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arXiv 2018年

作者： Danubianu, Mirela Pentiuc, Stefan Gheorghe Tobolcea, Iolanda Socaciu, Tiberiu Ştefan cel Mare" University of Suceava Faculty of Electrical Engineering and Computer Science 1 Universitatii str. Suceava720229 Romania Ştefan cel Mare" University of Suceava Faculty of Electrical Engineering and Computer Science 1 Universitatii str. Suceava720229 Romania A.I.Cuza" University of Iasi Faculty of Psyhology and Educational Sciences 3 TomaCozma str. Iasi700554 Romania Ştefan cel Mare" University of Suceava Faculty of Economic Science and Public Administration 1 Universitatii str. Suceava720229 Romania

Lately, the children with speech disorder have more and more become object of specialists’ attention and investment in speech disorder therapy are increasing The development and use of information technology in order to assist and follow speech disorder therapy allowed researchers to collect a considerable volume of data. The aim of this paper is to present a data mining system designed to be associated with TERAPERS system in order to provide information based on which one could improve the process of personalized therapy of speech disorders. Copyright © 2018, The Authors. All rights reserved.

关键词： Data mining

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Comparison study of displacement estimation methods for microwave ablation procedures using electrode displacement elastography

Comparison study of displacement estimation methods for micr...

引用

2017 IEEE International Ultrasonics Symposium, IUS 2017

作者： Pohlman, Robert Jiang, Jingfeng Yang, Wenjun Ziemlewicz, Timothy J. Alexander, Marci Wergin, Kelly Lubner, Meghan Hinshaw, James Louis Lee, Fred T. Varghese, Tomy Department of Medical Physics University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI United States Department of Biomedical Engineering Michigan Technological University HoughtonMI United States Department of Radiology University of Wisconsin School of Medicine and Public Health MadisonWI United States

ISBN: (纸本)9781538633830

Minimally invasive procedures such as microwave ablation (MWA) are growing in popularity as a substitute for surgical resection of hepatocellular carcinomas. Ultrasound electrode displacement elastography (EDE) has demonstrated the potential to provide a feasible non-invasive and non-ionizing approach for monitoring the region of thermal necrosis. © 2017 IEEE.

关键词： Signal to noise ratio Estimation Strain Microwave imaging Microwave theory and techniques Electrodes Elastography

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Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 (vol 395, pg 1779, 2020)

引用

LANCET 2020年第10239期395卷 1762-1762页

作者： Reiner, R. C., Jr. Hay, S., I Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Global Health School of Medicine University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States College of Health and Medical Sciences Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics Haramaya University Harar Ethiopia Department of Medical Laboratory Sciences Haramaya University Harar Ethiopia School of Nursing and Midwifery Haramaya University Harar Ethiopia School of Pharmacy Haramaya University Harar Ethiopia School of Public Health Haramaya University Harar Ethiopia Haramaya University Harar Ethiopia Advanced Diagnostic and Interventional Radiology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Biology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Research Institute Tehran University of Medical Sciences Tehran Iran Department of Economics and Management Sciences for Health Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Department of Microbiology Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Hematology-Oncology and Stem Cell Transplantation Research Center Tehran University of Medical Sciences Tehran Iran Iran National Institute of Health Research Tehran University of Medical Sciences Tehran Iran Metabolomics and

Summary Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood *** We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor *** The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1-65·8), 17·4% (7·7-28·4), and 59·5% (34·2-86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy *** By co-analysing geospatial trends in d

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Enhanced differential super class-AB OTA 13

Enhanced differential super class-AB OTA

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13th Conference on Ph.D. Research in Microelectronics and Electronics, PRIME 2017

作者： Garde, M. Pilar Lopez-Martin, Antonio Ramírez-Angulo, Jaime Institute of Smart Cities Public University of Navarra Pamplona Spain Klipsch School of Electrical and Computer Engineering New Mexico State University Box 30001/Dept.3-0 Las CrucesNM88003-0001 United States

ISBN: (纸本)9781509065073

A fully differential Super Class AB Operational Transconductance Amplifier (OTA) is presented. To provide additional dynamic current boosting and increased gain-bandwidth product (GBW), not only adaptive biasing techniques has been applied at the differential input stage, but also Local Common Mode Feedback (LCMFB). Additionally, Quasi Floating Gate (QFG) transistors are employed, enhancing the performance of the amplifier. The OTA has been fabricated in a standard 0.5-μm CMOS process. Simulation results yield a slew rate improvement by factor 86 and a GBW enhancement by factor 16 when compared to the class A OTA while driving the same 70-pF load. Supply voltages are ±1-V and the value of the quiescent current is 10 μA. The overhead in terms of quiescent power, silicon area, and noise level is small. © 2017 IEEE.

关键词： Operational amplifiers

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Improved common-mode feedback based on LCMFB techniques 13

Improved common-mode feedback based on LCMFB techniques

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13th Conference on Ph.D. Research in Microelectronics and Electronics, PRIME 2017

作者： Garde, M. Pilar Lopez-Martin, Antonio Ramirez-Angulo, Jaime Institute of Smart Cities Public University of Navarra Pamplona Spain Klipsch School of Electrical and Computer Engineering New Mexico State University Box 30001/Dept.3-0 Las CrucesNM88003-0001 United States

ISBN: (纸本)9781509065073

A novel class AB Differential Difference Amplifier (DDA) for use in continuous-time common-mode feedback circuits is proposed. It is based on using resistive Local Common-Mode Feedback (LCMFB) to enhance gain-bandwidth product (GBW) and dynamic operation of the common-mode feedback loop, without requiring extra static power consumption or supply voltage requirements. Simulation results for a 0.5 μm CMOS process show an increase in open loop DC gain (ADC) of 15.3 dB and also in GBW by a factor of 5.5. Additionally, measurement results for a test chip prototype fabricated in the same process validate the proposal. © 2017 IEEE.

关键词： Timing circuits

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Toward responsible AI governance: Balancing multi-stakeholder perspectives on AI in healthcare

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International Journal of Medical Informatics 2025年

作者： Leon Rozenblit Amy Price Anthony Solomonides Amanda L. Joseph Eileen Koski Gyana Srivastava Steven Labkoff David Bray Monica Lopez-Gonzalez Reva Singh Dave deBronkart Paul J. Barr Peter Szolovits Kiran Dattani Charles Jaffe Douglas Fridsma Russell Baris Russell Leftwich Robert Stolper Mark G. Weiner Yuri Quintana Q.E.D. Institute New Haven CT USA Division of Clinical Informatics Beth Israel Deaconess Medical Center Boston MA USA Yale School of Management New Haven CT USA The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Hanover NH USA BMJ London UK Research Institute Endeavor Health Evanston IL USA School of Health Information Science University of Victoria Victoria BC Canada Homewood Research Institute Guelph Ontario Canada IBM Research Yorktown Heights NY USA Quantori Boston MA USA Loomis Innovation Council Stimson Center Washington DC USA Business Executives for National Security Washington DC USA Cognitive Insights for Artificial Intelligence Baltimore MD USA American Medical Informatics Association Washington DC USA Society for Participatory Medicine Boston MA USA HL7® Patient Empowerment Workgroup USA The BMJ’s Patient Advisory Panel London UK Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Google Cloud Healthcare and Life Sciences New York NY USA Health Level 7 International Ann Arbor MI USA Health Universe San Francisco CA USA eLumindata Westport CT USA Department of Biomedical Informatics Vanderbilt University Nashville TN USA IQVIA Boston MA USA Department of Population Health Sciences Weill Cornell Medicine New York NY USA Department of Medicine Harvard Medical School Boston MA USA

Introduction The rapid integration of artificial intelligence (AI) into healthcare presents significant governance challenges, requiring balanced approaches that safeguard safety, efficacy, equity, and trust (SEET). This study proposes a cognitive framework to guide AI governance, addressing tradeoffs between speed, scope, and capability. Objective To develop a structured governance model that harmonizes stakeholder perspectives, focusing on multi-dimensional challenges and ethical principles essential for AI in healthcare. Methods A multidisciplinary team convened at the Blueprints for Trust conference, organized by the American Medical Informatics Association (AMIA), and the Division of Clinical Informatics at Beth Israel Deaconess Medical Center. Following extensive discussions with 190 participants across sectors, three governance models were identified to address specific domains: (1) Clinical Decision Support (CDS), (2) Real-World Evidence (RWE), (3) Consumer Health (CH). Results Three governance models emerged, tailored to CDS, RWE, and CH domains. Key recommendations include establishing a Health AI Consumer Consortium for patient-centered oversight, initiating voluntary accreditation and certification frameworks, and piloting risk-level-based standards. These models balance rapid adaptation with SEET-focused safeguards through transparency, inclusivity, and ongoing learning. Conclusion A proactive, constraint-based governance framework is critical for responsible AI integration in healthcare. This structured, multi-stakeholder approach provides a roadmap for ethical, transparent governance that can evolve with technological advancements, enhancing trust and safety in healthcare AI applications.

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Development of an astronaut crew behavioral skills taxonomy for medical event management on future long duration exploration missions 69

Development of an astronaut crew behavioral skills taxonomy ...

引用

69th International Astronautical Congress: #InvolvingEveryone, IAC 2018

作者： Yule, S. Robertson, J.M. Dias, R.D. Doyle, T. Gupta, A. Singh, S. Lipsitz, S. Pozner, C.N. Smink, D.S. Thorgrimson, J.L. Musson, D. Brigham and Women's Hospital Surgery BostonMA United States Neil and Elise Wallace STRATUS Center for Medical Simulation Brigham and Women's Hospital BostonMA United States Brigham and Women's Hospital Center for Surgery and Public Health BostonMA United States Harvard Medical School BostonMA United States Brigham and Women's Hospital Emergency Medicine BostonMA United States Northern Ontario School of Medicine Thunder BayON Canada McMaster University Department of Electrical and Computer Engineering HamiltonON Canada

Unexpected medical events during long-duration space missions may significantly impact crew health and compromise mission success. Significant factors impacting the outcome of medical events include limited options for premature return to Earth and mission designs scenarios that include delayed communication with ground control. The high acuity, low frequency nature of medical events in spaceflight mirror those occurring in terrestrial surgical environments. Non-technical skills (NTS), including situation awareness, leadership and team coordination, have been shown to increase patient safety and improve outcomes. NTS are behavioural skills;theorized to act through the reduction of performance errors and unexpected events. This study described an approach to developing a NTS taxonomy and behavioural marker system to support medical event management during future long duration exploration missions. This program is comprised of several stages, beginning with an expert panel prioritizing nontechnical skills and medical conditions for simulation and scenario design. A spacecraft themed simulator was designed, constructed and used to simulate and film medical events deemed most likely to occur in spaceflight. The overarching goal is to create a simulation-based training program for astronauts on long duration space missions, improve outcomes in spaceflight medical events, and reduce the overall health risk on long duration exploration missions. Copyright © 2018 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Health risks

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Distributed optical fiber microphone

Distributed optical fiber microphone

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IEEE Sensors

作者： C. R. Zamarreno C. Martelli R. Daciuk G. Dutra U. J. Dreyer J. C. Cardozo Da Silva I. R. Matias F. J. Arregui Electrical and Electronic Engineering Department Public University of Navarra Pamplona NA/Spain Graduate School on Electrical Engineering and Applied Computer Science Federal University of Technology Curitiba PR/Brazil

ISBN: (纸本)9781509010134

A distributed acoustic sensing (DAS) optical fiber system has been studied using intensity-based optical time-domain reflectometry (i-OTDR). The fiber strain originated by sound waves (pressure waves) is in general quite small to enable the detection using conventional Rayleigh backscattering intensity based systems (low signal to noise ratio). Here, a 10 m portion of optical fiber has been rolled following a plane spiral shape in order increase the fiber surface subjected to the acoustic waves pressure and improve the signal to noise ratio (SNR). The performance of the system within the human voice frequency range (300-3400Hz) has been studied showing to effectively acquire voice signals at different points along a ～2.5 Km fiber.

关键词： Distributed acoustic sensing Rayleigh scattering optical fiber optical microphone

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