A Predictive Analytics Approach to the Optimization of Diagnosis, Treatment, and Ambulatory Management of Major Depressive Disorder and Bipolar Disorder

优化重度抑郁症和双相情感障碍的诊断、治疗和门诊管理的预测分析方法

• 批准号: 406067999
• 负责人: Professor Dr. Tim Hahn
• 金额: --
• 依托单位: Klinik für Psychische Gesundheit
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/406067999?language=en
• 关键词: Predictive; Analytics; Approach; Optimization; Diagnosis

Clinicians working with Major Depressive Disorder (MDD) and Bipolar Disorder (BD) patients face multiple challenges, ranging from determining a timely and correct diagnosis and devising an optimal treatment strategy to managing mood episodes in a way that minimizes disease progression and individual suffering. Here, we will now utilize advances in the emerging field of Predictive Analytics in Mental Health to develop, adapt, and implement state-of-the-art machine-learning algorithms directly addressing three most pressing clinical objectives in applied affective disorder research. We aim to construct diagnostic support models 1) differentiating MDD and BD patients, 2) predicting individual response to Electroconvulsive Therapy (ECT) in MDD patients and 3) capable of dynamic real-time relapse-risk prediction based on smartphone data. To this end, we will devise a principled approach dealing with the massively multivariate and multimodal nature of MDD and BD and provide an algorithmic framework aiming to predict real-time relapse-risk. Specifically, we will first develop an unsupervised Deep Learning approach for automated feature-engineering with the goal of representing Magnetic Resonance Imaging data on a lower-dimensional manifold. This will not only alleviate the Curse of Dimensionality, but also provide features which are more invariant to scanner sites and acquisition protocols, enabling the seamless construction of more robust multi-center models. Second, we will employ state-of-the-art data integration methodology to fuse genetic, psychometric, and neuroimaging information, enabling MDD vs. BD classification and ECT response prediction. Building a multimodal model comprising the three most heavily investigated data sources in affective disorder research will – for the first time – provide empirical evidence regarding the question to what extent combining patient characteristics commonly measured in psychiatry improves model performance. Third, we will develop a model for real-time relapse-risk prediction based on smartphone data by evaluating algorithms which conceptualize relapse-risk 1) as a deviation from the patterns present during symptom-free periods and 2) as a critical transition from a Complex Systems perspective. Building on multimodal neuroimaging, genetic and psychometric data already available to us (total N>41,000) as well as on the smartphone-based dataset currently acquired in an associated multicenter project (total N > 2,000), we will fully focus on advancing state-of-the-art machine learning methodology in affective disorders research and facilitate the construction of predictive models with direct relevance for clinical practice. Bringing together the largest datasets available and the most powerful machine learning algorithms emerging today opens up the unique opportunity to catalyze translational efforts in psychiatry, moving the field from proof-of-concept studies towards first clinical applications.

治疗重度抑郁症（MDD）和双相情感障碍（BD）患者的临床医生面临着多重挑战，从确定及时和正确的诊断，制定最佳治疗策略，到以最小化疾病进展和个体痛苦的方式管理情绪发作。在这里，我们现在将利用心理健康预测分析这一新兴领域的进展来开发、适应和实施最先进的机器学习算法，直接解决应用情感障碍研究中三个最紧迫的临床目标。我们的目标是构建诊断支持模型：1)区分MDD和BD患者；2)预测MDD患者对电痉挛治疗（ECT）的个体反应；3)能够基于智能手机数据动态实时预测复发风险。为此，我们将设计一种原则性的方法来处理MDD和BD的大规模多元和多模态性质，并提供一个旨在预测实时复发风险的算法框架。具体来说，我们将首先开发一种用于自动特征工程的无监督深度学习方法，目标是在低维流形上表示磁共振成像数据。这不仅可以缓解维度诅咒，还可以提供对扫描站点和采集协议更不变性的特性，从而实现更健壮的多中心模型的无缝构建。其次，我们将采用最先进的数据集成方法来融合遗传、心理测量和神经影像学信息，从而实现MDD与BD的分类和ECT反应预测。建立一个包含情感性障碍研究中三个最深入调查的数据源的多模态模型将-首次-提供关于在多大程度上结合精神病学中通常测量的患者特征改善模型性能的问题的经验证据。第三，我们将开发一个基于智能手机数据的实时复发风险预测模型，通过评估将复发风险概念化的算法：1)从无症状期出现的模式的偏离；2)从复杂系统的角度来看，复发风险是一个关键的转变。基于我们已经获得的多模态神经成像、遗传和心理测量数据（总计41,000个）以及目前在相关多中心项目中获得的基于智能手机的数据集（总计2,000个），我们将完全专注于在情感障碍研究中推进最先进的机器学习方法，并促进与临床实践直接相关的预测模型的构建。汇集最大的可用数据集和当今出现的最强大的机器学习算法，为促进精神病学的转化工作提供了独特的机会，使该领域从概念验证研究转向首次临床应用。