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首页> 外文期刊>Journal of Pain Research >Reducing Opioid Prescriptions by Identifying Responders on Topical Analgesic Treatment Using an Individualized Medicine and Predictive Analytics Approach
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Reducing Opioid Prescriptions by Identifying Responders on Topical Analgesic Treatment Using an Individualized Medicine and Predictive Analytics Approach

机译：通过识别使用个性化医学和预测分析方法识别题目镇痛治疗的响应者来减少阿片类药物处方

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Purpose: Chronic pain is a life changing condition, and non-opioid treatments have been lately introduced to overcome the addictive nature of opioid therapies and their side effects. In the present study, we explore the potential of machine learning methods to discriminate chronic pain patients into ones who will benefit from such a treatment and ones who will not, aiming to personalize their treatment. Patients and Methods: In the current study, data from the OPERA study were used, with 631 chronic pain patients answering the Brief Pain Inventory (BPI) validated questionnaire along with supplemental questions before and after a follow-up period. A novel machine learning approach combining multi-objective optimization and support vector regression was used to build prediction models which can predict, using responses in the baseline, the four different outcomes of the study: total drugs change, total interference change, total severity change, and total complaints change. Data were split to training (504 patients) and testing (127 patients) sets and all results are measured on the independent test set. Results: The machine learning models extracted in the present study significantly overcame other state of the art machine learning methods which were deployed for comparative purposes. The experimental results indicated that the machine learning models can predict the outcomes of this study with considerably high accuracy (AUC 73.8– 87.2%) and this allowed their incorporation in a decision support system for the selection of the treatment of chronic pain patients. Conclusion: Results of this study revealed the potential of machine learning for an individualized medicine application for chronic pain therapies. Topical analgesics treatment were proven?to be, in general, beneficial but carefully selecting with the suggested individualized medicine decision support system was able to decrease by approximately 10% the patients which would have been subscribed with topical analgesics without having benefits from it.
机译：目的：慢性疼痛是一种寿命不断变化的条件，最近介绍了非阿片类药物治疗，以克服阿片类药物疗法的上瘾性质及其副作用。在本研究中，我们探讨了机器学习方法的潜力，以区分慢性疼痛患者进入那些将受益于这种治疗的人，旨在个性化他们的治疗。患者和方法：在目前的研究中，使用了来自歌剧研究的数据，631例慢性疼痛患者接受了短暂的止痛药（BPI）验证了调查问卷以及后续期前和之后的补充问题。一种新颖的机器学习方法，组合多目标优化和支持向量回归来构建可以预测的预测模型，这些模型可以在基线中使用响应，研究的四种不同结果：总体药物变化，总干扰变化，总严重程度变化，和总投诉发生变化。数据分为培训（504名患者）和测试（127名患者）套，所有结果都在独立的测试集上测量。结果：本研究中提取的机器学习模型显着克服了用于比较目的部署的艺术机器学习方法的其他状态。实验结果表明，机器学习模型可以以相当高的精度（AUC 73.8-87.2％）预测该研究的结果，并且这允许它们在决策支持系统中结合选择慢性疼痛患者的治疗。结论：本研究结果揭示了机器学习对慢性疼痛疗法的个体化药物的潜力。潜意镇痛治疗被证明是一般的，有益但仔细选择的建议个性化医学决策支持系统能够减少约10％的患者，这些患者将以局部镇痛药认购而不从中受益。

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