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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

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Multi-omic Microbiome Profiles in the Female Reproductive Tract in Early Pregnancy

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Infectious Microbes & Diseases 2019年第2期1卷 49-60页

作者： Sophonie Jean Bernice Huang Hardik I.Parikh David J.Edwards J.Paul Brooks Naren Gajenthra Kumar Nihar U.Sheth Vishal Koparde Ekaterina Smirnova Snehalata Huzurbazar Philippe H.Girerd Dayanjan S.Wijesinghe Jerome F.StraussIII Myrna G.Serrano Jennifer M.Fettweis Kimberly K.Jefferson Gregory A.Buck Department of Microbiology and Immunology Virginia Commonwealth UniversityRichmondVA Center for Microbiome Engineering and Data Analysis Virginia Commonwealth UniversityRichmondVA Department of Statistical Sciences and Operations Research Virginia Commonwealth UniversityRichmondVA Supply Chain Management and Analytics School of BusinessVirginia Commonwealth UniversityRichmondVA Life Sciences Virginia Commonwealth UniversityRichmondVA DNAnexus Inc. Mountain ViewCA Frederick National Laboratory for Cancer Research FrederickMD Department of Biostatistics Virginia Commonwealth UniversityRichmondVA Department of Statistics University of WyomingLaramieWY Obstetrics and Gynecology Virginia Commonwealth UniversityRichmondVA Pharmacotherapy and Outcomes Science Virginia Commonwealth UniversityRichmondVA Department of Computer Science Virginia Commonwealth UniversityRichmondVA.

The vaginal microbiome likely influences host signaling compounds within the reproductive tract,including pro-inflammatory signals,which may play an important role during *** lactobacilli are associated with positive pregnancy outcome,whereas bacterial vaginosis,a dysbiosis of the vaginal microbiome,is associated with an increased risk of adverse pregnancy outcomes including preterm *** the host response could be predicted based on the taxonomic composition of the vaginal microbiome,particularly early in pregnancy,then those predictions could potentially be used to personalize intervention methods to reduce preterm birth and other adverse *** this proof of principle study,we apply multivariate strategies to analyze 16S rRNA-based taxonomic surveys in conjunction with targeted immuno-proteomic and lipidomic data from vaginal samples from 58 women enrolled in the Multi-Omic Microbiome Study-Pregnancy Initiative during early *** between the vaginal microbiome and the vaginal lipidome have not been previously *** from this study reveal significant multiple pairwise associations between microbial taxa,specific eicosanoids and sphingomyelins,and *** the biologic significance of these associations is not yet known,these results support the utility of such multi-omic approaches as a means to predict the impact of the microbiome on the host.

关键词： vaginal microbiome preterm birth multi-omics metagenomics cytokine profiles lipidomics

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Network reinforcement driven drug repurposing for Covid-19 by exploiting disease-gene-drug associations

arXiv

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arXiv 2020年

作者： Nam, Yonghyun Yun, Jae-Seung Lee, Seung Mi Park, Ji Won Chen, Ziqi Lee, Brian Verma, Anurag Ning, Xia Shen, Li Kim, Dokyoon Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia United States Division of Endocrinology and Metabolism Department of Internal Medicine St. Vincent’s Hospital College of Medicine Catholic University of Korea Seoul Korea Republic of Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul Korea Republic of Department of Surgery Seoul National University College of Medicine Seoul Korea Republic of Computer Science and Engineering Department College of Engineering Ohio State University Columbus United States Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia United States Biomedical Informatics Department College of Medicine Ohio State University Columbus United States Translational Data Analytics Institute Ohio State University Columbus United States Institute for Biomedical Informatics University of Pennsylvania Philadelphia United States

Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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IROS 2019 lifelong robotic vision challenge lifelong object recognition report

arXiv

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arXiv 2020年

作者： She, Qi Feng, Fan Liu, Qi Chan, Rosa H.M. Hao, Xinyue Lan, Chuanlin Yang, Qihan Lomonaco, Vincenzo Parisi, German I. Ma, Liang Wu, Jianwen Zhong, Qiaoyong Xie, Di Pu, Shiliang Graffieti, Gabriele Pellegrini, Lorenzo Lomonaco, Vincenzo Maltoni, Davide Wang, Zhengwei Brophy, Eoin Ward, Tomás E. Zhou, Liguang Yang, Lin Goel, Vidit Sheet, Debdoot Kumar, Somesh Song, Soonyong Bae, Heechul Han, Hyonyoung Son, Youngsung Piyasena, Duvindu Kanagarajah, Sathursan Lam, Siew-Kei Wu, Meiqing Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic Engineering Tsinghua University Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Wuhan University Wuhan China ContinualAI Research University of Bologna Italy ContinualAI Research University of Hamburg Germany Intel Labs and ContinualAI Research Hikvision Research Institute Hangzhou China University of Bologna Bologna Italy V-SENSE School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Inisht Centre for Data Analytics School of Computing Dublin City University Dublin Ireland Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Peking University Beijing China Shandong University Qingdao China Beijing Film Academy Beijing China Indian Institute of Technology Kharagpur India Korea Republic of Nanyang Technological University Singapore Singapore

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top 8 finalists (out of over 150 teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenLORIS) - Object Recognition (OpenLORIS-object) is designed for driving lifelong/continual learning research and application in robotic vision domain, with everyday objects in home, office, campus, and mall scenarios. The dataset explicitly quantifies the variants of illumination, object occlusion, object size, camera-object distance/angles, and clutter information. Rules are designed to quantify the learning capability of the robotic vision system when faced with the objects appearing in the dynamic environments in the contest. Individual reports, dataset information, rules, and released source code can be found at the project homepage. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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AI for the public sector: Opportunities and challenges of cross-sector collaboration

arXiv

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arXiv 2018年

作者： Mikhaylov, Slava Jankin Esteve, Marc Campion, Averill Institute for Analytics and Data Science School of Computer Science Electronic Engineering Department of Government University of Essex School of Public Policy University College London Department of Strategy and General Management ESADE Ramon Llull University

Public sector organisations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and AI in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations. Copyright © 2018, The Authors. All rights reserved.

关键词： Artificial intelligence

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Corrigendum to "Generation of a local lung respiratory motion model using a weighted sparse algorithm and motion prior-based registration" [Comput. Biol. Med. 123 (2020) 103913]

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computers in biology and medicine 2021年 140卷 104271页

作者： Dong Chen Hongzhi Xie Lixu Gu Jing Liu Liang Tian College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Shijiazhuang China Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Department of Cardiothoracic Surgery Peking Union Medical College Hospital Beijing China. School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China. Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Electronic address: liujing01@***. Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Electronic address: tianliang@***.

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$\rho$ -Dominant Skyline Computation on data Stream

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IEEE Access 2018年 6卷 53201-53213页

作者： Zhiqiong Wang Junchang Xin Linlin Ding Jianmin Ba Xiaosong Gao Neusoft Institute of Intelligent Healthcare Technology Co. Ltd. Key Laboratory of Big Data Management and Analytics Shenyang China School of Information Liaoning University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang China

Skyline query is widely used in multi-standard decision making process. As the extension to skyline query, p-dominant skyline query controls the base of the result set by adjusting data proportion, which is suitable for the application on fast deciding data stream. However, skyline query on data stream cannot satisfy the need for skyline query of corresponding value ratio, and the traditional p-dominant skyline query cannot provide skyline query with frequent data update. As a result, a data stream-based algorithm that can achieve the goal of p-dominant skyline query is proposed. First, the definitions of full-dominant relationship and p-dominance are given, after which the definitions of p-dominant skyline and p-dominant skyline based on data stream are proposed. Second, by analyzing the characters of data stream-based p-dominant skyline, a new data filtering method is established. Based on the method, p-Dominant Skyline Query on data stream over Sliding Window (DSSW) is proposed in order to improve the efficiency of p-dominant skyline query. After that, we expend the p-dominant skyline query of data stream into n-of-N p-dominant skyline query and the (n1, n 2 )-of-Np-dominant skyline query in data stream. Finally, we prove that DSSW has obvious advantages in responding time and storage skyline space by comparing with the traditional p-dominant skyline query algorithms through abundant experiments.

关键词： IEEE merchandise Microsoft Windows Indexes Decision making Heuristic algorithms computer science

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data governance for platform ecosystems: Critical factors and the state of practice 21

Data governance for platform ecosystems: Critical factors an...

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21st Pacific Asia Conference on Information Systems: Societal Transformation Through IS/IT, PACIS 2017

作者： Lee, Sung Une Zhu, Liming Jeffery, Ross Architecture and Analytics Platforms Group Data61 CSIRO Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia

Recently, "platform ecosystem" has received attention as a key business concept. Sustainable growth of platform ecosystems is enabled by platform users supplying and/or demanding content from each other: e.g. Facebook, YouTube or Twitter. The importance and value of user data in platform ecosystems is accentuated since platform owners use and sell the data for their business. Serious concern is increasing about data misuse or abuse, privacy issues and revenue sharing between the different stakeholders. Traditional data governance focuses on generic goals and a universal approach to manage the data of an enterprise. It entails limited support for the complicated situation and relationship of a platform ecosystem where multiple participating parties contribute, use data and share profits. This article identifies data governance factors for platform ecosystems through literature review. The study then surveys the data governance state of practice of four platform ecosystems: Facebook, YouTube, EBay and Uber. Finally, 19 governance models in industry and academia are compared against our identified data governance factors for platform ecosystems to reveal the gaps and limitations. © PACIS 2017.

关键词： Social networking (online)

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GeoMF++: Scalable location recommendation via joint geographical modeling and matrix factorization

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ACM Transactions on Information Systems 2018年第3期36卷 33-33页

作者： Lian, Defu Zheng, Kai Ge, Yong Cao, Longbing Chen, Enhong Xie, Xing Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Management Information Systems University of Arizona TucsonAZ85721 United States Advanced Analytics Institute University of Technology Sydney NSW Australia School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Microsoft Research Beijing100080 China

Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location's spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data;Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collectivematrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space. © 2018 ACM.

关键词： Location

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Truth of varying shades: Analyzing language in fake news and political fact-checking

Truth of varying shades: Analyzing language in fake news and...

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Rashkin, Hannah Choi, Eunsol Jang, Jin Yea Volkova, Svitlana Choi, Yejin Paul G. Allen School of Computer Science and Engineering University of Washington United States Data Sciences and Analytics Pacific Northwest National Laboratory United States

ISBN: (纸本)9781945626838

We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on *** using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text. © 2017 Association for Computational Linguistics.

关键词： Fake detection

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