Using natural language processing to determine predictors of healthy diet and physical activity behavior change in ovarian cancer survivors

摘要 癌症幸存者是美国不断增长的人口；目前有1600多万人生活在美国和 到2030年，这一数字预计将超过2200万。据估计，超过50%的新癌症 可以通过结合健康行为(例如，体育活动和健康饮食)来消除病例； 癌症幸存者罹患新癌症和复发癌症的风险很高。不幸的是，一个重要的 癌症幸存者的百分比没有达到健康饮食和身体健康的癌症预防指南 活动。在过去的几十年里，各种基于电话的生活方式干预已经证明 帮助幸存者达到癌症预防指南的有效性，然而这些试验是劳动密集型的 而且交付成本高昂，限制了它们广泛传播的潜力。我们建议通过以下方式解决这一障碍 利用人工智能领域的最新进展来降低成本并最大限度地发挥这些 急需的干预措施。机器学习(ML)和自然语言处理(NLP)是分析的 自动从数据中的直接和间接模式学习的技术。我们建议使用机器学习 分析语音的算法，以帮助预测谁可能面临采用健康生活方式不良的风险 行为。这些语音数据将来自生活方式干预提高卵巢癌生存率 (Lives)研究，一项基于电话的生活方式干预测试低脂肪和高蔬菜的饮食， 水果和纤维，加上增加的体力活动，将增加1200卵巢疾病进展的时间 与注意力控制组相比，最近完成治疗的癌症幸存者。干预 教练使用激励性访谈来引发行为改变，所有关于LIFE试验的电话都是 记录了对饮食、体力活动、患者报告和临床结果的重复评估。我们将使用 这个现有的和健壮的纵向数据集将会话语音数据与显式结果配对，以 实现以下目标。1)开发ML模型以识别教练之间交互的模式 以及他们的参与者发出了饮食和体力活动发生最佳行为改变的可能性的信号 全面的生活数据集，利用语音记录的通话、人口统计以及临床和患者报告 在多个时间点收集的结果。2)将最大似然模型按“干预因素”进行分解，因此 该参与者影响、指导对干预方案的遵守，以及 可以单独评估交互在预测行为变化方面的作用，以及遵守 干预目标。这种分解将直接实现对干预计划的及早和有针对性的调整 对个体而言，降低干预成本，提高干预效果。ML和NLP方法可以 建立倾听教练对话的模型，并自动预测对话是否会产生积极结果 改变制定健康的生活方式。这样的预测模型将能够更有效地， 有效的、个性化的生活方式干预，是迈向个性化行为医学的第一步。

ABSTRACT Cancer survivors are a growing population in the United States; more than 16 million currently live in the US and by 2030 this number is expected to exceed 22 million. It is estimated that more than 50 percent of new cancer cases could be eliminated through a combination of healthy behaviors (e.g., physical activity and healthy diet); and cancer survivors are at high risk for developing new and recurrent cancer. Unfortunately, a significant percentage of cancer survivors are not attaining the cancer preventive guidelines of healthy diet and physical activity. In the past few decades, a variety of telephone-based lifestyle interventions have demonstrated effectiveness in helping survivors meet cancer preventive guidelines, however these trials are labor intensive and expensive to deliver, limiting their potential for broad dissemination. We propose to address this hurdle by taking advantage of recent advances in artificial intelligence to reduce the cost and maximize the impact of these much-needed interventions. Machine learning (ML) and Natural Language Processing (NLP) are analytical techniques that automatically learn from direct and indirect patterns in data. We propose to use machine learned algorithms to analyze speech to aid in predicting who may be at risk of poor adoption of healthy lifestyle behaviors. These speech data will come from the Lifestyle Intervention for Ovarian cancer Enhanced Survival (LIVES) study, a telephone-based lifestyle intervention testing whether a diet low in fat and high in vegetables, fruit, and fiber, coupled with increased physical activity will increase time to disease progression in 1200 ovarian cancer survivors who have recently completed treatment, as compared to an attention control. Intervention coaches employed motivational interviewing to elicit behavior change and all calls on the LIVES trial were recorded with repeat assessments of diet, physical activity, patient reported and clinical outcomes. We will use this existing and robust longitudinal data set, which pairs conversational speech data with explicit outcomes, to achieve the following objectives. 1) Develop a ML model to identify patterns in the interactions between coaches and their participants that signal a likelihood of optimal behavior change in diet and physical activity given the comprehensive LIVES data set, utilizing voice recorded calls, demographics, and clinical and patient reported outcomes collected at multiple time points. 2) Decompose the ML model in terms of “intervenable factors”, so that participant affect, coach adherence to the intervention protocol, and other important aspects of the interaction can be individually evaluated for their role in predicting behavior change, as well as adherence to intervention goals. This decomposition will directly enable early and targeted adjustments to intervention plans for individuals, reducing the cost and increasing the efficacy of intervention strategies. ML and NLP methods can produce models that listen to a coaching conversation and automatically predict whether it will result in positive change towards enactment of healthy lifestyle behaviors. Such predictive models would enable more efficient, effective, and individualized lifestyle interventions, the first step towards personalized behavioral medicine.