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Interpretable machine learning reveals key dose trajectory patterns predicting success of acupuncture-assisted methadone tapering
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Baochao Fan1, Yiming Chen1, Chen Chen2, Peiming Zhang1, 3, 4, Jingchun Zeng5, *, Liming Lu1, *
Acupuncture and Herbal Medicine | 2025, 5(4) : 469 - 479
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Acupuncture and Herbal Medicine | 2025, 5(4): 469-479
• Original Articles •
Interpretable machine learning reveals key dose trajectory patterns predicting success of acupuncture-assisted methadone tapering
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Baochao Fan1, Yiming Chen1, Chen Chen2, Peiming Zhang1, 3, 4, Jingchun Zeng5, *, Liming Lu1, *
Affiliations
  • 1Clinical Research and Big Data Laboratory, South China Research Center for Acupuncture and Moxibustion, Medical College of Acu-Moxi and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China
  • 2Jiangsu Medical College, Yancheng, China
  • 3Department of Acupuncture and Moxibustion, Foshan Hospital of Traditional Chinese Medicine, Affiliated to Guangzhou University of Chinese Medicine, Foshan, China
  • 4The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine, Foshan, China
  • 5Department of Acupuncture, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
About Author:

Baochao Fan and Yiming Chen contributed equally to this work as co-first authors.

Published: 2025-12-25 doi: 10.1097/HM9.0000000000000177
Outline
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Objective:

Long-term methadone maintenance treatment (MMT) requires gradual dose reduction to mitigate adverse effects; prior research has shown that acupuncture facilitates dose tapering and alleviates opioid cravings. Although medication dosage parameters hold significant clinical value in addiction medicine, the impact of early outpatient dose data on therapeutic effects currently remains unclear. This study aimed to construct a methadone dose reduction prediction model and analyze the clinical value of historical dose trajectories.

Methods:

Data from two randomized trials (N = 197 patients across six Chinese MMT clinics) were analyzed, with participants grouped into the acupuncture and non-acupuncture cohorts. The primary outcome was combined outcome comprising methadone dose reduction and craving score changes. Pre-intervention dose trajectories were derived via cluster analysis and merged with baseline features. Five machine learning models were trained using SHapley Additive exPlanations (SHAP) to explain the feature contributions. Subgroup analyses linking trajectories to the effects of acupuncture were conducted.

Results:

Methadone dose data were clustered into three trajectories. Model training included nine features from 11 variables. The CatBoost model achieved the best performance on the test set (area under the curve = 0.7531, accuracy = 0.8205). The SHAP summary plot revealed that the three most influential factors in methadone dose reduction were intervention type, body mass index, and dosage trajectory. Subgroup analysis showed that trajectory class 2 exhibited significantly better efficacy than class 0 at weeks 2 and 4 of acupuncture (week 2: risk difference, 20.4%, P = 0.015; week 4: risk difference, 27.5%, P = 0.013).

Conclusions:

In this study, we established a trajectory-based prediction model for MMT dose reduction and demonstrated the clinical value of historical trajectories. The results suggest that acupuncture optimally supports patients with dynamic "rise-then-decline" trajectories, advancing personalized MMT strategies.

Acupuncture  /  Machine learning  /  Methadone maintenance treatment  /  Predictive model  /  Trajectory analysis
Baochao Fan, Yiming Chen, Chen Chen, Peiming Zhang, Jingchun Zeng, Liming Lu. Interpretable machine learning reveals key dose trajectory patterns predicting success of acupuncture-assisted methadone tapering[J]. Acupuncture and Herbal Medicine, 2025 , 5 (4) : 469 -479 . DOI: 10.1097/HM9.0000000000000177
  • National Natural Science Foundation of China(82174527; 82405556)
  • Project of First-Class Universities and High-level Dual Discipline for Guangzhou University of Chinese Medicine(A1-2601-24-414-109Z71; A1-2601-24-415-109Z50; A1-2601-24-415-109Z76)
  • Zhongshan Coma and Wake-up Promotion Workshop of Traditional Chinese Medicine(A2-3105-242-109-001)
  • The China Postdoctoral Science Foundation General Project(2024M750464)
Year 2025 volume 5 Issue 4
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Article Info
doi: 10.1097/HM9.0000000000000177
  • Receive Date:2025-07-28
  • Online Date:2026-11-04
  • Published:2025-12-25
Article Data
Affiliations
History
  • Received:2025-07-28
  • Accepted:2025-11-19
Funding
National Natural Science Foundation of China(82174527; 82405556)
Project of First-Class Universities and High-level Dual Discipline for Guangzhou University of Chinese Medicine(A1-2601-24-414-109Z71; A1-2601-24-415-109Z50; A1-2601-24-415-109Z76)
Zhongshan Coma and Wake-up Promotion Workshop of Traditional Chinese Medicine(A2-3105-242-109-001)
The China Postdoctoral Science Foundation General Project(2024M750464)
Affiliations
    1Clinical Research and Big Data Laboratory, South China Research Center for Acupuncture and Moxibustion, Medical College of Acu-Moxi and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China
    2Jiangsu Medical College, Yancheng, China
    3Department of Acupuncture and Moxibustion, Foshan Hospital of Traditional Chinese Medicine, Affiliated to Guangzhou University of Chinese Medicine, Foshan, China
    4The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine, Foshan, China
    5Department of Acupuncture, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

Corresponding:

* Liming Lu, E-mail: ;
Jingchun Zeng, E-mail: .
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表12种不同金属材料的力学参数
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Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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