高级检索
当前位置: 首页 > 详情页

Decision tree model based prediction of the efficacy of acupuncture in methadone maintenance treatment

文献详情

资源类型:
WOS体系:

收录情况: ◇ SCIE

机构: [1]Clinical 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, [2]School of Artificial Intelligence, South China Normal University, Guangzhou, China, [3]Postdoctoral Research Station, Department of Rehabilitation, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China, [4]Department of Rehabilitation, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China, [5]The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
出处:
ISSN:

关键词: methadone maintenance treatment machine learning decision tree feature importance acupuncture

摘要:
BackgroundPatients with MMT often face difficulties such as sleep disturbance, headaches, and difficulty in complete abstinence from drugs. Research has shown that acupuncture can mitigate side effects while attenuating methadone dependence. It also has a synergistic and attenuated effect on methadone maintenance treatment (MMT). Exploring the predictors of the efficacy of acupuncture intervention in MMT might help clinicians and patients promote acupuncture-assisted participation in MMT, and improve clinical treatment strategies for MMT.ObjectiveTo describe the effect of potential predictors on MMT after acupuncture intervention by building a decision-tree model of data from A Clinical Study of Acupuncture-assisted MMT.Design, setting, and participantsIn this randomized controlled trial, 135 patients with MMT underwent acupuncture at the Substance Dependence Department of Guangzhou Huiai Hospital in Guangzhou, Guangdong Province, China.InterventionA total of 135 patients were 1:1 randomly assigned to either an acupuncture plus routine care group (acupuncture plus methadone) or a routine group (methadone only) for 6 weeks, and followed up for 10 weeks. Sex, age, education level, route of previous opioid use, years of opioid use, and MMT time were recorded before the trial.Outcome measurements and statistical analysisAll analyses were based on the intention-to-treat (ITT) population. The two decision tree models used the change of methadone dosage and the VAS score for opioid desire as response variables, respectively, and the evaluation criteria were positive effect (decreased by & GE;20%) and no effect (decreased by < 20%, or increased). We generated the respective feature weights for the decision tree and evaluated the model's accuracy and performance by Precision-Recall.ResultsThe overall accuracy of methadone reduction and psychological craving VAS scoring decision trees were 0.63 and 0.74, respectively. The Methadone Dosage Efficacy decision tree identified years of opioid use (weight = 0.348), acupuncture (weight = 0.346), and route of previous opioid use (weight = 0.162) as key features. For the VAS Score decision tree, acupuncture (weight = 0.618), MMT time (weight = 0.235), and age (weight = 0.043) were the important features.ConclusionExploratory decision tree analysis showed that acupuncture, years of opioid use, route of previous opioid use, MMT time, and age were key predictors of the MMT treatment. Thus, acupuncture-assisted MMT strategy should consider the relevant influencing factors mentioned above.Patient summaryUnderstanding patient characteristics and the impact of acupuncture regimens on methadone dosage reduction in MMT patients may help physicians determine the best treatment regimen for patients. An analysis of data from our clinical trial showed that acupuncture, years of opioid use, route of previous opioid use, age, and MMT time were key predictors of progressive recovery in patients with MMT. Eligible patients may benefit most from the MMT rehabilitation that reduces consumption and psychological cravings for methadone.

基金:
语种:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2021]版:
大类 | 3 区 医学
小类 | 3 区 临床神经病学 3 区 神经科学
最新[2025]版:
大类 | 3 区 医学
小类 | 3 区 临床神经病学 3 区 神经科学
JCR分区:
出版当年[2020]版:
Q2 NEUROSCIENCES Q2 CLINICAL NEUROLOGY
最新[2023]版:
Q2 CLINICAL NEUROLOGY Q3 NEUROSCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2020版] 出版当年五年平均 出版前一年[2019版] 出版后一年[2021版]

第一作者:
第一作者机构: [1]Clinical 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,
共同第一作者:
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:2018 今日访问量:0 总访问量:645 更新日期:2024-07-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 广东省中医院 技术支持:重庆聚合科技有限公司 地址:广州市越秀区大德路111号