Can machine learning be used to predict antidepressant use outcome longitudinally?

机器学习可以用来纵向预测抗抑郁药物的使用结果吗？

• 批准号: 2277816
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2277816
• 关键词: machine; learning; used; predict; antidepressant

This project aims to understand the long-term effects of antidepressant use in the adult population. There is currently a gap in the literature regarding the longitudinal effects of pharmaceutical interventions for depression, so there is not great clinical understanding of the impact of antidepressants on health outcomes long-term. For example, some longitudinal studies suggest that the symptoms of depression improve overall after long term antidepressant use, but this is reported in tandem with adverse psychological and physical health outcomes like weight gain, sexual dysfunction, and emotional numbness (Dehar et al. 2016). In the psychiatric literature, there is an impoverished understanding of depression causality, with multiple competing hypotheses suggesting genetic, psychological, neurochemical, and neurostructural correlates of the illness. This suggests that depression as an overarching umbrella term could include multiple phenotypes, that if captured, could explain different illness trajectories and predict differential health outcomes after pharmaceutical intervention.The long-term effects of antidepressant use on a wide range of health outcomes should be explored to ascertain whether they are an optimal intervention for people who present with mild to moderate depression and anxiety. This project will apply machine learning and modelling techniques to the Avon Longitudinal Study of Parents and Children (ALSPAC) data set, to develop predictive models of antidepressant use outcomes. For example, significant changes in patient symptom profiles may indicate cognitive, psychological, or behavioural changes after long-term antidepressant use.Initial exploration of the ALSPAC data set will be undertaken using a standard data science workflow, to analyse and understand exposures relevant to antidepressant use outcomes. Advanced machine learning methods will be used, and potentially progressed, in order to optimise novelty of contribution. State of the art anomaly detection will be developed to identify exposures that significantly change outcome probability. Bayesian network models and causal learning methods will be developed to compute the probability of relevant exposures being causally related to measurable patient outcomes, such as behavioural, cognitive, psychological and lifestyle outcomes. Deep clustering algorithms will be used to sub-group patients together, to explore whether certain exposures have more predictive power in long-term mood outcomes than others and factors which may modify associations such as genetic profiles or engagement with psychological therapies .The timeline of the project is still being planned, but the first year would include developing models and running them on test datasets, bespoke dataset construction from existing ALSPAC data, and ALSPAC data familiarity. The second year would be used to run iterations of optimised models and potentially replicate with other cohort studies, with the final year being used for thesis write up and communication of results to relevant clinical bodies such as psychiatrists, GPS, and policy makers through digital health / machine learning publications.The novelty of the project lies in the application of machine learning techniques to ALSPAC data to track antidepressant outcomes over at least a decade. While ML techniques have been applied across a number of digital healthcare domains, their use for mental health monitoring, prediction, and understanding the clinical pathways of patients remains under-researched. This is critically important moving forward, as this work will feed into the increasing use of precision medicine and apps for mental health (e.g., digital phenotyping), whereby having a deeper understanding of the phenotypes underpinning depression and how we can analyse this quickly and efficiently could transform the way we intervene and offer healthcare.

该项目旨在了解抗抑郁药在成年人群中使用的长期影响。目前文献中关于药物干预对抑郁症的纵向影响存在空白，因此对抗抑郁药对健康结果的长期影响没有很好的临床理解。例如，一些纵向研究表明，长期使用抗抑郁药后，抑郁症状总体改善，但这与不良心理和身体健康结局（如体重增加、性功能障碍和情感麻木）同时报告（Dehar et al. 2016）。在精神病学文献中，对抑郁症因果关系的理解很贫乏，多个相互竞争的假设表明疾病与遗传，心理，神经化学和神经结构相关。这表明，抑郁症作为一个总括性术语可能包括多种表型，如果被捕获，可以解释不同的疾病轨迹，并预测药物干预后的不同健康结果。应探讨抗抑郁药使用对广泛健康结果的长期影响，以确定它们是否是轻度至中度抑郁和焦虑患者的最佳干预措施。该项目将机器学习和建模技术应用于雅芳父母和儿童纵向研究（ALSPAC）数据集，以开发抗抑郁药使用结果的预测模型。例如，患者症状特征的显著变化可能表明长期使用抗抑郁药后的认知、心理或行为变化。将使用标准数据科学工作流程对ALSPAC数据集进行初步探索，以分析和了解与抗抑郁药使用结果相关的暴露。先进的机器学习方法将被使用，并可能取得进展，以优化贡献的新奇。将开发最先进的异常检测技术，以识别显著改变结果概率的暴露。将开发贝叶斯网络模型和因果学习方法，以计算与可测量的患者结局（如行为、认知、心理和生活方式结局）存在因果关系的相关暴露的概率。深度聚类算法将被用于将患者分组在一起，以探索某些暴露是否比其他暴露对长期情绪结果具有更大的预测能力，以及可能改变相关性的因素，如遗传特征或心理治疗的参与。该项目的时间轴仍在计划中，但第一年将包括开发模型并在测试数据集上运行，从现有的ALSPAC数据和ALSPAC数据熟悉度定制数据集的建设。第二年将用于运行优化模型的迭代，并可能与其他队列研究重复，最后一年用于撰写论文并将结果传达给相关临床机构，如精神科医生，GPS，和政策制定者通过数字健康/该项目的新奇在于将机器学习技术应用于ALSPAC数据以跟踪抗抑郁药至少十年的成果。虽然机器学习技术已经应用于许多数字医疗领域，但它们用于心理健康监测、预测和理解患者临床路径的研究仍然不足。这对于向前发展至关重要，因为这项工作将为越来越多地使用精准医学和心理健康应用程序提供支持（例如，数字表型），从而更深入地了解抑郁症的表型以及我们如何快速有效地分析这一点，可以改变我们干预和提供医疗保健的方式。