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Insights from statistical analysis of opioid data

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arXiv 2018年

作者： Basu, Kaustav Choudhuri, Sandipan Sen, Arunabha Majumdar, Aniket NetXT Lab School of Computing Informatics and Decision Systems Engineering Arizona State University Galvanize Inc.

Opioid overdose has emerged as a full blown epidemic in the United States. In the last few years, there has been an alarming increase in Opioid related deaths, resulting in the loss of 63,600 lives in 2016 alone. The epidemic which is killing more than 100 people each day, was declared as a public health emergency by the US government, in October 2017. Although a few health related companies and commercial firms have examined this important issue from various available data sources, to the best of our knowledge, the academic community has not been engaged in research in this important topic. It can be safely noted that the study of the epidemic, from the data analytics perspective, is in its infancy. Given that a significant amount of Opioid related data is available in public domain, it provides the academic community an opportunity to analyze such data to provide recommendations to the public health authorities to mitigate the impact of the epidemic. In that vein, we collected some publicly available data to analyze the important contributing factors of the epidemic. In particular, we examine the role of the individuals prescribing Opioid drugs on the spread of the epidemic. In addition, we examine the impact of income level, age and educational level of various neighborhoods in a large US city, on Opioid related incidences, to find any correlation between them. Copyright © 2018, The Authors. All rights reserved.

关键词： Epidemiology

User satisfaction-driven bandwidth allocation for image transmission in a crowded environment

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arXiv 2018年

作者： Choudhuri, Sandipan Basu, Kaustav Sen, Arunabha School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Epidemic plateau in critical SIR dynamics with non-trivial initial conditions

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arXiv 2020年

作者： Radicchi, Filippo Bianconi, Ginestra Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Alan Turing Institute 96 Euston Rd LondonNW1 2DB United Kingdom School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom

Containment measures implemented by some countries to suppress the spread of COVID-19 have resulted in a slowdown of the epidemic characterized by time series of daily infections plateauing over extended periods of time. We prove that such a dynamical pattern is compatible with critical Susceptible-Infected-Removed (SIR) dynamics. In traditional analyses of the critical SIR model, the critical dynamical regime is started from a single infected node. The application of containment measures to an ongoing epidemic, however, has the effect to make the system enter in its critical regime with a number of infected individuals potentially large. We describe how such non-trivial starting conditions affect the critical behavior of the SIR model. We perform a theoretical and large-scale numerical investigation of the model. We show that the expected outbreak size is an increasing function of the initial number of infected individuals, while the expected duration of the outbreak is a non-monotonic function of the initial number of infected individuals. Also, we precisely characterize the magnitude of the fluctuations associated with the size and duration of the outbreak in critical SIR dynamics with non-trivial initial conditions. Far from heard immunity, fluctuations are much larger than average values, thus indicating that predictions of plateauing time series may be particularly challenging. Copyright © 2020, The Authors. All rights reserved.

关键词： Epidemiology

Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

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Computers in Biology and Medicine 2025年 194卷

作者： Hager Saleh Shaker El-Sappagh Michael McCann Saeed Hamood Alsamhi John G. Breslin Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road Galway H91 TK33 Ireland Atlantic Technological University Letterkenny Donegal Ireland Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha 13518 Egypt Department of Computing Atlantic Technological University Letterkenny Donegal Ireland Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea Department of Electronics Engineering IBB University Ibb Ibb Yemen

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been designed to perform multivariate, multi-horizon time-series forecasting for vital signs, specifically for a person’s blood oxygen saturation level (SpO2) and respiratory rate (RR). The framework leverages advanced attention-based models, with a particular emphasis on the Temporal Fusion Transformer (TFT) and Temporal Convolutional Network (TCN), and we benchmark its performance against classical deep learning architectures, including LSTM, GRU, Bi-LSTM, Bi-GRU, CNN, and Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. Both univariate and multivariate forecasting tasks are explored across multiple prediction horizons (i.e., 7, 15 and 25 min), using physiological time-series data from the MIMIC-III database. The proposed system incorporates a cascaded fine-tuning strategy, wherein the TFT model is sequentially fine-tuned on individual patients’ data, significantly enhancing the model’s generalizability to unseen patient profiles. Empirical results demonstrate that the TFT model consistently outperforms baseline models across all forecasting settings, achieving lower RMSE and MAE values, and exhibiting superior capacity for capturing long-sequence dependencies and temporal feature dynamics. To validate its applicability in real-time clinical environments, the framework integrates a simulated streaming infrastructure using Apache Kafka and Apache Flink, enabling continuous data ingestion, forecasting, and visualization of vital signs. This end-to-end deployment underscores the system’s potential for ICU monitoring, allowing clinicians to anticipate patient deterioration proactively. In summary, we introduce a comprehensive framework that uniquely integrates TFT with cascaded fine-tuning for multivar

关键词： Real-time patient monitoring Time-series data forecasting Intensive care unit Sequence-to-sequence modeling Multi-horizon forecasting Attention-based modeling Deep learning

A unified framework for planning in adversarial and cooperative environments

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arXiv 2018年

作者： Kulkarni, Anagha Srivastava, Siddharth Kambhampati, Subbarao School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy, as well as legible plans which are easy for team members to understand in cooperative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. For adversarial settings, our approach produces obfuscated plans with observations that are consistent with at least k goals from a set of decoy goals. By slightly varying our framework, we present an approach for goal legibility in cooperative settings which produces plans that achieve a goal while being consistent with at most j goals from a set of confounding goals. In addition, we show how the observability of the observer can be controlled to either obfuscate or clarify the next actions in a plan when the goal is known to the *** present theoretical results on the complexity analysis of our *** demonstrate the execution of obfuscated and legible plans in a cooking domain using a physical robot Fetch. We also provide an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains. Copyright © 2018, The Authors. All rights reserved.

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Building a pubmed knowledge graph

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arXiv 2020年

作者： Xu, Jian Kim, Sunkyu Song, Min Jeong, Minbyul Kim, Donghyeon Kang, Jaewoo Rousseau, Justin F. Li, Xin Xu, Weijia Torvik, Vetle I. Bu, Yi Chen, Chongyan Ebeid, Islam Akef Li, Daifeng Ding, Ying School of Information Management Sun Yat-sen University Guangzhou China Department of Computer Science and Engineering Korea University Seoul Korea Republic of Department of Library and Information Science Yonsei University Seoul Korea Republic of Dell Medical School University of Texas AustinTX United States School of Information University of Texas AustinTX United States Texas Advanced Computing Center AustinTX United States School of Information Sciences University of Illinois ChampaignIL United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

PubMed®is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguated, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of the credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving a F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities. The PKG is freely available on Figshare (http://***/s/6327a55355fc2c99f3a2, simplified version that exclude PubMed raw data) and TACC website (http://***/datasets/ped, full version). Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

The latent structure of global scientific development

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arXiv 2021年

作者： Miao, Lili Murray, Dakota Jung, Woo-Sung Larivière, Vincent Sugimoto, Cassidy R. Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of École de Bibliothéconomie et des Sciences de l'Information Université de Montréal MontréalQCH2L 2C4 Canada Observatoire des Sciences et des Technologies Université du Québec à Montréal MontréalQCH2L 2C4 Canada Department of Science and Innovation National Research Foundation Centre of Excellence in Scientometrics and Science Technology and Innovation Policy Stellenbosch University Stellenbosch7600 South Africa Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States IN47408 United States Connection Science Massachusetts Institute of Technology CambridgeMA02139 United States

Science is essential to innovation and economic prosperity. Although studies have shown that national scientific development is affected by geographic, historic, and economic factors, it remains unclear whether there are universal structures and trajectories of national scientific development that can inform forecasting and policymaking. Here, by examining countries' scientific 'exports'-publications that are indexed in international databases-we reveal a three-cluster structure in the relatedness network of disciplines that underpin national scientific development and the organization of global science. Tracing the evolution of national research portfolios reveals that while nations are proceeding to more diverse research profiles individually, scientific production is increasingly specialized in global science over the past decades. By uncovering the underlying structure of scientific development and connecting it with economic development, our results may offer a new perspective on the evolution of global science. © 2021, CC BY.

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sbi reloaded: a toolkit for simulation-based inference workflows

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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

Improving student learning performance in a virtual hands-on lab system in cybersecurity education

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Improving student learning performance in a virtual hands-on...

Frontiers in Education (FIE) Conference

作者： Zhen Zeng Yuli Deng IHan Hsiao Dijiang Huang Chun-Jen Chung School of Computing Informatics and Decision Systems Arizona State University Tempe Arizona USA

ISBN: (纸本)9781538611753;9781538611746

This Research Work in Progress paper presents a study on improving student learning performance in a virtual hands-on lab system in cybersecurity education. As the demand for cybersecurity-trained professionals rapidly increasing, virtual hands-on lab systems have been introduced into cybersecurity education as a tool to enhance students' learning. To improve learning in a virtual hands-on lab system, instructors need to understand: what learning activities are associated with students' learning performance in this system? What relationship exists between different learning activities? What instructors can do to improve learning outcomes in this system? However, few of these questions has been studied for using virtual hands-on lab in cybersecurity education. In this research, we present our recent findings by identifying that two learning activities are positively associated with students' learning performance. Notably, the learning activity of reading lab materials (p

关键词： Computer security Task analysis Education Servers Logic gates informatics