Machine learning-assisted precision mental health for cigarette smoking cessation

机器学习辅助精准戒烟心理健康

• 批准号: 10464420
• 负责人: Gaylen E Fronk
• 金额: $ 4.68万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-19 至 2025-06-18
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10464420
• 关键词: Abstinence; Address; Caring; Characteristics; Clinical; Clinical Data; Clinical Trials; Complex; Data; Data Sources; Development; Effectiveness; Engineering; FDA approved; Fellowship; Genetic; Genetic Models; Goals; Grant; Individual; Individual Differences; Interdisciplinary Study; Knowledge; Machine Learning; Medical Genetics; Mental Health; Mentorship; Methods; Modeling; Outcome; Patients; Performance; Persons; Pharmaceutical Preparations; Phenotype; Population; Prediction of Response to Therapy; Prevalence; Probability; Public Health; Randomized Controlled Trials; Recovery; Research; Sampling; Selection for Treatments; Signal Transduction; Smoke; Smoking; Smoking Cessation Intervention; Substance Use Disorder; Techniques; Therapeutic; Training; Treatment Effectiveness; Treatment outcome; Validation; Work; base; career; cigarette smoking; clinical practice; cost; experience; genetic analysis; health goals; high dimensionality; improved; knowledge integration; machine learning model; non-genetic; novel; precision medicine; prevent; programs; prospective; psychologic; skills; smoking abstinence; smoking cessation; success; treatment response

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.

项目摘要 本奖学金申请的目的是支持和促进申请人的必要培训 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。她的长- 长期研究计划将1）确定和利用可能允许差别待遇的数据源 选择，2）建立机器学习模型来选择治疗方法，3）提出这些模型供使用 在临床实践中，以及4）使用这些模型来告知新的治疗方法的开发，以填补护理空白。 通过拟议的培训目标，指导指导和补充经验，该奖学金将 战略性地推进申请人的职业生涯。她将增加她对物质使用障碍的了解， 治疗。她将获得先进的定量方法的专业知识，包括特征工程，遗传 分析和机器学习。她将完成培训，掌握指挥和传播 跨学科研究准备进一步对药物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方面的应用 条件精确的心理健康指导治疗选择使用个体差异特征可能 来预测治疗的成功有几个因素阻碍了成功选择治疗方法的进展 通过精准的心理健康首先，传统的分析技术不足以解释真实的- 治疗反应和恢复的世界复杂性。第二，精确的心理健康模型 在相同的样本中构建和评估并且因此不推广到新的数据（即，新患者）。 第三，精确的心理健康研究很少包括遗传特征（预测因子）， 与临床（非遗传）数据的交互，防止跨领域知识的整合。 现代机器学习方法非常适合解决这些限制。机 学习模型可以容纳跨数据源的高维特征阵列， - 预测信号，其稳健地推广到患者的新样本，并且结合遗传和非遗传 特色同时拟议的项目将把机器学习应用于精准心理健康 戒烟的典范。吸烟仍然是一个严重和昂贵的公共卫生危机 现有的治疗方法最多只能起到中等效果。拟议的项目将产生一个模型 其可以指导在几个一线（即，FDA批准）戒烟 药物治疗精准心理健康范式在戒烟中的成功应用 将通过加速和优化治疗益处而具有立即的临床影响。该秘书处还将成为 如何改善药物使用障碍的治疗结果的模板。