SCH: INT: Distributed Analytics for Enhancing Fertility in Families

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，导致许多个人和夫妇推迟生育。这导致不孕症和低生育率成为美国重要的公共卫生问题，影响了大约15%的夫妇，包括男性和女性，导致每年用于不孕症服务的费用超过50亿美元。这些费用往往不在医疗保险范围之内，因此造成了获得机会的不平等。该项目将利用来自自我管理调查和医疗记录的信息，对生育潜力、怀孕、体外受精周期的成功以及影响生育力的特定生殖健康问题的存在进行高度准确的个性化预测。除了预测之外，该项目还将开发生成个性化建议的方法，使个人及其医生能够做出最合适的个性化医疗保健决策。这项工作符合个性化医疗的出现，并得到了数据和算法进步的帮助。该项目将培训工程和计算机科学研究生为医学信息学做出贡献，让本科生和高中生参与进来，影响教育产品，并通过使用安全网医院的数据，帮助了解不孕症治疗服务的社会经济差异。该项目中开发的预测和规范模型将基于机器学习和分析的许多进展，包括：（i）新的预测模型，可处理连续和离散结果，对离群值具有鲁棒性，可产生高度准确的个性化预测，并实现离群值检测; ㈡新颖的处方模式，从一系列选择中进行最佳选择，以提出产生以健康为中心的成果的建议;以及（iii）处理临床报告的自然语言处理方法，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保存节点之间的交互来训练模型，例如医院系统，智能手机应用程序云，现有的预期队列和个人健康记录。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.