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arXiv 2020年

作者： Tang, Shengpu Modi, Aditya Sjoding, Michael W. Wiens, Jenna Department of Electrical Engineering & Computer Science University of Michigan Ann Arbor United States Department of Internal Medicine Michigan Medicine University of Michigan Ann Arbor United States Institute for Healthcare Policy & Innovation University of Michigan Ann Arbor United States

Standard reinforcement learning (RL) aims to find an optimal policy that identifies the best action for each state. However, in healthcare settings, many actions may be near-equivalent with respect to the reward (e.g., survival). We consider an alternative objective – learning set-valued policies to capture near-equivalent actions that lead to similar cumulative rewards. We propose a model-free algorithm based on temporal difference learning and a near-greedy heuristic for action selection. We analyze the theoretical properties of the proposed algorithm, providing optimality guarantees and demonstrate our approach on simulated environments and a real clinical task. Empirically, the proposed algorithm exhibits good convergence properties and discovers meaningful near-equivalent actions. Our work provides theoretical, as well as practical, foundations for clinician/human-in-the-loop decision making, in which humans (e.g., clinicians, patients) can incorporate additional knowledge (e.g., side effects, patient preference) when selecting among near-equivalent actions. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Empowering Communities: A Co-creation model for addressing odour pollution in Thessaloniki, Greece

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Social Sciences and Humanities Open 2025年 11卷

作者： Karakosta, Charikleia Vlachos, Stavros Sideri, Vasiliki Siskos, Apostolos National Technical University of Athens School of Electrical and Computer Engineering Decision Support Systems Laboratory Energy Policy Unit (EPU-NTUA) 9 Iroon Polytechniou Str. Athens 157 80 Greece Department of Business Administration University of West Attica Petrou Ralli & Thivon 250 122 44 Greece ENVIROMETRICS Technical Consultants SA 3 Kodrou Str. 152 32 Greece

Odour pollution, often overlooked but significant, poses challenges to environmental and public health. The issue is particularly complex due to its subjective nature, with individual responses to odours varying widely. The paper aims to develop and validate a collaborative, multi-phase model designed to guide co-designing processes in addressing odour pollution at a local level. The model is thoroughly explained through illustrative examples from a case study in Thessaloniki, Greece, and emphasizes the importance of inclusiveness, coordination, and citizen science in addressing the issue. Following the successful pilot application and validation in Thessaloniki, the paper reflects on implementing the model to support citizen science interventions for odour pollution. Challenges and opportunities for replication are discussed, shedding light on the transformative potential of this co-creation approach. © 2025 The Authors

关键词： Citizen science Odour nuisance Participatory approach Stakeholder engagement Statistical analysis Thessaloniki

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Research needed in computational social science for power system reliability, resilience, and restoration

arXiv

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arXiv 2020年

作者： Valinejad, Jaber Mili, Lamine van der Wal, C. Natalie Bradley Department of Electrical and Computer Engineering Virginia Tech Northern Virginia Center Greater Washington D.C.VA22043 United States Department of Computer Science same university India Delft University of Technology Faculty of Technology Policy and Management dept. Multi-Actor Systems Netherlands

In the literature, smart grids are modeled as cyber-physical power systems without considering the computational social aspects. However, end-users are playing a key role in their operation and response to disturbances via demand response and distributed energy resources. Therefore, due to the critical role of active and passive end-users and the intermittency of renewable energy, smart grids must be planned and operated by considering the computational social aspects in addition to the technical aspects. The level of cooperation, flexibility, and other social features of the various stakeholders, including consumers, prosumers, and microgrids, affect the system efficiency, reliability, and resilience. In this paper, we design an artificial society simulating the interaction between power systems and the social communities that they serve via agent-based modeling inspired by Barsade’s theory on the emotional spread. The simulation results show a decline in the consumers’ and prosumers’ satisfaction levels induced by a shortage of electricity. It also shows the effects of social diffusion via the Internet and mass media on the satisfaction level. In view of the importance of computational social science for power system applications and the limited number of publications devoted to it, we provide a list of research topics that need to be achieved to enhance the reliability and resilience of power systems’ operation and planning. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Publisher Correction: wMel replacement of dengue-competent mosquitoes is robust to near-term climate change

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Nature Climate Change 2024年第1期14卷 106页

作者： Váleri N. Vásquez Lara M. Kueppers Gordana Rašić John M. Marshall Energy and Resources Group University of California Berkeley CA USA Department of Electrical Engineering and Computer Sciences College of Engineering University of California Berkeley CA USA Climate and Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA USA Mosquito Genomics QIMR Berghofer Medical Research Institute Brisbane Queensland Australia Divisions of Epidemiology and Biostatistics School of Public Health University of California Berkeley CA USA Innovative Genomics Institute University of California Berkeley CA USA

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FTP1.7 Critical decision analysis of pre-hospital major haemorrhage protocol activation

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British Journal of Surgery 2023年第SUPPLEMENT_6期110卷

作者： Wohlgemut, Jared McMullen, Heather Marsden, Max Marsh, William Perkins, Zane Tai, Nigel Centre for Trauma Sciences Blizard Institute Queen Mary University of London London UK Global Public Health Unit Centre for Public Health and Policy Wolfson Institute of Population Health Queen Mary University of London London UK Department of Electrical Engineering and Computer Science Queen Mary University of London London UK London’s Air Ambulance 17th Floor Royal London Hospital London UK Trauma Service Royal London Hospital Barts Health NHS Trust London UK

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metasnf: Meta Clustering with Similarity Network Fusion in R

arXiv

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arXiv 2024年

作者： Velayudhan, Prashanth S. Xu, Xiaoqiao Kallurkar, Prajkta Balbon, Ana Patricia Secara, Maria T. Taback, Adam Sabac, Denise Chan, Nicholas Ma, Shihao Wang, Bo Felsky, Daniel Ameis, Stephanie H. Cox, Brian Hawco, Colin Erdman, Lauren Wheeler, Anne L. The Hospital for Sick Children TorontoON Canada Department of Physiology The University of Toronto TorontoON Canada Centre for Computational Medicine Hospital for Sick Children TorontoON Canada Department of Obstetrics and Gynecology University of Toronto TorontoON Canada Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada Krembil Centre for Neuroinformatics Centre for Addiction and Mental Health TorontoON Canada Division of Rheumatology Hospital for Sick Children TorontoON Canada Vector Institute TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Division of Biostatistics Dalla Lana School of Public Health University of Toronto TorontoON Canada Department of Psychiatry University of Toronto TorontoON Canada Department of Anthropology University of Toronto TorontoON Canada Department of Psychiatry Temerty Faculty of Medicine University of Toronto TorontoON Canada Department of Psychiatry The Hospital for Sick Children TorontoON Canada Division of Gastroenterology Department of Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States James M. Anderson Center for Health Systems Excellence Cincinnati Children’s Hospital Medical Center CincinnatiOH United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States

metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by clustering the solutions themselves, to clustering workflows based on similarity network fusion (SNF). SNF is a multi-modal data integration algorithm commonly used for biomedical subtype discovery. The package also contains functions to assist with cluster visualization, characterization, and validation. This package can help researchers identify SNF-derived cluster solutions that are guided by context-specific utility over context-agnostic measures of quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Cluster analysis

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Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data: A Prospective UK Biobank Study

JACC: Advances

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JACC: Advances 2024年第9期3卷 101180-101180页

作者： Dorraki, Mohsen Liao, Zhibin Abbott, Derek Psaltis, Peter J. Baker, Emma Bidargaddi, Niranjan Wardill, Hannah R. van den Hengel, Anton Narula, Jagat Verjans, Johan W. School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia Australian Institute for Machine Learning (AIML) Adelaide Australia Lifelong Health Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia School of Electrical & Electronic Engineering University of Adelaide Adelaide Australia Faculty of Health and Medical Sciences The University of Adelaide Adelaide Australia Department of Cardiology Central Adelaide Local Health Network Adelaide Australia College of Medicine and Public Health Flinders University Adelaide Australia Precision Cancer Medicine Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia School of Biomedicine Faculty of Health and Medical Sciences University of Adelaide Adelaide Australia University of Texas Health Science Center Houston United States

Background: Robust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. The intricate relationship between mental health disorders and CVD is widely recognized. However, existing models often overlook psychological factors, relying on a limited set of clinical and lifestyle parameters, or being developed on restricted population subsets. Objectives: This study aims to assess the impact of integrating psychological data into a novel machine learning (ML) approach on enhancing CVD prediction performance. Methods: Using a comprehensive UK Biobank data set (n = 375,145), the correlation between CVD and traditional and psychological risk factors was examined. CVD included hypertensive disease, ischemic heart disease, heart failure, and arrhythmias. An ensemble ML model containing 5 constituent algorithms (decision tree, random forest, XGBoost, support vector machine, and deep neural network) was tested for its ability to predict CVD based on 2 training data sets: using traditional CVD risk factors alone, or using a combination of traditional and psychological risk factors. Results: A total of 375,145 subjects with normal health status and with CVD were included. The ensemble ML model could predict CVD with 71.31% accuracy using traditional CVD risk factors alone. However, by adding psychological factors to the training data, accuracy increased to 85.13%. The accuracy and robustness of the ensemble ML model outperformed all 5 constituent learning algorithms. Conclusions: Incorporating mental health assessment data within an ensemble ML model results in a significantly improved, highly accurate, CVD prediction model, outperforming traditional risk factor prediction alone. © 2024 The Authors

关键词： artificial intelligence cardiovascular disease cardiovascular prediction machine learning mental health

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Simulating the charging of electric vehicles by laser

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Procedia computer Science 2019年 155卷 527-534页

作者： Yuvrajsinh Rathod Larry Hughes Yuvrajsinh Rathod MASc student Electrical and Computer Engineering Dalhousie University Halifax Canada Larry Hughes Founding Fellow MacEachen Insittute for Public Policy and Goverance Dalhousie University Halifax Canada

At present, wired and wireless charging methods for electric vehicles (EVs) have a number of drawbacks including short charging ranges and long charging times. Laser power transfer (LPT) is a wireless power transfer technique which can be used for UAV (unmanned aerial vehicle) and satellite charging. Recent developments in photovoltaic cells and laser technology allows the transfer power using light which might overcome many of the issues related with other charging methods. In this paper, we describe the design and implementation (in MATLAB) of a novel, high-power charging method using a laser (monochromatic light) to charge electric vehicles. The paper examines the overall efficiency of the LPT for a various input power-level. In addition, it also examines the safety and the possible charging infrastructure for LPT technology. Using this technique, we believe that an overall efficiency of 10-37% can be achieved using existing technology.

关键词： Electric vehicle LPT Wireless charging

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The Family Level Assessment of Screen Use-Mobile Approach: Development of an Approach to Measure Children's Mobile Device Use

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JMIR Formative Research 2022年第10期6卷 e40452页

作者： Perez, Oriana Vadathya, Anil Kumar Beltran, Alicia Barnett, R. Matthew Hindera, Olivia Garza, Tatyana Musaad, Salma M. Baranowski, Tom Hughes, Sheryl O. Mendoza, Jason A. Sabharwal, Ashutosh Veeraraghavan, Ashok United States Department of Agriculture Agricultural Research Service Children's Nutrition Research Center Baylor College of Medicine Houston TX United States Department of Electrical & Computer Engineering Rice University Houston TX United States Center for Research Computing Rice University Houston TX United States Baylor College of Medicine Houston TX United States Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA United States Center for Child Health Behavior and Development Seattle Children's Research Institute Seattle WA United States

Background: There is a strong association between increased mobile device use and worse dietary habits, worse sleep outcomes, and poor academic performance in children. Self-report or parent-proxy report of children's screen time has been the most common method of measuring screen time, which may be imprecise or biased. Objective: The objective of this study was to assess the feasibility of measuring the screen time of children on mobile devices using the Family Level Assessment of Screen Use (FLASH)-mobile approach, an innovative method that leverages the existing features of the Android platform. Methods: This pilot study consisted of 2 laboratory-based observational feasibility studies and 2 home-based feasibility studies in the United States. A total of 48 parent-child dyads consisting of a parent and child aged 6 to 11 years participated in the pilot study. The children had to have their own or shared Android device. The laboratory-based studies included a standardized series of tasks while using the mobile device or watching television, which were video recorded. Video recordings were coded by staff for a gold standard comparison. The home-based studies instructed the parent-child dyads to use their mobile device as they typically use it over 3 days. Parents received a copy of the use logs at the end of the study and completed an exit interview in which they were asked to review their logs and share their perceptions and suggestions for the improvement of the FLASH-mobile approach. Results: The final version of the FLASH-mobile approach resulted in user identification compliance rates of >90% for smartphones and >80% for tablets. For laboratory-based studies, a mean agreement of 73.6% (SD 16.15%) was achieved compared with the gold standard (human coding of video recordings) in capturing the target child's mobile use. Qualitative feedback from parents and children revealed that parents found the FLASH-mobile approach useful for tracking how much time their chi

关键词： children mobile media apps mobile phone mobile phone use screen time tablet use

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Deep learning applied to chest X-Rays: Exploiting and preventing shortcuts

arXiv

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arXiv 2020年

作者： Jabbour, Sarah Fouhey, David Kazerooni, Ella Sjoding, Michael W. Wiens, Jenna Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Department of Radiology Department of Internal Medicine University of Michigan Ann ArborMI United States Institute for Healthcare Policy & Innovation Department of Internal Medicine University of Michigan Ann ArborMI United States

While deep learning has shown promise in improving the automated diagnosis of disease based on chest X-rays, deep networks may exhibit undesirable behavior related to shortcuts. This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest. For instance, clinical protocols might lead to a dataset in which patients with pacemakers are disproportionately likely to have congestive heart failure. This skew can lead to models that take shortcuts by heavily relying on the biased attribute. We explore this problem across a number of attributes in the context of diagnosing the cause of acute hypoxemic respiratory failure. Applied to chest X-rays, we show that i) deep nets can accurately identify many patient attributes including sex (AUROC = 0.96) and age (AUROC ≥ 0.90), ii) they tend to exploit correlations between such attributes and the outcome label when learning to predict a diagnosis, leading to poor performance when such correlations do not hold in the test population (e.g., everyone in the test set is male), and iii) a simple transfer learning approach is surprisingly effective at preventing the shortcut and promoting good generalization performance. On the task of diagnosing congestive heart failure based on a set of chest X-rays skewed towards older patients (age ≥ 63), the proposed approach improves generalization over standard training from 0.66 (95% CI: 0.54-0.77) to 0.84 (95% CI: 0.73-0.92) AUROC. While simple, the proposed approach has the potential to improve the performance of models across populations by encouraging reliance on clinically relevant manifestations of disease, i.e., those that a clinician would use to make a diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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