Developing Smokers for Smoker (S4S): A Collective Intelligence tailoring system

描述(申请人提供)：吸烟仍然是癌症死亡的头号可预防原因。需要新的方法来吸引21世纪的吸烟者戒烟。我建议开发S4S(吸烟者为吸烟者)，这是下一代以患者为中心的计算机定制健康通信(CTHC)系统。与目前基于规则的CTHC不同，S4将用复杂的机器学习算法取代规则，并利用参与网络辅助烟草干预的数千名吸烟者的集体经验，为进入系统的新吸烟者加强与个人相关的定制。NCI K07将提供有指导的研究经验，让我有机会在癌症健康沟通方面获得新的能力，以改变行为、研究设计和临床试验实施和评估的统计方法。我将修改亚马逊和谷歌等公司在医疗保健领域使用的Collectiv智能算法，以增强CTHC。利用来自科学专家的知识，目前的CTHC收集基线患者“档案”，然后使用专家编写的、基于规则的系统来为患者子集定制消息。这种基于理论的“市场细分”在帮助患者实现生活方式目标方面一直是有效的。然而，基于规则的系统真正个性化内容并随着时间的推移适应个性化的能力是自然有限的。目前的CTHC已经达到了这个限制，我建议超越这个限制。我的第一个目标是开发Web2.0“S4S”推荐系统。我的第二个目标是在NCI资助的网络辅助烟草干预(Decide2Quit.org)的背景下评估S4。在我的努力中，我将在我的主要导师(休斯顿博士，医学硕士)和另外两个导师团队的指导下进行工作：癌症健康行为和沟通团队(Stehenie Lemon，博士和凯瑟琳·马佐，EDD)，以及临床试验设计和分析团队(Jeroan Allison，医学，硕士和Arlene Ash，博士)。我的综合培训还包括课程作业、研讨会和会议。

DESCRIPTION (provided by applicant): Smoking is still the number one preventable cause of cancer death. New approaches are needed to engage smokers in the 21st century in smoking cessation. I propose to develop S4S (Smokers for Smoker), a next- generation patient-centered computer tailored health communication (CTHC) system. Unlike current rule- based CTHCs, S4S will replace rules with complex machine learning algorithms, and use the collective experiences of thousands of smokers engaged in a web-assisted tobacco intervention to enhance personally- relevant tailoring for new smokers entering the system. This NCI K07 will provide a mentored research experience, giving me the opportunity to acquire new competencies in cancer health communication for behavior change, research design and statistical methods underlying clinical trial implementation and evaluation. I will adapt collectiv intelligence algorithms that have been used outside healthcare by companies like Amazon and Google to enhance CTHC. Using knowledge from scientific experts, current CTHC collect baseline patient "profiles" and then use expert-written, rule-based systems to tailor messages to patient subsets. Such theory-based "market segmentation has been effective in helping patients reach lifestyle goals. However, there is a natural limit in the ability of a rule-based system to truly personalize content, and adapt personalization over time. Current CTHC have reached this limit, and I propose to go beyond. My first aim is to develop the Web 2.0 "S4S" recommender system. My second aim is to evaluate S4S within the context of a NCI funded web-assisted tobacco intervention (Decide2Quit.org). In my efforts, I will guided by my primary mentor (Dr. Houston, MD MPH) and two other mentor teams: The Cancer Health Behavior and Communication Team (Stephenie Lemon, PhD and Kathleen Mazor, EdD), and the Clinical Trial Design and Analysis Team (Jeroan Allison, MD, MS and Arlene Ash, PhD). My comprehensive training also includes coursework, seminars, and conferences.