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Automated Abdominal Aortic Calcification Assessment and Major Adverse Cardiovascular Events in People Undergoing Osteoporosis Screening

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Heart, Lung and Circulation 2024年 33卷 S394-S395页

作者： C. Smith M. Sim Z. Illyas S. Gilani D. Suter S. Reid B. Monchka M. Jafari Jozani G. Figtree J. Schousboe W. Leslie J. Lewis Nutrition & Health Innovation Research Institute Edith Cowan University Perth WA Australia Medical School University of Western Australia Perth WA Australia Centre for AI&ML School of Science Edith Cowan University Perth WA Australia Department of Electrical and Computer Engineering University of Manitoba Winnipeg MB Canada George and Fay Yee Centre for Healthcare Innovation University of Manitoba Winnipeg MB Canada Department of Statistics University of Manitoba Winnipeg MB Canada Faculty of Health and Medicine The University of Sydney Sydney NSW Australia Kolling Institute of Medical Research St Leonards NSW Australia Park Nicollet Clinic and HealthPartners Institute HealthPartners Minneapolis MN USA Division of Health Policy and Management University of Minnesota Minneapolis MN USA Departments of Medicine and Radiology University of Manitoba Winnipeg MB Canada Centre for Kidney Research Children's Hospital at Westmead School of Public Health Sydney Medical School The University of Sydney Sydney NSW Australia

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Between Access and Privacy: Challenges in Sharing Health Data

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Yearbook of medical informatics 2018年第1期27卷 55-59页

作者： Malin, Bradley Goodman, Kenneth Department of Biomedical Informatics Vanderbilt University Nashville TN United States Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN United States Institute for Bioethics and Health Policy University of Miami Miami FL United States

OBJECTIVE: To summarize notable research contributions published in 2017 on data sharing and privacy issues in medical informatics.;METHODS: An extensive search of PubMed/Medline, Web of Science, ACM Digital Library, IEEE Xplore, and AAAI Digital Library was conducted to uncover the scientific contributions published in 2017 that addressed issues of biomedical data sharing, with a focus on data access and privacy. The selection process was based on three steps: (i) a selection of candidate best papers, (ii) the review of the candidate best papers by a team of international experts with respect to six predefined criteria, and (iii) the selection of the best papers by the editorial board of the Yearbook Results: Five best papers were selected. They cover the lifecycle of biomedical data collection, use, and sharing. The papers introduce 1) consenting strategies for emerging environments, 2) software for searching and retrieving datasets in organizationally distributed environments, 3) approaches to measure the privacy risks of sharing new data increasingly utilized in research and the clinical setting (e.g., genomic), 4) new cryptographic techniques for querying clinical data for cohort discovery, and 5) novel game theoretic strategies for publishing summary information about genome-phenome studies that balance the utility of the data with potential privacy risks to the participants of such studies.;CONCLUSION: The papers illustrated that there is no one-size-fitsall solution to privacy while working with biomedical data. At the same time, the papers show that there are opportunities for leveraging newly emerging technologies to enable data use while minimizing privacy risks. Georg Thieme Verlag KG Stuttgart.

关键词： Health records genomics data sharing privacy security

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Identification of cognitive resilience through neuropsychological tests in the Framingham Heart Study

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Phillip H Hwang Samad Amini Cody Karjadi Honghuang Lin Stacy L. Andersen Ioannis Paschalidis Rhoda Au Jesse B. Mez Boston University School of Medicine Boston MA USA Division of Systems Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA Boston University Alzheimer’s Disease Research Center Boston MA USA

Background To examine the predictive abilities of 1) traditional neuropsychological (NP) test summary scores, and 2) error and process scores in separating cognitively resilient individuals from those who exhibit cognitive decline. Method A total of 69 participants from the Framingham Heart Study (FHS) Offspring and Omni 1 cohorts who had ≥1 NP assessment from 2011–2014 and at high risk for dementia were included in the sample. Participants also had to be ≥60 years of age and dementia-free at the time of NP assessment. Being at high risk for dementia was determined using a cutoff score of ≥23 based on a published FHS dementia risk score algorithm. Dementia diagnosis was adjudicated based on DSM-IV criteria by consensus. Participants completed 10 NP tests, which were scored using 11 traditional scores and 75 error and process scores. Supervised machine learning algorithms were used to classify individuals who were cognitively resilient, defined as not progressing to dementia within ≥5 years after the NP evaluation. Result Among the 69 participants in our sample, 10 were cognitively resilient while 59 developed dementia. Average area under the curve when using traditional NP metrics to predict cognitive resilience ranged from 90.5% to 95.7% across each supervised machine learning algorithm (logistic regression, random forest, support vector machines, and light gradient boosting machine) (Figure 1). Average accuracy ranged from 85.3% to 92.6%, while average sensitivity was 100% across all models and average specificity ranged from 82.7% to 91.4% (Table 1). Average area under the curve when using the error and process scores to predict cognitive resilience ranged from 93.2% to 98.3% across each supervised machine learning algorithm (Figure 2). Average accuracy ranged from 89.9% to 97.0%, while average sensitivity was also 100% across all models and average specificity ranged from 88.2% to 96.5% (Table 2). All performance metrics were calculated based on five-fold cross-

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Challenges and Opportunities in Utilizing IoT-Based Stress Maps as a Community Mood Detector

Challenges and Opportunities in Utilizing IoT-Based Stress M...

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2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019

作者： Yuksel, Murat Wang, Wei Chaudhry, Shafaq Turgut, Damla Millican, Charles Kapucu, Naim Electrical Computer Engineering UCF 4328 Scorpius St. OrlandoFL32816 United States Graduate Center Baruch College CUNY 365 5th Ave. New YorkNY10016 United States Networking Wireless Systems Lab UCF 4328 Scorpius St. OrlandoFL32816 United States UCF Dept. of Computer Science 4328 Scorpius St. OrlandoFL32816 United States School of Public Administration UCF 4364 Scorpius St. OrlandoFL32816 United States

ISBN: (纸本)9781728150925

Stress has been known to cause physical and mental issues like depression, anxiety, insomnia, lower immunity, stroke, as well as leading to suicidal thoughts or violence towards others. Stress is not just a state of mind, but it is measurable. With the ubiquity of Internet of Things (IoT), and the integration with highly sensitive biosensors, it may be feasible to use these devices for detecting stress in public places. Moreover, correlating such stress data with social media streams can lead to insights into the psychological well-being of the community as a whole. We present a framework of such a community stress map based on social media and explore techniques for gathering data for measuring stress levels as well as detecting abnormal levels. This stress map can then be leveraged by emergency and crisis response teams for public safety and help them be proactive in allocating resources to the stressed areas indicated in the map. © 2019 IEEE.

关键词： Internet of things

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Coordinate: A Spreadsheet-Programmable Augmented Reality Framework for Immersive Map-Based Visualizations

Coordinate: A Spreadsheet-Programmable Augmented Reality Fra...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Aashiq Shaikh Linda Nguyen Alireza Bahremand Hannah Bartolomea Frank Liu Van Nguyen Derrick Anderson Robert LiKamWa School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA School of Arts Media and Engineering Arizona State University Tempe AZ USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Department of Psychology Arizona State University Tempe AZ USA School of Public Affairs Arizona State University Tempe AZ USA

ISBN: (数字)9781728156040

ISBN: (纸本)9781728156057

Augmented reality devices are opening up a new design space for immersive visualizations. 3D spatial content can be overlaid onto existing physical visualizations for new insights into the data. We present Coordinate, a collaborative analysis tool for augmented reality visualizations of map-based data designed for mobile devices. Coordinate leverages a spreadsheet-programmable web interface paired with a contemporary augmented reality infrastructure for an easy-to-use tool that can provide spatial information to multiple users. It also offers an immersive visualization experience that seeks to enrich presentations for business, educational, and scientific discussions.

关键词： Conferences Artificial intelligence Virtual reality 6G mobile communication Integrated circuits

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Non-invasive wearable system for Hypoglycemia detection: A proof of concept user-centered design process 62

Non-invasive wearable system for Hypoglycemia detection: A p...

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62nd Human Factors and Ergonomics Society Annual Meeting, HFES 2018

作者： Zhu, Yibo Zahed, Karim Mehta, Ranjana K. Sasangohar, Farzan Erraguntla, Madhav Lawley, Mark Abbas, Hasan Tahir Qaraqe, Khaled School of Public Health Department of Environmental and Occupational Safety Texas A and M University College StationTX United States Department of Industrial and Systems Engineering Texas A and M University College StationTX United States Department of Electrical and Computer Engineering Texas A and M University Qatar Doha Qatar

ISBN: (纸本)9781510889538

About 425 million adults around the world were living with diabetes in 2017. A relevant condition called Hypoglycemia is characterized by a dangerous low level of blood sugar that could be fatal to diabetic patients. Continuous glucose monitoring systems (CGMS) are the most popular commercially available technology for detecting diabetic hypoglycemia. However, CGMSs are invasive, costly, and not user-centric thereby not sustainable for diabetes management. This paper documents our initial efforts in designing an inexpensive, non-invasive, wearable physiological tremor sensory system to detect the onset of hypoglycemic events of diabetic patients. The design cycle briefly presented here includes: 1) determination of system (technology and user) requirements, 2) development of the tremor detection prototype, and 3) testing and validation of the system in non-clinical and clinical settings using human factors, data analytics, and biomedical sciences techniques and approaches. © 2018 Human Factors an Ergonomics Society Inc.. All rights reserved.

关键词： Insulin

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Joint Temporal Dietary and Physical Activity Patterns: Associations with Health Status Indicators and Chronic Diseases

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Current Developments in Nutrition 2020年第SUPPL 2期4卷 4140590-4140590页

作者： Lin, Luotao Aqeel, Marah Guo, Jiaqi Gelfand, Saul Delp, Edward Bhadra, Anindya Richards, Elizabeth Hennessy, Erin Eicher-Miller, Heather Department of Nutrition Science Purdue University School of Electrical and Computer Engineering Purdue University Department of Statistics Purdue University School of Nursing Purdue University Friedman School of Nutrition Science and Policy Tufts University

AbstractObjectivesThe aims of this study are to (1) create clusters of joint temporal patterns of diet and physical activity (PA), and (2) determine the association of these clusters with health status indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure and health outcomes including obesity, Type 2 Diabetes (T2DM) and metabolic syndrome (MS) in U.S. adults 20–65 years. MethodsA random day of PA from accelerometry data and the first day 24 hour dietary recall collected in the National Health and Nutrition Examination Survey 2003–2006 were used to determine absolute PA intensity, absolute energy intake, and the time of these activities. Dietary and PA data from 1,627 U.S. adults were Z-normalized. Dynamic time warping (DTW) coupled with kernel-k means clustering algorithm was used to develop joint temporal dietary and PA patterns that maximally partition individuals with similar temporal behaviors into mutually exclusive clusters derived from the data rather than predefined standards. Multivariate regression models adjusted for ptential confounders, multiple comparisons and survey design factors determined associations between joint temporal patterns and health status indicators along with health outcomes ( P < 0.05/6). ResultsSignificant differences of health status indicators and health outcomes were discovered among four clusters. A cluster, representing a joint temporal dietary and PA pattern with proportionally equivalent average energy consumed with two energy intake peaks at 1 PM and 8 PM and the lowest PA intensity compared to all other clusters, was associated with significantly higher BMI (β: 3.5, P < 0.0001), WC (β: 9.5, P < 0.0001), and significantly higher odds of obesity (Odds Ratio = 4.685, P < 0.0001) compared to a cluster with similar energy and intake peaks and the highest PA intensity compared to all other c

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Temporal Physical Activity Patterns and Association with Health Status Indicators and Chronic Disease

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Current Developments in Nutrition 2020年第SUPPL 2期4卷 4141166-4141166页

作者： Eicher-Miller, Heather Aqeel, Marah Guo, Jiaqi Gelfand, Saul Delp, Edward Bhadra, Anindya Richards, Elizabeth Hennessy, Erin Lin, Luotao Department of Nutrition Science Purdue University School of Electrical and Computer Engineering Purdue University Department of Statistics Purdue University School of Nursing Purdue University Friedman School of Nutrition Science and Policy Tufts University

AbstractObjectivesIntegration of time to physical activity (PA) patterns is novel and may be linked to health. Temporal PA patterns integrating time and PA were created to determine their association with health indicators: body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure, and chronic diseases obesity, diabetes, and metabolic syndrome in US adults 20–65 years. MethodsObjectively measured PA accelerometry data collected from the National Health and Nutrition Examination Survey 2003–2006 was used to pattern absolute PA intensity and time of activity from 1627 non-pregnant adults with one random valid PA day. Modified dynamic time warping and kernel k-means clustering grouped individuals representing temporal PA patterns. Multivariate regression models controlling for potential confounders and adjusting for multiple comparisons ( P < 0.05/6) determined associations between clusters and health outcomes. ResultsA cluster representing a temporal PA pattern with the highest average intensity peaking at 11:00 and tapering off through the day was associated with lower BMI ( P < 0.0001), WC ( P < 0.0001), and 65% lower odds of obesity compared to a cluster with lower average PA intensity peaking at 12:00 and tapering off through the day (95%CI: 0.2,0.5) and a cluster with the lowest average PA intensity with no distinct activity peaks (95%CI: 0.2,0.4). Another cluster with a temporal PA pattern with high average PA intensity peaking at 19:00 was associated with lower BMI ( P = 0.0003), WC ( P = 0.001), and 60% lower odds of obesity compared to a cluster with lower average PA intensity peaking at 12:00 and tapering off through the day (95%CI: 0.2,0.7) and a cluster with the lowest average PA intensity with no distinct peaks (95%CI: 0.2,0.6). ConclusionsTemporal PA patterns are associated with differences in US adult health outcomes. Two clusters with th

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Temporal Dietary Patterns Are Associated with Body Mass Index, Waist Circumference and Obesity

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Current Developments in Nutrition 2020年第SUPPL 2期4卷 4140518-4140518页

AbstractObjectivesThe integration of time with dietary patterns throughout a day, or temporal dietary patterns (TDP), have been linked with dietary quality but links to health outcomes are unknown. TDP were created with the objective to examine their association with health status indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure, and chronic diseases obesity, diabetes, and metabolic syndrome in US adults 20–65 years. MethodsThe first-day 24-hour dietary recall from 1627 non-pregnant US adult participants of the cross-sectional National Health and Nutrition Examination Survey 2003–2006 was used to determine energy intake (kcal), time of intake (min), and sequence of intake occasions throughout the 24-hour day. Modified dynamic time warping coupled with kernel k-means algorithm, clustered participants into four groups representing distinct TDP. Multivariate regression models determined associations between TDP clusters and all outcomes, controlling for potential confounders, energy misreporting, and adjusting for multiple comparisons and the complex survey design ( P < 0.05/6). ResultsThe cluster representing a TDP with proportionally equivalent average energy at three main eating occasions from 8:00 to 23:00 with peaks reaching 175 kcal at 9:00, 13:00, and 19:00, had statistically significant and clinically meaningful lower BMI ( P < 0.0001), WC ( P < 0.0001) and 75% lower odds of obesity compared to three other clusters representing distinct patterns of much higher average peak energy intake of 500 kcal at 13:00 (odds ratio (OR): 4.4; 95% confidence interval (CI)): 2.5, 7.9), 530 kcal at 18:00 (OR: 5.3; 95% CI: 2.8, 10.1), and 550 kcal at 20:00 (OR: 6.7; 95%CI: 3.9, 11.6). ConclusionsA positive association of the TDP of moderate energy intake throughout the day with healthy weight outcomes supports previous findings of hi

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Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

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Exposome 2024年第1期4卷 osae001页

作者： Ming Kei Chung John S House Farida S Akhtari Konstantinos C Makris Michael A Langston Khandaker Talat Islam Philip Holmes Marc Chadeau-Hyam Alex I Smirnov Xiuxia Du Anne E Thessen Yuxia Cui Kai Zhang Arjun K Manrai Alison Motsinger-Reif Chirag J Patel Department of Biomedical Informatics Harvard Medical School Boston MA USA. School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong China. Institute of Environment Energy and Sustainability The Chinese University of Hong Kong Hong Kong China. Biostatistics and Computational Biology Branch Division of Intramural Research National Institute of Environmental Health Sciences Durham NC USA. Cyprus International Institute for Environmental and Public Health School of Health Sciences Cyprus University of Technology Limassol Cyprus. Department of Electrical Engineering and Computer Science University of TN Knoxville TN USA. Department of Population and Public Health Sciences Keck School of Medicine of the University of Southern CA Los Angeles CA USA. Department of Physics Villanova University Villanova Philadelphia USA. Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Chemistry NC State University Raleigh NC USA. Department of Bioinformatics and Genomics College of Computing and Informatics University of NC at Charlotte Charlotte NC USA. Department of Biomedical Informatics University of CO Anschutz Medical Campus Aurora CO USA. Exposure Response and Technology Branch Division of Extramural Research and Training National Institute of Environmental Health Sciences Durham NC USA. Department of Environmental Health Sciences School of Public Health University at Albany State University of NY Rensselaer NY USA.

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

关键词： Exposome-Wide Association Study (ExWAS) data science epidemiology exposome false discovery rate phenotype

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