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

该项目旨在了解成年人使用抗抑郁药的长期影响。目前，关于药物干预对抑郁症的纵向影响的文献存在空白，因此，对抗抑郁药对健康结果的长期影响的临床理解并不多。例如，一些纵向研究表明，长期使用抗抑郁药后，抑郁症的症状总体上有所改善，但据报道，这与体重增加、性功能障碍和情绪麻木等不利的心理和身体健康结果有关（Dehar et al. 2016）。在精神病学文献中，人们对抑郁症的因果关系知之甚少，有多种相互竞争的假设，表明这种疾病与遗传、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。应探索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。该项目将把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，患者症状谱的显著变化可能表明长期使用抗抑郁药后认知、心理或行为发生变化。将使用标准的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。为了优化贡献的新颖性，将使用先进的机器学习方法，并可能取得进展。最先进的异常检测将被开发出来，以识别那些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，以计算相关暴露与可测量的患者结果（如行为，认知，心理和生活方式结果）因果相关的概率。深度聚类算法将用于对患者进行分组，以探索某些暴露是否比其他暴露对长期情绪结果具有更强的预测能力，以及可能改变遗传概况或参与心理治疗等关联的因素。项目的时间表仍在规划中，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数据构建定制数据集，以及对ALSPAC数据的熟悉程度。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进行复制，最后一年将用于撰写论文，并通过数字健康/机器学习出版物将结果传达给相关临床机构，如精神病学家、GPS和政策制定者。该项目的新颖之处在于将机器学习技术应用于ALSPAC数据，以追踪至少十年的抗抑郁药物效果。虽然机器学习技术已应用于许多数字医疗保健领域，但它们在精神健康监测、预测和理解患者临床路径方面的应用仍有待研究。这是至关重要的进展，因为这项工作将有助于越来越多地使用精准医学和心理健康应用程序（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些表型，从而改变我们干预和提供医疗保健的方式。

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.