Machine learning-assisted precision mental health for cigarette smoking cessation

项目摘要 此奖学金申请的目的是支持和促进申请者所需的培训。 在药物使用障碍的精确心理健康方面发展独立的研究事业。她长长的- 学期研究计划将1)确定和利用可能允许区别对待的数据源 选择，2)建立机器学习模型以在处理中进行选择，3)提出这些模型以供使用 在临床实践中，以及4)使用这些模型来为新疗法的开发提供信息，以填补护理方面的空白。 通过建议的培训目标、指导指导和补充经验，这种团契将 战略性地推进申请者的职业生涯。她将增加对药物使用障碍的了解，并 治疗。她将获得先进的量化方法方面的专业知识，包括特征工程、遗传学 分析和机器学习。她将完成她的培训，掌握指挥和传播的技能 跨学科研究有望进一步对物质使用障碍进行精确的心理健康研究。 拟议的项目旨在将机器学习应用于吸烟的精确心理健康。 停止。精准心理健康是精准医学范式在心理健康领域的应用 条件。精准的心理健康指导治疗选择可能利用个体差异特征 来预测治疗的成功。有几个因素阻碍了成功选择治疗的进展。 通过精确的心理健康。首先，传统的分析技术已经不足以解释真实的- 作为治疗反应和康复基础的世界复杂性。第二，精准的心理健康模型是 在同一样本中建立和评估，因此不适用于新数据(即新患者)。 第三，精确的心理健康研究很少将遗传特征(预测因素)与基因特征(预测因素)放在一起 与临床(非遗传)数据的交互，阻止跨域的知识集成。 当代的机器学习方法非常适合于解决这些限制。机器 学习模型可以适应跨数据源的高维要素数组，提取可靠的 预测信号有力地推广到新的患者样本，并结合了遗传和非遗传 同时提供多种功能。拟议的项目将把机器学习应用于精确的心理健康 戒烟范例。吸烟仍然是一个严重且代价高昂的公共健康危机 对于这种疾病，现有的治疗方法充其量只能起到适度的效果。拟议的项目将产生一个模型 这可以指导在几种一线(即FDA批准的)戒烟方法中进行治疗选择 药物。精准心理健康范式在戒烟中的成功应用 通过加速和优化治疗效益，将立即产生临床影响。它还将作为 如何改善药物使用障碍的治疗结果的模板。

Project Summary The goal of this fellowship application is to support and facilitate the necessary training for the applicant to develop an independent research career in precision mental health for substance use disorders. Her long- term program of research will 1) identify and harness data sources that may allow for differential treatment selection, 2) build machine learning models to select among treatments, 3) bring these models forward for use in clinical practice, and 4) use these models to inform the development of novel treatments to fill gaps in care. Through the proposed training goals, guided mentorship, and complementary experiences, this fellowship will strategically advance the applicant’s career. She will increase her knowledge of substance use disorders and treatments. She will gain expertise in advanced quantitative methods including feature engineering, genetic analyses, and machine learning. She will complete her training with the skills to conduct and disseminate interdisciplinary research poised to further precision mental health research for substance use disorders. The proposed project seeks to apply machine learning to precision mental health for cigarette smoking cessation. Precision mental health is the application of the precision medicine paradigm to mental health conditions. Precision mental health guides treatment selection using individual differences characteristics likely to predict treatment success. Several factors have hindered progress towards successful treatment selection via precision mental health. First, traditional analytic techniques have been insufficient to account for the real- world complexities that underlie treatment response and recovery. Second, precision mental health models are built and evaluated in the same sample and consequently do not generalize to new data (i.e., new patients). Third, precision mental health research has rarely included genetic features (predictors) alongside and in interaction with clinical (non-genetic) data, preventing integration of knowledge across domains. Contemporary machine learning approaches are well-suited to address these limitations. Machine learning models can accommodate high-dimensional arrays of features across data sources, extract reliable prediction signals that generalize robustly to new samples of patients, and incorporate genetic and non-genetic features simultaneously. The proposed project will apply machine learning to the precision mental health paradigm for cigarette smoking cessation. Cigarette smoking remains a critical and costly public health crisis for which existing treatments are only moderately effective at best. The proposed project will produce a model that can guide treatment selection among several first-line (i.e., FDA-approved) smoking cessation medications. Successful application of the precision mental health paradigm to cigarette smoking cessation would have immediate clinical impact by accelerating and optimizing therapeutic benefit. It would also serve as a template for how to improve treatment outcomes across substance use disorders.