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

使用自然语言处理确定卵巢癌幸存者健康饮食和身体活动行为变化的预测因子

• 批准号: 10510666
• 负责人: Steven Bethard
• 金额: $ 18.19万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10510666
• 关键词: Address; Adherence; Adoption; Affect; American; American Cancer Society; Artificial Intelligence; Behavior; Behavioral; Behavioral Medicine; Cancer Survivor; Characteristics; Chronic Disease; Clinical; Clinical Data; Computers; Coupled; Data; Data Set; Diet; Dimensions; Disease Progression; Educational Intervention; Effectiveness; Fatty acid glycerol esters; Fiber; Fruit; Funding; Goals; Guidelines; Health; Health education; Hour; Human; Individual; Institutes; Intervention; Language; Learning; Life Style; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Methods; Modeling; Monitor; Natural Language Processing; Neural Network Simulation; Outcome; Participant; Patient Outcomes Assessments; Patients; Pattern; Personal Satisfaction; Physical activity; Population; Preventive; Protocols documentation; Provider; Randomized; Recommendation; Recurrent Malignant Neoplasm; Reporting; Risk; Role; Signal Transduction; Speech; Survivors; System; Techniques; Telephone; Testing; Time; Training; Treatment Efficacy; Trial of Labor; United States; Vegetables; Voice; anticancer research; attentional control; behavior change; behavior prediction; behavioral adherence; behavioral outcome; cancer therapy; cost; demographics; experience; good diet; healthy lifestyle; healthy weight; high risk; improved; interest; lifestyle data; lifestyle intervention; longitudinal dataset; machine learning algorithm; machine learning model; motivational enhancement therapy; novel; nutrition; outcome prediction; predictive modeling; prevent; programs; telephone coaching; telephone-based

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.

抽象的 美国的癌症幸存者人数不断增长；目前有超过 1600 万人生活在美国，并且 到 2030 年，这一数字预计将超过 2200 万，估计超过 50% 是新发癌症。 可以通过健康行为（例如身体活动和健康饮食）的结合来消除病例； 不幸的是，癌症幸存者患新发癌症和复发癌症的风险很高。 癌症幸存者的百分比未达到健康饮食和身体健康的癌症预防指南 在过去的几十年里，各种基于电话的生活方式干预措施已经被证明。 有效帮助幸存者满足癌症预防指南，但这些试验是劳动密集型的 且交付成本昂贵，限制了其广泛传播的潜力。我们建议通过以下方式解决这一障碍。 利用人工智能的最新进展来降低成本并最大限度地发挥这些影响 机器学习 (ML) 和自然语言处理 (NLP) 是分析性的干预措施。 我们建议使用机器学习的技术，自动从数据中的直接和间接模式中学习。 分析语音的算法，以帮助预测谁可能面临健康生活方式不良的风险 这些语音数据将来自卵巢癌增强生存的生活方式干预。 （LIVES）研究，一项基于电话的生活方式干预测试是否低脂肪和高蔬菜饮食， 第 1200 章 与注意力干预相比，最近完成治疗的癌症幸存者。 教练采用动机性访谈来引发行为改变，并且 LIVES 试验中的所有电话均经过 我们将使用对饮食、体力活动、患者报告和临床结果的重复评估进行记录。 这个现有的、强大的纵向数据集将会话语音数据与明确的结果配对，以 实现以下目标 1) 开发 ML 模型来识别教练之间交互的模式。 以及他们的参与者发出信号，考虑到饮食和身体活动发生最佳行为改变的可能性 全面的 LIVES 数据集，利用语音通话记录、人口统计数据以及临床和患者报告 在多个时间点收集的结果 2）根据“可干预因素”分解机器学习模型，因此 影响教练对干预方案的遵守以及参与者的其他重要方面 可以单独评估互动在预测行为变化以及遵守行为方面的作用 这种分解将直接促进干预计划的早期和有针对性的调整。 对于个人来说，降低机器学习和自然语言处理方法的成本并提高其有效性。 生成能够聆听教练对话并自动预测是否会产生积极结果的模型 朝着制定健康的生活方式行为的转变，这种预测模型将实现更高效、更高效的目标。 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。