A machine learning based two-stage clinical decision support system for predicting patients' discontinuation from opioid use disorder treatment: retrospective observational study.
A machine learning based two-stage clinical decision support system for predicting patients' discontinuation from opioid use disorder treatment: retrospective observational study.
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DOI：
10.1186/s12911-021-01692-7
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发表时间：
2021-11-26
影响因子：
3.5
通讯作者：
Noor-E-Alam M
中科院分区：
文献类型：
--
作者：
Hasan MM;Young GJ;Shi J;Mohite P;Young LD;Weiner SG;Noor-E-Alam M
丁丙诺啡是阿片类药物使用障碍（OUD）患者广泛使用的治疗选择。过早停止这种治疗会对健康和社会造成许多负面影响。开发和评估基于机器学习的两阶段临床决策框架，用于预测哪些患者将在不到一年的时间内停止OUD治疗。拟议的框架分两个阶段进行此类预测：(i)在开始治疗时，（ii）在治疗开始后两/三个月。在这项回顾性观察分析中，我们使用了2013年至2015年马萨诸塞州所有付款人索赔数据（MA APCD）。研究样本包括5190例商业保险患者，在2014年1月至12月期间开始丁丙诺啡治疗，并且在2014年开始治疗日期之前至少一年前没有任何丁丙诺啡处方。停止治疗定义为至少连续两个月没有丁丙诺啡处方。六种机器学习模型（即逻辑回归、决策树、随机森林、极端梯度增强、支持向量机和人工神经网络）在输入数据上使用五倍交叉验证进行测试。第一阶段模型使用患者的人口统计信息。第二阶段模型包括基于覆盖天数比例（PDC）测量的早期治疗阶段药物依从性信息。开始使用丁丙诺啡的患者中有相当大比例（48.7%）在一年内停止治疗。第一阶段模型的接收工作特性曲线下面积（c统计量）在0.55 ~ 0.59之间变化。纳入患者在治疗早期阶段的依从性知识（2个月和3个月PDC）导致基于c统计量的模型判别能力有统计学意义的增加（p值< 0.001）。我们还利用决策树模型构造了可解释的决策分类规则。机器学习模型可以以合理的判别能力预测哪些患者最有可能过早停止治疗。提出的机器学习框架可以作为一种工具，在进一步验证后帮助通知临床决策支持系统。这可以潜在地帮助开处方者在不同的患者群体中根据他们对治疗中断的脆弱性优化分配有限的医疗资源，并设计个性化的支持系统，以提高患者对OUD治疗的长期依从性。
Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients’ demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients’ adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models’ discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model. Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients’ long-term adherence to OUD treatment.
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影响因子：
3.9
作者：
Meinhofer, Angelica;Williams, Arthur Robin;Bao, Yuhua
通讯作者：
Bao, Yuhua
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通讯作者：
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通讯作者：
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作者：
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通讯作者：
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