Harnessing Patient Generated Data to Find Causes and Effects of Diet in Pregnancy

利用患者生成的数据来查找怀孕期间饮食的原因和影响

• 批准号: 9980490
• 负责人: ANDREA L DEIERLEIN
• 金额: $ 28.21万
• 依托单位: STEVENS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980490
• 关键词: Accelerometer; Address; Affect; Algorithms; Beds; Birth; Blood Glucose; Calories; Chronic Disease; Classification; Clinical; Data; Data Set; Development; Diabetes Mellitus; Diabetic Diet; Diet; Dietary Factors; Dietary intake; Disease; Event; Food; Gestational Diabetes; Glucose; Health; Health Status; Heart Rate; Hospitals; Individual; Life; Life Style; Macronutrients Nutrition; Measurement; Measures; Meat; Medical; Methods; Modeling; Motivation; Non-Insulin-Dependent Diabetes Mellitus; Ontology; Outcome; Patients; Population; Pregnancy; Prevalence; Proteins; Psychological Transfer; Public Health; Public Health Informatics; Resources; Risk; Risk Factors; Sampling; Series; Source; Staging; Stress; Structure; System; Temperature; Testing; Time; To specify; Weight; Work; automated algorithm; cohort; design; diabetes risk; health data; heterogenous data; improved; insight; novel; nutrition; pregnant; wearable sensor technology

Enormous amounts of biomedical data are generated by hospitals, but most of this data is available only after people become ill. For people with chronic diseases such as diabetes, though, many important events happen outside of the medical system. Patient generated health data (PGHD) can provide detailed insight into an individual's health during daily life. With longterm continuous glucose data, activity data, and food logs, we could develop personalized models of how factors affect blood glucose and deliver personalized guidance to patients on how to better manage it. Transforming PGHD into information to guide decisions is a highly general problem that applies to all forms of diabetes, and other chronic diseases. We specifically focus on identifying dietary and lifestyle risk factors for gestational diabetes mellitus (GDM). GDM occurs in 9% of pregnancies, and leads to a 7-fold increase in Type 2 Diabetes risk after birth, making it a significant public health problem. Pregnancy provides an ideal test bed for methods designed to make use of PGHD and uncover causes, as outcomes can be captured in a limited study duration. Motivated by trying to find causes and effects of nutrition in pregnancy, we develop generalizable algorithms that address widespread challenges in the use of PGHD for causal inference. First, existing causal inference methods assume we have well-defined variables (e.g. bodyweight), but nutrition can be measured in many ways (calories, macronutrients, food groups). This puts a large burden on users, and limits the potential for data-driven inference. We introduce the first causal inference algorithm that automatically identifies optimal variable granularity for each relationship, by leveraging ontologies. This allows identification of different effects between, say, protein and specific meats on health outcomes, without users needing to specify such hypotheses. Second, while individual level data is essential for personalized inference, only limited data may be available when a treatment decision must be made or when health status is changing over time, such as during pregnancy. Leveraging population data can yield more accurate inferences, but existing methods are unable to identify relevant data dynamically and pregnant individuals may be more similar to others at the same stage of pregnancy than to themselves in the recent past. We introduce new methods for dynamic causal transfer learning that continually identify and adapt relevant population data for personalized causal inference. We initially test our approach on publicly available ICU, diabetes, and nutrition datasets, before collecting a unique dietary and activity dataset from 150 pregnant individuals.

医院产生了大量的生物医学数据，但这些数据中的大多数只能在 在人们生病之后。然而，对于糖尿病等慢性病患者来说， 发生在医疗系统之外。患者生成的健康数据（PGHD）可以提供详细的见解 在日常生活中影响个人健康。通过长期连续血糖数据、活动数据和食物 日志，我们可以开发个性化模型的因素如何影响血糖，并提供个性化的 指导患者如何更好地管理它。将PGHD转化为指导决策的信息， 这是一个非常普遍的问题，适用于所有形式的糖尿病和其他慢性疾病。我们特别 重点是确定饮食和生活方式的危险因素，妊娠糖尿病（GDM）。GDM发生在 9%的怀孕，并导致出生后2型糖尿病风险增加7倍，使其成为一个显着的 公共卫生问题。妊娠为使用PGHD的方法提供了理想的试验平台 并发现原因，因为可以在有限的研究时间内获得结果。动机是想找到 的原因和影响的营养在怀孕期间，我们制定了可推广的算法，解决广泛的 使用PGHD进行因果推理的挑战。首先，现有的因果推理方法假设我们 有明确定义的变量（例如体重），但营养可以通过多种方式测量（卡路里， 宏量营养素、食物组）。这给用户带来了很大的负担，并限制了数据驱动的潜力。 推论我们介绍了第一个因果推理算法，自动识别最佳变量 每个关系的粒度，通过利用本体。这允许识别不同的影响 例如，蛋白质和特定肉类之间的健康结果，而无需用户指定此类 假设其次，虽然个人水平的数据对于个性化推断是必不可少的，但只有有限的数据 当必须做出治疗决定或当健康状况随时间变化时， 例如在怀孕期间。利用人口数据可以产生更准确的推论，但现有的 方法不能动态地识别相关数据，并且怀孕个体可能与 其他人在怀孕的同一阶段比自己在最近的过去。我们引入新的方法 动态因果迁移学习，不断识别和调整相关的人口数据， 个性化因果推理我们首先在公开的ICU、糖尿病和 营养数据集，然后从150名孕妇中收集独特的饮食和活动数据集。