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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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Ethical machine learning in health care

arXiv

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arXiv 2020年

作者： Chen, Irene Y. Pierson, Emma Rose, Sherri Joshi, Shalmali Ferryman, Kadija Ghassemi, Marzyeh Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Microsoft Research CambridgeMA02143 United States Center for Health Policy Center for Primary Care and Outcomes Research Stanford University StanfordCA94305 United States Vector Institute TorontoON Canada Department of Technology Culture and Society Tandon School of Engineering New York University BrooklynNY11201 United States Department of Computer Science University of Toronto TorontoON Canada

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges. © 2020, CC BY.

关键词： Health care

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Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection

Meta-learning techniques to analyze the raman data for optic...

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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Sharma, Mukta Sharma, Lokesh Jeng, Ming-Jer Chang, Liann-Be Huang, Shiang-Fu Wu, Shih-Lin Department of Electronic Engineering Chang Gung University Taoyuan333 Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou244 Taiwan Department of Public Health Chang Gung University Taoyuan 333 Taiwan AI Innovation Research Center Chang Gung University Taoyuan333 Taiwan Department of Cardiology Chang Gung Memorial Hospital Taoyuan333 Taiwan Department of Electrical Engineering Ming Chi University of Technology New Taipei City24301 Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan333 Taiwan

ISBN: (纸本)9781728152097

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy. © 2019 IEEE.

关键词： Adaptive boosting

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Institutional Origins of Protective COVID-19 public Health policy Responses: Informational and Authority Redundancies and policy Stringency

Journal of Political Institutions and Political Economy

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Journal of Political Institutions and Political Economy 2020年第4期1卷 585-613页

作者： Shvetsova, Olga Zhirnov, Andrei VanDusky-Allen, Julie Adeel, Abdul Basit Catalano, Michael Catalano, Olivia Giannelli, Frank Muftuoglu, Ezgi Riggs, Tara Sezgin, Mehmet Halit Tahir, Naveed Zhao, Tianyi Department of Political Science Binghamton University P.O. Box 6000 Binghamton 13902 NY United States Q-Step Centre Department of Politics University of Exeter Clayden Streatham Rise Exeter EX4 4PE United Kingdom School of Public Service Boise State University 1910 University Dr. Boise 83725 ID United States 3215 Stack Avenue Endicott 13760 NY United States School of Health Professions Rutgers The State University of New Jersey 675 Hoes Lane West 6th Floor Piscataway 08854 NJ United States Department of Electrical Engineering and Computer Science Syracuse University 111 College Place Syracuse 13210 NY United States

In this essay, we argue that institutional systems that allow redundancies in information channels and in policy-making are more likely to generate a rapid policy response to crises such as the onset of COVID-19 pandemic than more streamlined systems. Since democracies and decentralized polities feature higher informational and authority redundancies, we theorize improved crisis response in democracies, and in more decentralized democracies. To assess our theoretical expectations, we construct an original data set of stringency of policy measures that were adopted in response to COVID-19 by governments at different levels in 64 countries between January and May 2020. We find that democracies and liberal democracies responded to COVID-19 stronger and faster. Federalism and decentralization in addition to democratic institutions played a less uniform, but still a positive role. Beyond their other acknowledged merits, democratic institutions have superior capacity to mount a quick policy response to unqualified threats. © 2020 O. Shvetsova, et al.

关键词： COVID-19 democracy federalism public health policy system redundancies

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On Accelerated Testing for COVID-19 Using Group Testing

arXiv

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arXiv 2020年

作者： Narayanan, Krishna R. Heidarzadeh, Anoosheh Laxminarayan, Ramanan Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States Center for Disease Dynamics Economics & Policy Silver SpringMD20910 United States Department of Global Health University of Washington SeattleWA98104 United States Princeton Environment Institute Princeton University PrincetonNJ08544 United States

COVID-19 has resulted in a global health crisis that may become even more acute over the upcoming months. One of the main reasons behind the current rapid growth of COVID-19 in the U.S. population is the limited availability of testing kits and the relatively-high cost of screening tests. In this draft, we demonstrate the effectiveness of group testing (pooling) ideas to accelerate testing for COVID-19. This draft is semi-tutorial in nature and is written for a broad audience with interest in mathematical formulations relevant to COVID-19 testing. Therefore, ideas are presented through illustrative examples rather than through purely theoretical formulations. The focus is also on pools of size less than 64 such as what is practical with current RT-PCR technology. Copyright © 2020, The Authors. All rights reserved.

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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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PIPELINE SEGMENTATION USING LEVEL-SET METHOD

PIPELINE SEGMENTATION USING LEVEL-SET METHOD

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IEEE International Geoscience and Remote Sensing Symposium

作者： A. Leangaramkul T. Kasetkasem Y. Tipsuwan T. Isshiki T. Chanwimaluang P. Hoonsuwan Department of Electrical Engineering Kasetsart University Bangkok Thailand Department of Computer Engineering Kasetsart University Bangkok Thailand Department of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan National Electronics and Computer Technology Center (NECTEC) Pathumthani Thailand PTT Exploration and Production Public Company Limited (PTTEP) Bangkok Thailand

Autonomous underwater vehicles (AUVs), unmanned underwater vehicles, are cheaper to operate than remotely operated vehicles (ROVs), since an AUV does not require a trained human operator to control. As a result, in this paper, we propose the pipeline tracking algorithm using sequences of the forward-looking sonar (FLS) images installed on a preprogram AUV. The FLS is more suitable to be used in the subsurface environment than an optical camera due to the limited visibility and floating particles in the sub-sea surface environment. In our work we first employed the speckle noise reduction to the FLS images. Then, we applied the level-set method (LSM) to extract a pipeline boundary in each frame. Lastly, the post-processing step was designed to link the unconnected segments together to from a complete pipeline. Our algorithm can perform the segmentation and tracking with more than 85% accuracy.

关键词： Pipelines Image segmentation Sonar Shape Skeleton Speckle Image resolution

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Pipeline Direction Extraction Algorithm Using Level Set Method

Pipeline Direction Extraction Algorithm Using Level Set Meth...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

Autonomous underwater vehicles (AUVs), unmanned underwater vehicles, provide lower operation costs in underwater pipeline inspection than remotely operated vehicles (ROVs), since there is no need for a human operator. However, the navigation algorithm is an important part of the successful deployment of AUVs. In this work, we propose an algorithm that extracts the direction of a pipeline based on the level set method (LSM). This direction shall be used for the automatic navigation of an AUV. The low light and turbid water prohibit the use of an optical camera. Instead, a forward-looking sonar (FLS) was selected to provide input sequence images. Due to speckle noise, the traditional image segmentation algorithm cannot be used. Thus, the LSM is applied to represent the direction of pipelines. Lastly, the post-processing which estimates the direction is used to increase the accuracy. From experiments, our algorithm can extract the pipeline direction with accuracies of as high as 94.33%.

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An Elevation Mapping Algorithm Based on a Markov Random Field Model for Underwater Exploration

An Elevation Mapping Algorithm Based on a Markov Random Fiel...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： N. Prasatthong T. Kasetkasem Y. Tipsuwan T. Isshiki T. Chanwimaluang P. Hoonsuwan Department of Electrical Engineering Kasetsart University Bangkok Thailand Department of Computer Engineering Kasetsart University Bangkok Thailand Department of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan National Electronics and Computer Technology Center (NECTEC) Pathumthani Thailand PTT Exploration and Production Public Company Limited (PTTEP) Bangkok Thailand

The 2-D Forward-looking Sonar (2D-FLS) is a new technology of sonar signals that increases the performance of underwater exploration of an Autonomous Underwater Vehicle (AUV). Because the AUV is a fully automatic robot that can handle various missions and does not require humans to control its operation, FLS is the best tool for underwater navigation in poor visibility conditions. In this paper, we used an FLS image sequence to estimate the height of the pipeline and create an elevation map containing information about the height in each of the pixels. In our algorithm, we employed the Markov random field to model the elevation map, since elevations of neighboring pixels are more likely to have similar values. Next, the Metropolis Algorithm (MA) is used to find the solution of the maximum a posterori equation.

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An AI-based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Samad Amini Boran Hao Lifu Zhang Mengting Song Aman Gupta Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo

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