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Designing Survey-Based Mobile Interfaces for Rural Patients With Cancer Using Apple's ResearchKit and CareKit: Usability Study

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JMIR Formative Research 2024年 8卷 e57801页

作者： Donawa, Alyssa Powell, Christian Wang, Rong Chih, Ming-Yuan Patel, Reema Zinner, Ralph Aronoff-Spencer, Eliah Baker, Corey E. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Computer Science University of Kentucky Lexington KY United States Department of Human and Organizational Development Vanderbilt University Nashville TN United States Department of Health and Clinical Sciences University of Kentucky Lexington KY United States Division of Medical Oncology Markey Cancer Center University of Kentucky Lexington KY United States Division of Infectious Diseases & Global Public Health School of Medicine University of California San Diego San Diego CA United States

Background: Despite the increased accessibility and availability of technology in recent years, equality and access to health-related technology remain limited to some demographics. In particular, patients who are older or from rural communities represent a large segment of people who are currently underusing mobile health (mHealth) solutions. System usability continues to hinder mHealth adoption among users with nontraditional digital literacy. Objective: This study aims to investigate if state-of-the-art mobile app interfaces from open-source libraries provide sufficient usability for rural patients with cancer, with minimal design changes and forgoing the co-design process. Methods: We developed Assuage (Network Reconnaissance Lab) as a research platform for any mHealth study. We conducted a pilot study using Assuage to assess the usability of 4 mobile user interfaces (UIs) based on open-source libraries from Apple's ResearchKit and CareKit. These UIs varied in complexity for reporting distress symptoms. Patients with cancer were recruited at the Markey Cancer center, and all research procedures were conducted in person. Participants completed the distress assessment using a randomly selected UI in Assuage with little to no assistance. Data were collected on participant age, location, mobile app use, and familiarity with mHealth apps. Participants rated usability with the System Usability Scale (SUS), and usability issues were documented and compared. A one-way ANOVA was used to compare the effect of the UIs on the SUS scores. Results: We recruited 30 current or postsurgery patients with cancer for this pilot study. Most participants were aged >50 years (24/30, 80%), from rural areas (25/30, 83%), had up to a high school education (19/30, 63%), and were unfamiliar with mHealth apps (21/30, 70%). General mobile app use was split, with 43% (14/30) of the patients not regularly using mobile apps. The mean SUS score across the UIs was 75.8 (SD 22.2), with UI 3 and UI

关键词： apps CareKit digital divide digital literacy distress ehealth adoption ehealth literacy HealthKit mHealth mobile health patients with cancer rural health usability usability testing

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Heterogeneous Oblique Double Random Forest

arXiv

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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges

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Informatics in Medicine Unlocked 2024年 51卷

作者： Mienye, Ibomoiye Domor Obaido, George Jere, Nobert Mienye, Ebikella Aruleba, Kehinde Emmanuel, Ikiomoye Douglas Ogbuokiri, Blessing Institute for Intelligent Systems University of Johannesburg Auckland Park Johannesburg 2006 South Africa Center for Human-Compatible Artificial Intelligence (CHAI) Berkeley Institute for Data Science (BIDS) University of California Berkeley 94720 CA United States Department of Computer Science University of Fort Hare Alice Campus Eastern Cape Alice South Africa School of Computing and Mathematical Sciences University of Leicester Leicester LE1 7RH United Kingdom School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Computer Science Brock University Niagara Region St. Catharines L2S 3A1 ON Canada

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, reliable, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including the need to balance interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends, offering valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, fostering the development of transparent, trustworthy, and effective AI-driven solutions. © 2024 The Authors

关键词： AI Bias Ethics Fairness Healthcare Machine learning

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A New Physical Reservoir Using the Complex Dynamics of Electric Fields in Type-Ii Superconductors Containing Pinning centers Interacting with Quantized Flux Lines

SSRN

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SSRN 2024年

作者： Ueda, Tenma Otabe, Edmund Soji Arita, Ken Usami, Yuki Tanaka, Hirofumi Matsuno, Tetsuya Department of Physics and Computer Science Kyushu Institute of Technology 680–4 Kawazu Iizuka Fukuoka820–8502 Japan Department of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology 2–4 Hibikino Wakamatsu Kitakyushu808–019 Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology 2–4 Hibikino Wakamatsu Kitakyushu808–019 Japan Department of Creative Engineering National Institute of Technology Ariake College 150 Higashihagio-Machi Omuta Fukuoka836-8585 Japan

Reservoir computing, which takes advantage of physical phenomena with nonlinearities, is attracting a lot of ***, it is expected to focus on superconductivity, a physical phenomenon with nonlinearities between the output electric field and the input current *** to other physical phenomena used in reservoirs, it is considered that with superconductivity, the nonlinearity can easily be adjusted spatially by pin placement to produce dynamics suitable for *** nonlinearity between the electric field generated by the motion of the quantized magnetic flux lines and the input current density was used to perform a reservoir computing *** waveform generation tasks, a NARMA2 task and a nonlinear-memory task were performed, and all tasks were generally *** was found that the electromagnetic phenomenon in superconductors can be used as a physical reservoir. © 2024, The Authors. All rights reserved.

关键词： Electric lines

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Correction: Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

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International journal of obesity (2005) 2025年第2期49卷 364-365页

作者： Arthur Leroy Varsha Gupta Mya Thway Tint Delicia Shu Qin Ooi Fabian Yap Ngee Lek Keith M Godfrey Yap Seng Chong Yung Seng Lee Johan G Eriksson Mauricio A Álvarez Navin Michael Dennis Wang Department of Computer Science The University of Manchester Manchester UK. Institute for Human Development and Potential Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. Bioinformatics Institute Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. Department of Paediatrics Yong Loo Lin School of Medicine National University of Singapore Singapore Republic of Singapore. Department of Paediatrics KK Women's and Children's Hospital Singapore Republic of Singapore. Duke-NUS Medical School Singapore Republic of Singapore. MRC Lifecourse Epidemiology Centre and NIHR Southampton Biomedical Research Centre University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK. Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Republic of Singapore. Division of Paediatric Endocrinology Department of Paediatrics Khoo Teck Puat-National University Children's Medical Institute National University Hospital National University Health System Singapore Republic of Singapore. Department of General Practice and Primary Health Care University of Helsinki Helsinki Finland. Folkhälsan Research Center Helsinki Finland. Department of Computer Science University of Sheffield Sheffield UK. Institute for Human Development and Potential Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. dennis_wang@sics.a-star.edu.sg. Bioinformatics Institute Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. dennis_wang@sics.a-star.edu.sg. Department of Computer Science University of Sheffield Sheffield UK. dennis_wang@sics.a-star.edu.sg. National Heart and Lung Institute Imperial College London London UK. dennis_wang@sics.a-star.edu.sg.

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Moving towards informative and actionable social media research

arXiv

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arXiv 2025年

作者： Bak-Coleman, Joseph B. Lewandowsky, Stephan Lorenz-Spreen, Philipp Narayanan, Arvind Orben, Amy Oswald, Lisa Centre for the Advanced Study of Collective Behavior University of Konstanz Konstanz Germany Department of Collective Behavior Max Planck Institute of Animal Behavior Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Santa Fe Institute Santa FeNM United States Berkman Klein Center Harvard University CambridgeMA United States School of Psychological Science University of Bristol Bristol United Kingdom Department of Psychology University of Potsdam Potsdam Germany Center Synergy of Systems Center for Scalable Data Analytics and Artificial Intelligence Dresden University of Technology Dresden Germany Center for Adaptive Rationality Max Planck Institute for Human Development Berlin Germany Center for Information Technology Policy Princeton University PrincetonNJ United States Department of Computer Science Princeton University PrincetonNJ United States MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge United Kingdom

Social media is nearly ubiquitous in modern life, and concerns have been raised about its putative societal impacts, ranging from undermining mental health and exacerbating polarization to fomenting violence and disrupting democracy. Despite extensive research, consensus on these effects remains elusive, with observational studies often highlighting concerns while randomized controlled trials (RCTs) yield conflicting or null findings. This review examines how the complexity inherent in social systems can account for such discrepancies, emphasizing that emergent societal and long-term outcomes cannot be readily inferred from individual-level effects. In complex systems, such as social networks, feedback loops, hysteresis, multi-scale dynamics, and non-linearity limit the utility of approaches for assessing causality that are otherwise robust in simpler contexts. Revisiting large-scale experiments, we explore how null or conflicting findings may reflect these complexities rather than a true absence of effects. Even in cases where the methods are appropriate, assessing the net impacts of social media provides little actionable insight given that eliminating social media is not a realistic option for whole populations. We argue that progress will require a complexity-minded approach focused on specific design choices of online platforms that triangulates experimental, observational and theoretical methods. © 2025, CC BY.

关键词： Tweets

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Investigating the interrelationships between the barriers of green computing adoption: TISM-MICMAC-DEMATEL method

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Quality and Quantity 2024年第1期59卷 507-570页

作者： Dalvi-Esfahani, Mohammad Nilashi, Mehrbakhsh Abumalloh, Rabab Ali Thurasamy, Ramayah Ahmad, Muhammad Shakil Blockchain Fintech and Cryptocurrency Research Group University of Southampton Malaysia Johor Iskandar Puteri 79100 Malaysia UCSI Graduate Business School UCSI University No. 1 Jalan Menara Gading UCSI Heights Cheras Kuala Lumpur 56000 Malaysia Department of Computer Science and Engineering Qatar University Doha 2713 Qatar School of Management Universiti Sains Malaysia Minden Penang 11800 Malaysia Department of Information Technology and Management Daffodil International University Birulia Bangladesh Department of Management Sunway Business School (SBS) Selangor Petaling Jaya 47500 Malaysia University Center for Research & amp Development (UCRD) Chandigarh University Punjab Ludhiana 140413 India Faculty of Economics and Business Universitas Indonesia (UI) West Java Depok City 16424 Indonesia Management Information Systems Department School of Business The University of Jordan (UJ) Amman 11942 Jordan TUIBS Leadership Management & amp Human Resources Teesside University Middlesbrough United Kingdom Faculty of Business Sohar University Sohar P.C 311 Oman

While a considerable body of research addresses firms’ endeavors toward environmental sustainability, the prominence of green computing has been somewhat eclipsed by impediments hindering its swift integration and development. To address this gap, this study conducted a comprehensive analysis, identifying twelve distinct barriers categorized into adoption costs, technological challenges, and internal obstacles. Through consultation with fifteen field experts, the study employed Interpretive Structural Modeling (ISM) and Matrice d’Impacts Croises Multipication Applique´ an Classment (MICMAC) methodologies to scrutinize the influences among these barriers. The findings illuminated that firm size and legal constraints emerge as pivotal and autonomous factors exerting substantial influence on the adoption. Additionally, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was deployed to ascertain the relative importance of these barriers and delineate their causal relationships. Notably, the DEMATEL results underscored that, according to experts, adoption cost barriers hold greater significance compared to other dimensions. Transactional risk, technological complexity, and switching cost were identified as pivotal barriers within their respective dimensions. The decision-making framework formulated in this study offers valuable insights for managers and practitioners, providing a nuanced understanding of the interplay between identified barriers and their implications for implementing green computing initiatives. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Barriers DEMATEL Green computing MICMAC Total interpretive structural modeling

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A virtual reality methodology for cardiopulmonary resuscitation training with and without a physical mannequin

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Journal of Biomedical Informatics 2020年 111卷 103590-103590页

作者： Buttussi, Fabio Chittaro, Luca Valent, Francesca Human-Computer Interaction Lab Department of Mathematics Computer Science and Physics University of Udine Udine Italy SOC Istituto di Igiene ed Epidemiologia Clinica Azienda Sanitaria Universitaria Friuli Centrale Udine Italy

Background: Cardiopulmonary resuscitation (CPR) is an emergency procedure that can increase survival after a cardiac arrest. Performing CPR effectively requires both procedural knowledge and manual skills. Traditional CPR training methodology includes lessons led by instructors and supervised practice on mannequins, thus requiring considerable resources. Objective: This paper proposes a new methodology for low-cost CPR training based on virtual reality (VR) with and without the addition of a physical mannequin. Moreover, it describes an experimental evaluation of the methodology that assessed gain in manual skills during training, transfer of procedural knowledge and manual skills in a final assessment, and changes in self-efficacy with three measurements over time (pre-training, post-training, and post-assessment). Methods: We implemented a VR application that supports the proposed methodology, and can thus be used with or without a mannequin. The experimental evaluation involved 30 participants who tried CPR in VR twice, performing two repetitions of 30 chest compressions per trial. Half participants tried the VR application with the mannequin and half without it. Final assessment required all participants to perform CPR on the mannequin without the assistance of VR. To assess self-efficacy, participants filled in a questionnaire at the three times of measurement. Results: Mixed-design ANOVAs showed effects of repetition, effects of group, or interaction between the two variables on manual skills assessed during training. In the final assessment, participants in both groups correctly remembered most of the steps of the procedure. ANOVAs revealed differences between the two groups only in pressure-related skills (better with mannequin) and in the number of wrong steps added to the procedure (better without mannequin). Mixed-design ANOVA showed a self-efficacy increase in both groups after training, which was maintained after final assessment. Conclusions: The propo

关键词： Resuscitation

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Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound

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npj Digital Medicine 2025年第1期8卷 1-9页

作者： Chao, Chieh-Ju Gu, Yunqi Richard Kumar, Wasan Xiang, Tiange Appari, Lalith Wu, Justin Farina, Juan M. Wraith, Rachael Jeong, Jiwoon Arsanjani, Reza Kane, Garvan C. Oh, Jae K. Langlotz, Curtis P. Banerjee, Imon Fei-Fei, Li Adeli, Ehsan Department of Cardiovascular Medicine Mayo Clinic Rochester RochesterMN United States Stanford Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA United States Department of Radiology Mayo Clinic Arizona ScottsdaleAZ United States School of Computing and Augmented Intelligence Arizona State University PhoenixAZ United States Department of Cardiovascular Medicine Mayo Clinic Arizona ScottsdaleAZ United States Center of Artificial Intelligence in Medical Imaging Stanford University Stanford United States Department of Radiology Stanford University Stanford United States Department of Computer Science Stanford University Stanford United States Department of Psychiatry and Behavioral Sciences Stanford University Stanford United States

The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs. EchoNet: CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p © The Author(s) 2025.

关键词： Echocardiography

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Queer in HCI: Strengthening the Community of LGBTQIA+ Researchers and Research

Queer in HCI: Strengthening the Community of LGBTQIA+ Resear...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021

作者： Devito, Michael Ann Lustig, Caitlin Simpson, Ellen Allison, Kimberley Chuanromanee, Tya Spiel, Katta Ko, Amy Rode, Jennifer Dym, Brianna Muller, Michael Klaus Scheuerman, Morgan Marie Walker, Ashley Brubaker, Jed Ahmed, Alex Department of Information Science University of Colorado United States Human Centered Design and Engineering University of Washington United States School of Information Studies Syracuse University United States Department of Psychology Macquarie University Australia University of Notre Dame United States Human-Computer Interaction Group Tu Wien Austria The Information School University of Washington United States Knowledge Lab University College London United Kingdom Department of Information Science University of Colorado Boulder United States Ai Interactions Ibm Research United States Information Science University of Colorado Boulder United States Pitch Lab Northwestern University United States University of Colorado Boulder United States Carnegie Mellon University United States

ISBN: (纸本)9781450380959

As Queer human-computer interaction (HCI) becomes an established part of the larger field, both in terms of research on and with queer populations and in terms of employing queering theories and methods, the role of queer researchers has become a timely topic of discussion. However, these discussions have largely centered around member-researcher status and positionality when working with queer populations. Based on insights gathered at multiple ACM events over the past two years, we identified two pressing issues: (1) we need to better support queer people doing HCI research not specific to queer populations, and (2) we need to identify how to best support member-researchers in leading Queer HCI while including collaborators beyond the queer community. This Special Interest Group (SIG) aims to directly address these challenges by convening a broad community of queer researchers and allies, working not only on explicitly-queer topics but across a broad range of HCI topics. © 2021 Owner/Author.

关键词： Computation theory

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