Machine Learning Methods for Optimizing Individualized Treatment Strategies for Precision Psychiatry

用于优化精准精神病学个体化治疗策略的机器学习方法

• 批准号: 10208246
• 负责人: Yuanjia Wang
• 金额: $ 41.83万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-20 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10208246
• 关键词: Accounting; Address; Advocate; Attention; Behavior; Behavior Therapy; Behavioral; Biological; Biological Markers; Biology; Brain; Characteristics; Chronic; Classification; Clinical; Clinical Trials; Clinical Trials Database; Collaborations; Complex; Confidence Intervals; Data; Data Set; Data Sources; Decision Making; Diagnostic; Dimensions; Disease; Evaluation; Face; Functional disorder; Future; Goals; Heterogeneity; Learning; Machine Learning; Major Depressive Disorder; Measures; Mental disorders; Methods; Modality; Modeling; National Institute of Mental Health; Neuropsychological Tests; Outcome; Patients; Pharmacology; Phase; Psychiatry; Psychological Transfer; Public Health; Randomized; Randomized Controlled Trials; Recording of previous events; Reproducibility; Research; Research Domain Criteria; Research Personnel; Strategic Planning; Sum; Symptoms; Training; Treatment outcome; Variant; behavior test; behavioral phenotyping; cognitive control; comorbidity; data archive; denoising; design; disability; disability-adjusted life years; effective therapy; emotion regulation; improved; individual patient; individualized medicine; innovation; machine learning method; mental disorder diagnosis; mental health center; multimodal data; neuroimaging; neurophysiology; novel; optimal treatments; patient population; patient variability; personalized medicine; psychosocial; response; secondary outcome; statistical and machine learning; tool; treatment optimization; treatment response; treatment strategy; trend

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.

项目总结： 精神障碍导致巨大残疾，占全球残疾调整寿命年的1.839亿年-- 很宽。在目前可用的药物和行为干预措施中，没有一种单一的治疗方法是通用的。 很有感染力。此外，精神障碍患者的治疗反应远远不够。因此，有一个紧急情况 需要优化治疗反应。各种因素似乎与积极的治疗反应有关。 对于精神障碍，从而为通过结合患者特有的fi特征来提高应答率提供证据。 在治疗决策中使用术语，以努力实现精确的精神病学。然而，现有的方法来防止- Rate Patient-specific特征不能充分解决精确精神病学面临的独特挑战。 需要指出的是，精神障碍的治疗决策不可避免地面临广泛的诊断异质性。 遗传性，患者之间在疾病的生物学和临床表现上的巨大差异，以及不匹配 在诊断分类和潜在的病理生理学之间。为了应对这些新出现的挑战， 该建议旨在开发新的机器学习和统计推理方法，以建立个性化治疗- 治疗规则，以解释广泛的异质性和患者之间的变异性，并整合来自 跨多个障碍的多领域大脑和行为数据。具体地说，我们的目标是：(1)学习最优潜伏期 通过概率生成模型来表示患者，该模型有理论支持 精神卫生研究所研究领域标准战略计划(RDoC)；(2)纳入先前的最佳治疗- 通过定向迁移学习从临床试验的非随机阶段获得信息；(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 fi制定了决策规则。遵循RDoC的呼吁，将心理健康研究的中心放在潜在结构上 在疾病之间共享，这里开发的方法将被应用于一系列随机对照试验 (RCT)严重抑郁障碍和其他共病障碍患者，包括多个高质量 具有多模式数据(例如，症状、行为测试、心理社会测量、大脑测量)的随机对照试验。这 战略将允许检查跨障碍共享结构的治疗战略，从而将- 折痕可泛化。综上所述，本研究将使用机器学习方法和统计推理 努力更好地利用生物标记物和临床表现之间的复杂相互作用 精准精神病学，目的是为精神障碍患者选择最佳治疗方案。