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arXiv 2019年

作者： Oh, Jeeheh Wang, Jiaxuan Tang, Shengpu Sjoding, Michael Wiens, Jenna Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Institute for Healthcare Policy & Innovation Department of Internal Medicine University of Michigan Ann ArborMI United States

Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part, to their use of parameter sharing over time (i.e., cells are repeated hence the name recurrent). We hypothesize, however, that this trait also contributes to the increased difficulty such models have with learning relationships that change over time. Conditional shift, i.e., changes in the relationship between the input X and the output y, arises if the risk factors for the event of interest change over the course of a patient admission. While in theory, RNNs and gated RNNs (e.g., LSTMs) in particular should be capable of learning timevarying relationships, when training data are limited, such models often fail to accurately capture these dynamics. We illustrate the advantages and disadvantages of complete weight sharing (RNNs) by comparing an LSTM with shared parameters to a sequential architecture with time-varying parameters on three clinically-relevant prediction tasks: Acute respiratory failure (ARF), shock, and in-hospital mortality. In experiments using synthetic data, we demonstrate how weight sharing in LSTMs leads to worse performance in the presence of conditional shift. To improve upon the dichotomy between complete weight sharing vs. no weight sharing, we propose a novel RNN formulation based on a mixture model in which we relax weight sharing over time. The proposed method outperforms standard LSTMs and other state-of-the-art baselines across all tasks. In settings with limited data, relaxed weight sharing can lead to improved patient risk strati Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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Contrastive Structured Anomaly Detection for Gaussian Graphical Models

Contrastive Structured Anomaly Detection for Gaussian Graphi...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Abhinav Maurya Mark Cheung Heinz College of Information Systems and Public Policy Carnegie Mellon University Department of Electrical and Computer Engineering Carnegie Mellon University

ISBN: (纸本)9781538660522

Gaussian graphical models (GGMs) are probabilistic tools of choice for analyzing conditional dependencies between variables in complex networked systems such as social networks, sensor networks, financial markets, etc. Finding changepoints in the structural evolution of a GGM is therefore essential to detecting anomalies in the underlying system modeled by the GGM. In order to detect structural anomalies in a GGM, we consider the problem of estimating changes in the precision matrix of the corresponding multivariate Gaussian distribution. We take a two-step approach to solving this problem:- (i) estimating a background precision matrix using system observations from the past without any anomalies, and (ii) estimating a foreground precision matrix using a sliding temporal window during anomaly monitoring. Our primary contribution is in estimating the foreground precision using a novel contrastive inverse covariance estimation procedure. In order to accurately learn only the structural changes to the GGM, we maximize a penalized log-likelihood where the penalty is the l 1 norm of difference between the foreground precision being estimated and the already learned background precision. We suitably modify the alternating direction method of multipliers (ADMM) algorithm for sparse inverse covariance estimation to perform contrastive estimation of the foreground precision matrix. Our results on simulated GGM data show significant improvement in precision and recall for detecting structural changes to the GGM, compared to a non-contrastive sliding window baseline.

关键词： Graphical models Image edge detection Covariance matrices Sparse matrices Anomaly detection Optimization Estimation

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Learning eating environments through scene clustering

arXiv

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arXiv 2019年

作者： Yarlagadda, S.K. Baireddy, Sriram Güera, David Boushey, Carol J. Kerr, Deborah A. Zhu, Fengqing School of Electrical and Computer Engineering Purdue University West LafayetteIN United States University of Hawaii Cancer Center HonoluluHI United States School of Public Health Curtin University PerthWA Australia

It is well known that dietary habits have a significant influence on health. While many studies have been conducted to understand this relationship, little is known about the relationship between eating environments and health. Yet researchers and health agencies around the world have recognized the eating environment as a promising context for improving diet and health. In this paper, we propose an image clustering method to automatically extract the eating environments from eating occasion images captured during a community dwelling dietary study. Specifically, we are interested in learning how many different environments an individual consumes food in. Our method clusters images by extracting features at both global and local scales using a deep neural network. The variation in the number of clusters and images captured by different individual makes this a very challenging problem. Experimental results show that our method performs significantly better compared to several existing clustering approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Health

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Findings from the Tushirikiane-4-MH (Supporting Each Other for Mental Health) Mobile Health-Supported Virtual Reality Randomized Controlled Trial Among Urban Refugee Youth in Kampala, Uganda

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Global Mental Health 2025年 12卷 e12-e12页

作者： Logie, Carmen H. Okumu, Moses Admassu, Zerihun MacKenzie, Frannie Gittings, Lesley Kortenaar, Jean-Luc Khan, Naimul Hakiza, Robert Musoke, Daniel Kibuuka Nakitende, Aidah Katisi, Brenda Kyambadde, Peter Lester, Richard Mbuagbaw, Lawrence Factor-Inwentash Faculty of Social Work University of Toronto Toronto ON Canada Women's College Research Institute Women's College Hospital Toronto ON Canada United Nations University Institute for Water Environment & Health Hamilton ON Canada Centre for Gender and Sexuality Health Equity Vancouver BC Canada School of Social Work University of Illinois Urbana-Champaign United States Uganda Christian University Mukono Uganda School of Health Studies Western University London ON Canada Centre for Social Science Research University of Cape Town South Africa Dalla Lana School of Public Health University of Toronto Toronto ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Young African Refugees for Integral Development (YARID) Kampala Uganda International Research Consortium Kampala Uganda National AIDS and STI Control Programme Ministry of Health Kampala Uganda Most At Risk Population Initiative Mulago Hospital Kampala Uganda Department of Medicine University of British Columbia Vancouver BC Canada Department of Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Anesthesia McMaster University Hamilton ON Canada

Background: Virtual reality (VR) for mental health promotion remains understudied in low income humanitarian settings. We examined the effectiveness of VR in reducing depression with urban refugee youth in Kampala, Uganda. Methods: This randomized controlled trial assessed VR alone (Arm 1), VR followed by group problem management plus (GPM+) (Arm 2), and a control group (Arm 3), with a peer-driven and convenience sample of refugee youth aged 16-25 in Kampala. The primary outcome, depression, was measured with the Patient Health Questionnaire-9. Secondary outcomes included: mental health literacy, mental health stigma, self-compassion, mental wellbeing, and adaptive coping. Analyses were conducted at three timepoints (baseline, 8-weeks, 16-weeks) using generalized estimating equations (GEE). Results: Among participants (n=335, mean age: 20.77, standard deviation: 3.01;cisgender women: n=158, cisgender men: n=173, transgender women: n=4) we found no depression reductions for Arms 1 or 2 at 16-weeks compared with Arm 3. At 16-weeks, mental health literacy was significantly higher for Arm 2 compared with Arm 3, and self compassion was significantly higher in Arms 1 and Arm 2 compared with Arm 3. Discussion: VR alongside GPM+ may benefit self-compassion and MHL among urban refugee youth in Kampala, but these interventions were not effective in reducing depression. © The Author(s), 2025. Published by Cambridge University Press.

关键词： depression refugees self-compassion Uganda virtual reality youth

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Management of extra-articular manifestations in spondyloarthritis

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International journal of rheumatic diseases 2023年第2期26卷 183-186页

作者： Kevin Sheng-Kai Ma Yung-Heng Lee Chia-Jen Lin Po-Cheng Shih James Cheng-Chung Wei Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts USA. Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan. Department of Dermatology Massachusetts General Hospital Boston USA. Department of Orthopedics Cishan Hospital Ministry of Health and Welfare Kaohsiung Taiwan. Institute of Medical Science and Technology National Sun Yat-sen University Kaohsiung Taiwan. Department of Senior Services Industry Management Minghsin University of Science and Technology Hsinchu Taiwan. Department of Recreation and Sport Management Shu-Te University Kaohsiung Taiwan. School of Medicine Chung Shan Medical University Taichung Taiwan. Division of Allergy Immunology & Rheumatology Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan. Institute of Medicine Chung Shan Medical University Taichung Taiwan. Division of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan. Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan.

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Robots, telework, and the jobs of the future

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Science 2019年第6422期363卷 38页

作者： Jon Peha The reviewer is at the Department of Engineering and Public Policy and the Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA 15213 USA.

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Design considerations for building credible security testbeds a systematic study of industrial control system use cases

arXiv

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arXiv 2019年

作者： Ani, Uchenna D. Watson, Jeremy M. Green, Benjamin Craggs, Barnaby Nurse, Jason Department of Science Technology Engineering and Public Policy University College London School of Computing and Communications Lancaster University United Kingdom Department of Computer Science University of Bristol United Kingdom School of Computing University of Kent United Kingdom

This paper presents a mapping framework for design factors and implementation process for building credible Industrial Control Systems (ICS) security testbeds. The resilience of ICSs has become a critical concern to operators and governments following widely publicised cyber security events. The inability to apply conventional Information Technology security practice to ICSs further compounds challenges in adequately securing critical systems. To overcome these challenges, and do so without impacting live environments, testbeds for the exploration, development and evaluation of security controls are widely used. However, how a testbed is designed and its attributes, can directly impact not only its viability but also its credibility as a whole. Through a combined systematic and thematic analysis and mapping of ICS security testbed design attributes, this paper suggests that the expertise of human experimenters, design objectives, the implementation approach, architectural coverage, core characteristics, and evaluation methods;are considerations that can help establish or enhance confidence, trustworthiness and acceptance;thus, credibility of ICS security testbeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Testbeds

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Game theory of pandemic control: Can collaborative vaccine allocation strategies lead to better outcomes?

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IJID Regions 2025年 14卷 100535-100535页

作者： Yiwen Wang Yang Ye Xiaoyi Su Qingpeng Zhang Hsiang-Yu Yuan Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Center for Infectious Disease Modeling and Analysis Yale School of Public Health New Haven CT 06520 Department of Biostatistics City University of Hong Kong Hong Kong China HKU Musketeers Foundation Institute of Data Science and Department of Pharmacology and Pharmacy The University of Hong Kong Hong Kong China Department of Biomedical Sciences Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong China Centre for Applied One Health Research and Policy Advice Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong China

Introduction Increasing vaccine uptake is an effective way to prevent COVID-19 infection in individual countries after the relaxation of social distancing. However, under the limited vaccine resources, vaccine inequality between high- and low-income countries can also produce more infection and cost. Reducing such inequality relies on a more cooperative (unselfish) strategy, such as vaccine donation from high-income countries, which may initially increase their cost. The study aims to analyze the impact of vaccine inequality on cooperative decision-making between two populations. The results provide important insights on achieving global control of infectious disease outbreaks. Material and Methods We developed a transmission model incorporating virus evolution under different vaccine-induced natural selection and the migration to allow a long-term repeated outbreak. Vaccine allocation strategies were implemented between two populations the high vaccine coverage population (HC) and the low coverage population (LC). A game theory approach was employed to explore whether pursuing minimum cost for both sides (cooperative strategy) or itself (non-cooperative strategy) for each population. We estimated the effect of each strategy on the total cost (i.e. the sum of vaccine, hospitalization and labor costs). The Nash equilibrium was obtained by minimizing their own cost for individual countries under different vaccine inequality scenarios (from low to high inequality). Results Under low inequality, both populations achieved the same optimal cost with both cooperative and non-cooperative strategies. Under medium or high inequality, both populations reached a Nash equilibrium with non-cooperative strategies and could not reach global optimum. When inequality was high, HC tended towards vaccine donation even though non-cooperative strategies were adopted. Discussion When inequality is high, although cooperative decisions can obtain global optimal outcomes, populations tend to

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Nigrostriatal dopaminergic neurotransmission and resilience to peripheral systemic risk factors for gait slowing upon transition to uneven surfaces in older adult

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Aging Brain 2025年 8卷

作者： Lana M. Chahine Andrea Rosso Ian Troidl Mary Ganguli Anne Newman Steven Cummings Stephanie Studenski Brian Lopresti Sarah Royse Theodore Huppert Mark Redfern Patrick J. Sparto Nico I. Bohnen Caterina Rosano Department of Neurology School of Medicine University of Pittsburgh Pittsburgh PA USA Department of Epidemiology Graduate School of Public Health University of Pittsburgh Pittsburgh PA USA Departments of Psychiatry School of Medicine University of Pittsburgh Pittsburgh PA USA San Francisco Coordinating Center California Pacific Medical Center Research Institute San Francisco CA USA Department of Epidemiology and Biostatistics University of California San Francisco CA USA Division of Geriatric Medicine University of Pittsburgh School of Medicine PA USA Department of Radiology University of Pittsburgh USA Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh USA Department of Bioengineering Swanson School of Engineering University of Pittsburgh USA Department of Physical Therapy School of Health and Rehabilitation Sciences University of Pittsburgh USA Department of Radiology University of Michigan Ann Arbor MI USA Departments of Neurology & Radiology School of Medicine University of Michigan Ann Arbor MI USA

Identifying mechanisms that compensate for slow gait speed in older adults is crucial. Dopaminergic neurotransmission curbs deleterious associations of cerebrovascular disease with gait, but whether it compensates for peripheral systemic risk factors (PSRF) for gait slowing has not been studied. In this cross-sectional study of community-dwelling older adults, we examined the relationship between nigrostriatal dopaminergic terminal integrity and gait speed in individuals with and without ≥ 1 PSRF for gait slowing: obesity, joint pain, or reduced muscle strength. The primary outcome was gait speed cost (%GSC) on transition from even to uneven surface. Participants underwent dopaminergic imaging with dihydrotetrabenazine [ 11 C]DTBZ positron emission tomography. Among 197 individuals, (mean (SD) age 74.92 (4.53) years; 61.93 % female; 90.86 % White), 130 (65.99 %) had ≥ 1 PSRF. Relationship between posterior putamen [ 11 C]DTBZ binding and %GSC was modified by PSRF; in those with ≥ 1 PSRF (but not in those with no PSRF), posterior putamen [ 11 C]DTBZ binding was associated with %GSC (β = 0.198, p = 0.03) independent of potential confounders. This cross-sectional study indicates that higher striatal dopaminergic neurotransmission may compensate for the effects of PSRF on gait slowing.

关键词： Dopamine Gait slowing Resilience MYHAT Monongahela-Youghiogheny Healthy Aging Team study PET Positron Emission Tomography PSRF Peripheral Systemic Risk Factors SOMMA Study of Muscle, Mobility and Aging VMAT2 type-2 vesicular monoamine transporter

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