index; R², coefficient of determination; MSE, mean squared error; SVC, support vector classification; DT, decision tree; LR, logistic regression; rbf SVM, radial basis function support vector classification; BT, boosted tree; KNN, K-nearest neighbor; SVR, support vector regression; MLR, multiple linear regression; GPR, Gaussian process regression