Building Multistage Treatment Regimens for Depression after Acute Coronary SyndromeSyndrome

急性冠状动脉综合征后抑郁症多阶段治疗策略的建立 项目摘要 抑郁症不仅在急性冠状动脉综合征（ACS）患者中常见， 而且还显示出增加ACS复发和死亡的风险。尽管其发病率很高， 严重的影响，ACS后抑郁症的管理仍然很差，因为抑郁症筛查的不及时， ACS后抑郁症的治疗选择有限，如果初始治疗失败，缺乏有效的程序。 为了解决这些问题，临床研究人员试图开发个性化的阶梯式护理程序， ACS后抑郁症患者;这涉及让患者选择接受心理治疗和/或 抗抑郁治疗和根据需要调整治疗。治疗决定通常基于 患者人口统计学、治疗偏好、病史、疾病进展和共病状况。 随着现代技术的发展，可用的治疗方法越来越多，更多的病人， 在临床研究中收集患者信息。从而为治疗决策挖掘有用信息 变得越来越具有挑战性。在这个项目中，我们建议开发一种原则性的方法来构建简单的 从高维数据中获得可解释的多级治疗策略，可用于指导治疗 在整个疾病过程中的选择。该项目的目标1是致力于发展瓦里- 使用统计机器学习构建多级治疗策略的可行选择方法 技术.拟议的研究旨在将流行的变量选择技术（LASSO） 现有的治疗策略搜索方法，即Q-学习和A-学习，用于开发最佳的 治疗策略和用于识别患者对初始治疗的反应状态（用于确定患者对初始治疗的反应状态的重要因素）。 在后续阶段进行定制处理。目标2评估所提出的方法，将这些方法应用于 ACS后抑郁症数据，并解决了一些计算挑战。这一领域的统计研究 注重使用预先选定的模型和变量制定循证治疗政策; 很少有人讨论如何以原则性的方式选择模型或变量。拟议的工作旨在将此 使用现代机器学习技术的方法学差距。

Building Multistage Treatment Policy for Depression after Acute Coronary Syndrome Project Summary Depression is not only commonly observed among patients who experienced acute coronary syndrome (ACS), but also has been shown to increase risks for recurrent ACS and mortality. Despite its high prevalence and serious impact, management of post-ACS depression remains poor because of ineciencies in depression screen- ing, limited treatment options of depression after ACS, and lack of e ective procedure if initial treatment fails. To address these issues, clinical researchers have tried to develop personalized stepped care procedures for post-ACS depression patients; this involves o ering patients the choice of receiving psychotherapy and/or antidepressant treatment and adjusting treatment as needed. The treatment decisions are usually based on patient demographics, treatment preference, medical history, progress of disease, and comorbid conditions. With the development of modern technologies, the number of available treatments increases, and more pa- tient information are collected in clinical research. Thus excavating useful information for treatment decisions is becoming more challenging. In this project, we propose to develop a principled way to construct simple interpretable multistage treatment policies from high-dimensional data, that can be used to guide treatment selection throughout the course of the disease. Aim 1 of the project is devoted to the development of vari- able selection methodology for constructing multistage treatment policies using statistical machine learning techniques. The proposed research seeks to incorporate the popular variable selection technique (LASSO) into existing treatment policy search approaches, namely Q-learning and A-Learning, for developing optimal treatment policies and for identifying patient response status to initial treatment { an important factor for tailoring treatment in the subsequent stages. Aim 2 evaluates the proposed methods, applies the methods to post-ACS depression data, and addresses some computational challenges. Statistical research in this area has been focused on the development of evidence-based treatment policies using pre-chosen models and variables; few if any discuss how to select models or variables in a principled way. The proposed work aims to ll this gap in methodology using modern machine learning techniques.