Machine Learning Methods for Optimizing Individualized Treatment Strategies for Precision Psychiatry

项目摘要： 精神障碍导致巨大的残疾，占全世界1.839亿残疾调整生命年， 宽在目前可用的药理学和行为干预措施中，没有一种单一的治疗方法是普遍有效的。 有效。此外，对精神障碍的治疗反应远远不够。因此，有一个紧迫的 需要优化治疗反应。各种因素似乎与积极的治疗反应有关 精神障碍，从而提供证据，通过纳入患者特异性特征， 精神病学在治疗决策中的作用，以实现精确的精神病学。然而，现有的方法，以incorpo- 率患者特异性特征不足以解决精准精神病学面临的独特挑战。 要指出的是，精神障碍的治疗决策不可避免地面临着广泛的诊断异质性， 此外，疾病的生物学和临床表现的患者间差异很大， 诊断分类和潜在的病理生理学之间的联系。为了应对这些新出现的挑战， 该提案旨在开发新的机器学习和统计推断方法，以建立个性化治疗， 解释广泛的异质性和患者间变异性，并整合 多领域的大脑和行为数据跨越几种疾病。具体来说，我们的目标是：（1）学习最佳的潜在 通过概率生成模型表示患者，该模型在国家 心理健康研究所研究领域标准战略计划（RDoC）;（2）纳入先前的最佳治疗- 通过有针对性的迁移学习从临床试验的非随机阶段提取信息;（3）综合 从多项研究中学习的个体化治疗决策规则;以及（4）提供严格的统计推断 的决策规则。RDoC呼吁围绕潜在结构进行心理健康研究 由于在各种疾病中有共同的特点，本文开发的方法将应用于一系列随机对照试验 （随机对照试验），包括多个高质量的 具有多模态数据的RCT（例如，症状、行为测试、心理社会测量、大脑测量）。这 战略将允许检查跨疾病共享的结构的治疗策略，因此将在 提高概括性。总之，这项研究将使用机器学习方法和统计推断， 努力更好地利用生物标志物和临床表现之间的复杂相互作用， 精准精神病学，目标是为精神障碍患者选择最佳治疗方法。

Project Summary: Mental disorders cause immense disability, accounting for 183.9 million disability-adjusted life-years world- wide. Among currently available pharmacological and behavioral interventions, no single therapy is universally ef- fective. Moreover, treatment responses are far from adequate across mental disorders. As such, there is an urgent need to optimize treatment responses. Various factors appear to be associated with positive treatment responses for mental disorders, thus providing evidence for improving response rate by incorporating patient-specific charac- teristics in treatment decisions in an effort to achieve precision psychiatry. However, existing methods to incorpo- rate patient-specific characteristics do not adequately address the unique challenges facing precision psychiatry. To point, treatment decision making for mental disorders is inevitably confronted by extensive diagnostic hetero- geneity, substantial between-patient variation in biological and clinical manifestations of disease, and mismatch between diagnostic categorization and the underlying pathophysiology. To address these emerging challenges, this proposal aims to develop novel machine learning and statistical inference methods to build individualized treat- ment rules to account for the extensive heterogeneity and between-patient variability and integrate evidence from multi-domain brain and behavioral data across several disorders. Specifically, we aim to: (1) learn optimal latent representation of patients through a probabilistic generative model that has theoretical support under the National Institute of Mental Health Strategic Plan on Research Domain Criteria (RDoC); (2) incorporate prior optimal treat- ment information from the non-randomized phase of clinical trials through targeted transfer learning; (3) synthesize individualized treatment decision rules learned from multiple studies; and (4) provide rigorous statistical inference of fitted decision rules. Following the RDoC call for centering mental health research around latent constructs shared across disorders, the methods developed here will be applied to a range of randomized controlled trials (RCTs) of patients with major depressive disorder and other co-morbid disorders, including multiple high-quality RCTs with multi-modality data (e.g., symptoms, behavioral tests, psychosocial measures, brain measures). This strategy will allow for examination of treatment strategies for constructs shared across disorders and thus will in- crease generalizability. In sum, this research will use machine learning approaches and statistical inference in an effort to better leverage the complex interplay between biomarkers and clinical manifestations in the context of precision psychiatry, with the goal of selecting the best treatments for patients with mental disorders.