SCH: INT: Distributed Analytics for Enhancing Fertility in Families

SCH：INT：提高家庭生育能力的分布式分析

• 批准号: 1914792
• 负责人: Ioannis Paschalidis
• 金额: $ 119.98万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914792&HistoricalAwards=false
• 关键词: SCH; INT; Distributed; Analytics; Enhancing; SCH; INT; Distributed; Analytics; Enhancing

The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, involving both men and women, and resulting to more than $5 billion spent annually in infertility services. Such costs are often not covered by health insurance and, consequently, generate access disparities. This project will leverage information from self-administered surveys and medical records to produce highly accurate personalized predictions regarding fertility potential, pregnancy, the success of an In Vitro Fertilization cycle, and the presence of specific reproductive health issues affecting fertility. In addition to predictions, the project will develop methods to generate personalized recommendations, empowering individuals and their physicians to make the most appropriate, individualized health care decisions. The work is in line with the emergence of personalized medicine, aided by data and algorithmic advances. The project will train engineering and computer science graduate students to contribute to medical informatics, involve undergraduate and high school students, impact educational offerings, and, by using data from a safety-net hospital, help understand socioeconomic disparities in the use of infertility treatment services. The predictive and prescriptive models developed in this project will be based on a number of advances in machine learning and analytics, including: (i) new predictive models that handle both continuous and discrete outcomes, are robust to outliers, produce highly accurate personalized predictions, and enable outlier detection; (ii) novel prescriptive models that optimally select from a menu of choices to make recommendations that yield health-centered outcomes; and (iii) natural language processing methods to process clinical reports, culling critical information that can be used to enhance predictive models. To learn from data, the work will develop new distributed optimization and federated learning methods that can train models through interactions among individual data-holding nodes, such as hospital systems, clouds of smartphone applications, existing prospective cohorts, and personal health records. This distributed paradigm does not require data-holding nodes to share raw data, thus enhancing privacy and security.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代生活，教育和职业选择的需求以及辅助生殖技术的可用性，导致许多个人和夫妻推迟生育。这导致了美国的不育和次亲属作为重大公共卫生问题的出现，影响了约15％的夫妇，涉及男女，并每年在不育服务中花费了超过50亿美元。这种费用通常不受健康保险的覆盖，因此产生了访问差异。该项目将利用自我管理调查和医疗记录中的信息来产生有关生育能力，怀孕，体外受精周期的成功以及存在影响生育能力的特定生殖健康问题的高度准确的个性化预测。除了预测外，该项目还将开发出产生个性化建议的方法，赋予个人及其医生的能力，以做出最合适，个性化的医疗保健决定。这项工作符合个性化医学的出现，并在数据和算法进步的帮助下。该项目将培训工程和计算机科学研究生，以促进医学信息学，涉及本科生和高中生，影响教育产品，并通过使用安全网医院的数据，帮助了解社会经济差异在使用不育治疗服务时。该项目中开发的预测性和规范性模型将基于机器学习和分析的许多进步，包括：（i）处理连续和离散结果的新预测模型，对离群值具有鲁棒性，产生高度准确的个性化预测，并启用异常检测； （ii）从选择菜单中最佳选择以提出以健康为中心的结果的新颖规范模型； （iii）自然语言处理方法处理临床报告，挑选可用于增强预测模型的关键信息。为了从数据中学习，这项工作将开发新的分布式优化和联合学习方法，这些方法可以通过单个数据持有的节点之间的互动来培训模型，例如医院系统，智能手机应用程序的云，现有的前瞻性队列和个人健康记录。该分布式范式不需要数据持有的节点来共享原始数据，从而增强了隐私和安全性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估的评估来获得支持的。