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

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，导致许多个人和夫妇推迟生育。这导致不孕不育和生育能力低下成为美国的重大公共卫生问题，影响了约 15% 的夫妇（包括男性和女性），并导致每年在不孕不育服务上的支出超过 50 亿美元。此类费用通常不包含在健康保险范围内，因此会产生获取差异。该项目将利用自我管理的调查和医疗记录中的信息，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、个性化的医疗保健决策。这项工作与个性化医疗的出现相一致，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，影响教育服务，并通过使用安全网医院的数据，帮助了解使用不孕不育治疗服务的社会经济差异。该项目开发的预测和规范模型将基于机器学习和分析领域的多项进步，包括：（i）新的预测模型，可以处理连续和离散结果，对异常值具有鲁棒性，产生高度准确的个性化预测，并实现异常值检测； (ii) 新颖的规定模型，从选择菜单中进行最佳选择，提出产生以健康为中心的结果的建议； (iii)自然语言处理方法来处理临床报告，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，这些方法可以通过各个数据保存节点（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）之间的交互来训练模型。这种分布式范式不需要数据持有节点共享原始数据，从而增强了隐私和安全性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Improved prediction of MHC-peptide binding using protein language models.

• DOI: 10.3389/fbinf.2023.1207380
• 发表时间: 2023
• 期刊: FRONTIERS IN BIOINFORMATICS
• 作者: Hashemi, Nasser;Hao, Boran;Ignatov, Mikhail;Paschalidis, Ioannis Ch;Vakili, Pirooz;Vajda, Sandor;Kozakov, Dima
• 通讯作者: Kozakov, Dima

Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers

分布式鲁棒多类分类及其在深度图像分类器中的应用

• DOI: 10.1109/icassp49357.2023.10095775
• 发表时间: 2023
• 期刊: and Signal Processing (ICASSP
• 作者: Chen, Ruidi;Hao, Boran;Paschalidis, Ioannis Ch.
• 通讯作者: Paschalidis, Ioannis Ch.

Predictive models of pregnancy based on data from a preconception cohort study

• DOI: 10.1093/humrep/deab280
• 发表时间: 2022-03-01
• 期刊: HUMAN REPRODUCTION
• 影响因子: 6.1
• 作者: Yland, Jennifer J.;Wang, Taiyao;Paschalidis, Ioannis Ch
• 通讯作者: Paschalidis, Ioannis Ch

Communication-efficient SGD: From Local SGD to One-Shot Averaging

• 发表时间: 2021-06
• 作者: Artin Spiridonoff;Alexander Olshevsky;I. Paschalidis

A Prospective Cohort Study of COVID-19 Vaccination, SARS-CoV-2 Infection, and Fertility.

• DOI: 10.1093/aje/kwac011
• 发表时间: 2022-07-23
• 期刊: American journal of epidemiology
• 影响因子: 5
• 作者: Wesselink AK;Hatch EE;Rothman KJ;Wang TR;Willis MD;Yland J;Crowe HM;Geller RJ;Willis SK;Perkins RB;Regan AK;Levinson J;Mikkelsen EM;Wise LA
• 通讯作者: Wise LA