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SSRN 2022年

作者： Hu, Ming Zhang, Kai Nguyen, Quynh Tasdizen, Tolga Krusche, Krupali Uplekar School of Architecture Planning and Preservation University of Maryland 3835 Campus Drive College ParkMD20742 United States Department of Environmental Health Sciences School of Public Health University at Albany State University of New York One University Place | Room 157 | RensselaerNY12144 United States Epidemiology and Biostatistics University of Maryland School of Public Health Atlantic Bldg. #224 Room 1340K 4254 Stadium Dr College ParkMD20742 United States Electrical and Computer Engineering University of Utah 72 S Central Campus Drive 3750 WEB Salt Lake CityUT84112 United States School of Architecture University of Notre Dame 14 Walsh Family Hall of Architecture Notre DameIN46556 United States

As climate change increases the frequency and intensity of devastating and unpredictable extreme heat events, developments to the built environment should consider instigating practices that minimize the likelihood of indoor overheating during hot weather. Heatwaves are the leading cause of death among weather-related causes worldwide, including in developed and developing countries. In this empirical study, three housing characteristic categories (i.e., general housing conditions, living conditions, and housing thermal resistance) and eight variables were analyzed in twenty-seven states in the United States using multivariable regression models. Three variables—housing age (HA), housing crowding ratio (HCR), and roof condition (RC)—were found to be correlated with the risk of heat-related illness (HRI) indexes. Then, a logistic regression model was generated using the three variables to predict the risk of heat-related emergency department visits (EDV) and heat-related mortality (MORD) on a state level. The results indicate that the proposed logistic regression model correctly predicted 100% of the high-risk states for MORD for the eight states tested. Overall, this analysis provides additional evidence about the housing character variables that influence HRI. The outcomes also demonstrate that the built environment, especially housing, should be considered in techniques for mitigating climate change-exacerbated health conditions. © 2022, The Authors. All rights reserved.

关键词： Heat resistance

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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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Predicting Transitions from Mild Cognitive Impairment to Dementia within 5-years

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Alzheimer's & Dementia 2023年第S18期19卷

作者： Samad Amini Boran Hao Jingmei Yang 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 Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA College of Arts & Sciences 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 Early detection of individuals at high risk of future progression toward dementia is in great need, particularly in the era of emerging treatments for Alzheimer’s disease. Given the easier administration of neuropsychological (NP) tests over invasive and expensive biomarker acquisition, we seek to predict the future cognitive status of an individual based on digital voice recordings of NP exams. Method We utilized automatic speech recognition and natural language processing techniques to create an automated tool that predicts if an individual with mild cognitive impairment (MCI) will develop dementia in a fixed follow-up window (5 years). We characterize the predictive accuracy of this approach using data from the Framingham Heart Study. First, the automated transcription of the digital voice recordings were classified into 8 main NP sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using a large language model, only the participants’ sentences were encoded into quantitative data. These data and the participants’ characteristics, such as age, sex, ApoE4 (+/-) genotype, and education were employed to train and test a binary classifier, predicting dementia withing 5 years after a positive MCI diagnosis. We also provide a comparison between our approach and alternative models using demographics and ApoE genotype or the Mini-Mental State Examination (MMSE) score. Result We evaluated the performance of the classification task on the data containing 111 stable MCI and 103 progressive MCI subjects. Our method achieved an average accuracy of 75.5% on the held-out test data while a baseline model using age, sex, education, and ApoE has an average accuracy of 71.8%. The sensitivity and specificity of the proposed method versus the baseline model are 78.2% vs. 81.8% and 72.7% vs. 61.8%, respectively. Our approach also significantly outperforms the model based on the MMSE score by a margin of 14% in accuracy. Co

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The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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The false hope of current approaches to explainable artificial intelligence in health care

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The Lancet Digital Health 2021年第11期3卷 e745-e750页

作者： Ghassemi, Marzyeh Oakden-Rayner, Luke Beam, Andrew L Department of Electrical Engineering and Computer Science and Institute for Medical and Evaluative Sciences Massachusetts Institute of Technology Cambridge MA United States Vector Institute Toronto ON Canada Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia CAUSALab and Department of Epidemiology Harvard T H Chan School of Public Health Boston MA United States Department of Biomedical Informatics Harvard Medical School Boston MA United States

The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of bias. In this Viewpoint, we argue that this argument represents a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients. In the absence of suitable explainability methods, we advocate for rigorous internal and external validation of AI models as a more direct means of achieving the goals often associated with explainability, and we caution against having explainability be a requirement for clinically deployed models. © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

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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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Shedding light on inequities of power outages through data transparency

Progress in Energy

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Progress in Energy 2025年第3期7卷 033003-033003页

作者： Kelly A Stevens Sanam K Aksha Mohammad Siddiqur Rahman Elizabeth Trader Herbert Longenecker Rebecca Entress Thomas Wahl Christopher Emrich Kristopher O Davis School of Public Administration University of Central Florida Orlando FL United States of America Resilient Intelligent and Sustainable Energy Systems (RISES) Research Center University of Central Florida Orlando FL United States of America Florida Solar Energy Center (FSEC) University of Central Florida Orlando FL United States of America Department of Civil Environmental and Construction Engineering University of Central Florida Orlando FL United States of America National Center for Integrated Coastal Research University of Central Florida Orlando FL United States of America Department of Electrical and Computer Engineering University of Central Florida Orlando FL United States of America Department of Political Science University of South Carolina Columbia SC United States of America Department of Materials Science and Engineering University of Central Florida Orlando FL United States of America

In a changing climate, the electricity grid is increasingly exposed to more extreme weather that results in significant power outages. Further, lower income and racial-ethnic minority households are more likely to experience a higher frequency and duration of power outages compared to less socially vulnerable groups. The literature on the unequal impacts of power outages is a new, growing area of research that has been stymied in part due to the difficulty acquiring high spatial resolution, historical power outage data from utilities. In this Perspectives article, we review the availability of power outage data, and what can be learned from higher spatial resolution data about the uneven distribution of power outages. We illustrate this with a case study evaluating power outage data in Florida from 2018–2022 at the county and zip-code level. We compare this to literature on spatial heterogeneity of social vulnerability indicators to identify ways that higher spatial resolution outage data can better measure inequities of power outages. Last, we provide feedback from stakeholders in Central Florida in local government, utilities, and community groups on the ways that higher resolution outage data could be used for improving equitable grid resilience.

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Author Correction: Africa needs context-relevant evidence to shape its clean energy future

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Nature Energy 2024年 907页

作者： Yacob Mulugetta Lucas Somavilla Croxatto Meron Tesfamichael Abdulmutalib Yussuff Youba Sokona Philipp A. Trotter Samuel Fankhauser Jessica Omukuti Bjarne Steffen Edo Abraham Jean-Paul Adam Mekalia Paulos Aklilu Yohannes Hailu Linus Mofor S. Nadia Ouedraogo Lawrence Agbemabiese Churchill Agutu Florian Egli Abdulrasheed Isah Tobias S. Schmidt Olakunle Alao Wikus Kruger Peter Twesigye Bothwell Batidzirai Getachew Bekele Anteneh G. Dagnachew Ogunlade Davidson Fatima Denton E. Ogheneruona Diemuodeke Gebrekidan Gebresilassie Eshetu Mulualem Gebreslassie Mamadou Goundiam Haruna Kachalla Gujba Adam D. Hawkes Stephanie Hirmer Helen Hoka Mark Howells Daniel Kammen Francis Kemausuor Ismail Khennas Ifeoma Malo Minette Nago Destenie Nock Chukwumerije Okereke Benedict Probst Maria Schmidt Carlos Shenga Mohamed Sokona Jan Christoph Steckel Sebastian Sterl Bernard Tembo Julia Tomei Jim Watson Harald Winkler Department of Science Technology Engineering & Public Policy University College London London UK Responsible Technology Institute Department of Computer Science University of Oxford Oxford UK Groupe de Reflection et d’Initiatives Novatrices Bamako Mali Schumpeter School of Business and Economics University of Wuppertal Wuppertal Germany Smith School of Enterprise and the Environment University of Oxford Oxford UK Institute for Science Innovation and Society (INSIS) University of Oxford Oxford UK Climate Finance and Policy Group ETH Zurich Zurich Switzerland Department of Water Management Delft University of Technology Delft the Netherlands United Nations Economic Commission for Africa Addis Ababa Ethiopia Center for Energy & Environmental Policy University of Delaware Newark DE USA Energy and Technology Policy Group ETH Zurich Zurich Switzerland Kigali Collaborative Research Centre Kigali Rwanda IIPP Institute for Innovation and Public Purpose University College London London UK Institute of Science Technology and Policy ETH Zurich Zurich Switzerland Power Futures Lab Graduate School of Business University of Cape Town Cape Town South Africa African Union Development Agency (AUDA-NEPAD) Midrand Johannesburg South Africa School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa Ethiopia PBL Netherlands Environmental Assessment Agency the Hague the Netherlands Utrecht University Utrecht the Netherlands University of Sierra Leone Freetown Sierra Leone United Nations University–Institute for Natural Resources in Africa (UNU–INRA) Accra Ghana Department of Mechanical Engineering University of Port Harcourt Choba Nigeria Institute for Power Electronics and Electrical Drives RWTH Aachen University Aachen Germany Center of Energy Ethiopian Institute of Technology Mekelle University Mekelle Ethiopia Institute of Engineering University Grenoble Alpes Grenoble France GIZ — Africa–EU Energy Partnership (AEEP) Addis

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Pattern-Based Analysis of Time Series: Estimation

arXiv

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arXiv 2020年

作者： Sabeti, Elyas Song, Peter X.K. Hero, Alfred O. Michigan Institute for Data Science University of Michigan Ann ArborMI48109 United States School of Public Health Department of Biostatistics University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

While Internet of Things (IoT) devices and sensors create continuous streams of information, Big Data infrastructures are deemed to handle the influx of data in real-time. One type of such a continuous stream of information is time series data. Due to the richness of information in time series and inadequacy of summary statistics to encapsulate structures and patterns in such data, development of new approaches to learn time series is of interest. In this paper, we propose a novel method, called pattern tree, to learn patterns in the times-series using a binary-structured tree. While a pattern tree can be used for many purposes such as lossless compression, prediction and anomaly detection, in this paper we focus on its application in time series estimation and forecasting. In comparison to other methods, our proposed pattern tree method improves the mean squared error of estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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Ethical machine learning in health care

arXiv

引用

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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