Heterogeneous Treatment Effect Estimation with Subpopulation Identification for Personalized Medicine in Opioid Use Disorder.
Heterogeneous Treatment Effect Estimation with Subpopulation Identification for Personalized Medicine in Opioid Use Disorder.
复制标题
阿片类药物使用障碍个体化医疗的异质治疗效果估计和亚群识别。
DOI：
10.1109/icdm58522.2023.00127
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Zhang,Ping
中科院分区：
文献类型：
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作者：
Lee,Seungyeon;Liu,Ruoqi;Song,Wenyu;Zhang,Ping
深度学习模型在估计治疗效果(TEE)方面显示了良好的结果。然而，他们中的大多数忽略了不同亚组之间治疗结果的差异，这些亚组具有明显的特征。这一限制阻碍了他们为特定亚群提供准确估计和治疗建议的能力。在这项研究中，我们介绍了一个新的基于神经网络的框架，称为子组TE，它结合了子组识别和治疗效果评估。SubgroupTE识别不同的亚组，同时估计每个亚组的治疗效果，通过考虑治疗反应的异质性来改进治疗效果估计。对合成数据的对比实验表明，SubgroupTE在治疗效果估计方面优于现有模型。此外，在与阿片类药物使用障碍(OUD)相关的真实世界数据集上的实验表明，我们的方法具有增强OUD患者个性化治疗建议的潜力。
Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.