III: Medium: Collaborative Research: Detecting and Controlling Network-based Spread of Hospital Acquired Infections

III：媒介：合作研究：检测和控制医院获得性感染的网络传播

• 批准号: 1955939
• 负责人: Sriram Pemmaraju
• 金额: $ 38.18万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955939&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Detecting

Hospital Acquired Infections (HAIs) are becoming a major challenge in health systems worldwide. Detection and control of HAIs are challenging and resource intensive, because of the high costs of patient treatment and disinfection of hospital facilities, making them fundamental public health problems. Despite its huge importance for hospitals, and the interest from both clinical and epidemiological researchers, these problems remain poorly understood. This project seeks to develop a novel network-based approach to improve hospital infection control using models and data science. This proposal brings together a highly multi-disciplinary team of researchers, and will lead to fundamental contributions in different areas of computer science (data mining, machine learning, graph mining, social networks, and optimization), network science (mathematical models and dynamical systems) and computational epidemiology (infectious diseases, and hospital epidemiology). The planned work has immediate implications for public health e.g. it can lead to new design policies and guidance for hospital infection control. Research findings will be incorporated into graduate level classes, tutorials, contests and workshops to bring computational biologists and data miners together. There are several challenges in studying HAI outbreaks primarily because the dynamics of HAI spread are much more complex than other diseases, such as influenza, due to many more factors and pathways involved. To overcome these issues, the project team will use a new class of two-mode cascade models, which have very different dynamics than the standard models, and have not been studied in data mining. The will investigate the following topics: (1) Surveillance, early detection of HAI outbreaks, (2) Designing interventions to control the spread of HAIs, and (3) Modeling and estimating exposure risk for HAIs. A unified set of problems will be considered, including modeling, detection, control and inference of missing infections. These are challenging stochastic optimization problems on networks, and the project team will invent rigorous and scalable methods using tools from data mining, machine learning and combinatorial optimization. Their research will use a unique fine-grained, large-scale data set of operations from a public hospital, supplemented with data from other hospitals. The results will be validated with the help of domain experts including epidemiologists and clinicians involved in hospital infection control.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.

医院获得性感染（HAIs）正在成为世界各地卫生系统面临的主要挑战。由于患者治疗和医院设施消毒费用高昂，因此检测和控制艾滋病是一项具有挑战性和资源密集的工作，使其成为基本的公共卫生问题。尽管这些问题对医院非常重要，而且临床和流行病学研究人员对此都很感兴趣，但人们对这些问题的了解仍然很少。该项目旨在开发一种基于网络的新方法，利用模型和数据科学来改善医院感染控制。该提案汇集了一个高度跨学科的研究团队，并将在计算机科学（数据挖掘，机器学习，图挖掘，社会网络和优化），网络科学（数学模型和动态系统）和计算流行病学（传染病和医院流行病学）的不同领域做出基础贡献。计划中的工作对公共卫生具有直接影响，例如，它可能导致医院感染控制的新设计政策和指导。研究成果将被纳入研究生课程、教程、竞赛和研讨会，将计算生物学家和数据挖掘者聚集在一起。在研究HAI暴发方面存在若干挑战，主要是因为由于涉及更多因素和途径，HAI的传播动态比流感等其他疾病复杂得多。为了克服这些问题，项目团队将使用一类新的双模级联模型，它与标准模型具有非常不同的动态特性，并且尚未在数据挖掘中进行研究。将研究以下主题：(1)监测和早期发现HAI暴发，(2)设计干预措施以控制HAI的传播，以及(3)建模和估计HAI的暴露风险。将考虑一组统一的问题，包括建模、检测、控制和推断缺失感染。这些都是网络上具有挑战性的随机优化问题，项目团队将使用数据挖掘、机器学习和组合优化等工具发明严格且可扩展的方法。他们的研究将使用来自一家公立医院的独特的细粒度、大规模的手术数据集，并辅以其他医院的数据。结果将在领域专家的帮助下得到验证，包括参与医院感染控制的流行病学家和临床医生。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Dynamic Healthcare Embeddings for Improving Patient Care

• DOI: 10.1109/asonam55673.2022.10068627
• 发表时间: 2022-11
• 期刊: 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
• 作者: Hankyu Jang;Sulyun Lee;D. M. H. Hasan;P. Polgreen;Sriram V. Pemmaraju;Bijaya Adhikari Department of Computer Science;U. Iowa;Interdisciplinary Graduate Program in Informatics;Department of Preventive Medicine

Detecting Sources of Healthcare Associated Infections

• DOI: 10.1609/aaai.v37i4.25554
• 发表时间: 2023-06
• 作者: Hankyu Jang;Andrew Fu;Jiaming Cui;M. Kamruzzaman;B. Prakash;A. Vullikanti;B. Adhikari;Sriram V. Pemmaraju

Near-Optimal Spectral Disease Mitigation in Healthcare Facilities

医疗机构中近乎最佳的光谱疾病缓解

• DOI: 10.1109/icdm54844.2022.00121
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM
• 作者: Kiji, Masahiro;Hasibul Hasan, D. M.;Segre, Alberto M.;Pemmaraju, Sriram V.;Adhikari, Bijaya
• 通讯作者: Adhikari, Bijaya