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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Spectrum Sharing Between WLANs and Fixed Microwave Links in 6 and 13 GHz Bands: a Case Study

Spectrum Sharing Between WLANs and Fixed Microwave Links in ...

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IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： Nadia Yoza-Mitsuishi Ruoyu Sun Peter Mathys Technology Cybersecurity and Policy Program University of Colorado Boulder Boulder CO R&D Wireless CableLabs Louisville CO Department of Electrical and Computer Engineering University of Colorado Boulder Boulder CO

ISBN: (纸本)9781728123776

The 6 GHz band (5925-7125 MHz) has recently been proposed by the FCC to be shared with unlicensed devices This band is allocated to fixed, mobile (temporary fixed) and fixed satellite service links. The 13 GHz band (12700-13250 MHz) could also be subject to more flexible use, considering that it is allocated to the same type of incumbents. The purpose of this paper is to analyze coexistence between Wireless Local Area Networks (WLANs) and current terrestrial fixed links in these bands based on a case study approach, which can be replicated to other parts of the country. The coexistence analysis uses information from real links and accounts for the terrain morphology and actual land use to calculate the potential interference. We have conducted interference simulations from low-power indoor (LPI) WLANs to terrestrial fixed links and vice versa, from terrestrial fixed links to LPI WLANs, in the Denver metro area. The simulations have been performed in the upper part of the 6 GHz band, from 6875 to 7125 MHz, designated as the U-NII-8 band by the FCC, and in the 13 GHz band. To validate the simulations, we measured the incumbent signal power detected on the ground in a few locations in this area. We found that terrestrial fixed links operating in these bands can coexist with LPI WLANs without significant interference between each other, which will benefit consumers and encourage technology and innovation.

关键词： Interference FCC Wireless fidelity Databases Antennas Automobiles

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Detection of self-reported experiences with corruption on twitter using unsupervised machine learning

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Social Sciences and Humanities Open 2020年第1期2卷

作者： Li, Jiawei Chen, Wen-Hao Xu, Qing Shah, Neal Kohler, Jillian C. Mackey, Tim K. Department of Healthcare Research and Policy University of California San Diego - Extension United States Department of Anesthesiology University of California San Diego School of Medicine San Diego CA United States Global Health Policy Institute San Diego CA United States Department of Computer Science and Engineering University of California Jacobs School of Engineering San Diego United States WHO Collaborating Centre for Governance Transparency and Accountability in the Pharmaceutical Sector University of Toronto Ontario Canada Leslie Dan School of Pharmacy Dalla Lana School of Public Health and Munk School of Global Affairs University of Toronto ON Canada Division of Infectious Disease and Global Public Health University of California San Diego School of Medicine Department of Medicine San Diego CA United States

Background: Corruption is a significant challenge to the future of human development, economic progress, and population health in the post millennium. Corruption, in its different forms of bribery, fraud, waste, collusion, and illicit financial flows, not only leads to waste but can also erode trust in government and public systems. Corruption is also complex and globalized with different forms of corruption occurring across different countries and multiple industries. One critical tool to leverage in the fight against corruption is the use of innovative technologies such as machine learning. Methods: In this study, we deployed an unsupervised machine learning methodology using natural language processing to collect and analyze data from the popular social media platform Twitter with the aims of detecting self-reported experiences with corruption, including in the health sector. We collected data from the Twitter public API for keywords associated with corruption and used the biterm topic model to extract themes from the entire corpus of Tweets in order to detect user-generated messages reporting or discussing experiences with corruption. Results: We analyzed 22, 180, 425 tweets filtered for corruption-related keywords from January–May 2019. Using a combination of NLP and manual annotation, we detected 2383 tweets from 1556 users that included self-reporting of corruption for two dominant themes: police bribery and healthcare corruption. Overall, we found a small number of users actively reporting experiences with corruption, identified users located in countries that are perceived as having higher levels of corruption by their citizens, and found that the majority of messages included reports of users’ own experiences and/or documentation of corruption. Conclusion: Though technology is not a “silver bullet” that can entirely address the multifaceted nature of global corruption, this study demonstrates its potential utility as a force for good to enable better detec

关键词： Big data Bribery Corruption Healthcare Machine learning Natural language processing Sustainable development goals

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Design considerations for building credible security testbeds a systematic study of industrial control system use cases

arXiv

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arXiv 2019年

作者： Ani, Uchenna D. Watson, Jeremy M. Green, Benjamin Craggs, Barnaby Nurse, Jason Department of Science Technology Engineering and Public Policy University College London School of Computing and Communications Lancaster University United Kingdom Department of Computer Science University of Bristol United Kingdom School of Computing University of Kent United Kingdom

This paper presents a mapping framework for design factors and implementation process for building credible Industrial Control Systems (ICS) security testbeds. The resilience of ICSs has become a critical concern to operators and governments following widely publicised cyber security events. The inability to apply conventional Information Technology security practice to ICSs further compounds challenges in adequately securing critical systems. To overcome these challenges, and do so without impacting live environments, testbeds for the exploration, development and evaluation of security controls are widely used. However, how a testbed is designed and its attributes, can directly impact not only its viability but also its credibility as a whole. Through a combined systematic and thematic analysis and mapping of ICS security testbed design attributes, this paper suggests that the expertise of human experimenters, design objectives, the implementation approach, architectural coverage, core characteristics, and evaluation methods;are considerations that can help establish or enhance confidence, trustworthiness and acceptance;thus, credibility of ICS security testbeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Testbeds

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A structural haplotype in the 17q21.31 MAPT region is associated with increased risk for chronic traumatic encephalopathy endophenotypes

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Cell Reports Medicine 2025年第5期6卷 102084-102084页

作者： Han, Xudong Zhang, Yichi Petrosky, Jillian N. Bald, Sarah Sherva, Richard M. Labadorf, Adam Cherry, Jonathan D. Chung, Jaeyoon Farrell, Kurt Abdolmohammadi, Bobak Durape, Shruti Martin, Brett M. Palmisano, Joseph N. Farrell, John J. Alvarez, Victor E. Huber, Bertrand R. Dwyer, Brigid Daneshvar, Daniel H. Jun, Gyungah R. Lunetta, Kathryn L. Goldstein, Lee E. Katz, Douglas I. Cantu, Robert C. Shenton, Martha E. Cummings, Jeffrey L. Reiman, Eric M. Stern, Robert A. Alosco, Michael L. Tripodis, Yorghos Farrer, Lindsay A. Stein, Thor D. Crary, John F. McKee, Ann C. Mez, Jesse Bioinformatics Graduate Program Boston University Boston MA United States Section of Biomedical Genetics Department of Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston University Alzheimer's Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston VA Healthcare System Boston MA United States Boston University Chronic Traumatic Encephalopathy Center Boston University Boston MA United States Department of Pathology and Laboratory Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Pathology Neuroscience and Artificial Intelligence & Human Health Neuropathology Brain Bank & Research CoRE Friedman Brain Institute Ronald M. Loeb Center for Alzheimer's Disease Icahn School of Medicine at Mount Sinai New York NY United States Biostatistics and Epidemiology Data Analytics Center Boston University School of Public Health Boston MA United States Bedford VA Healthcare System Bedford MA United States Braintree Rehabilitation Hospital Braintree MA United States Department of Physical Medicine and Rehabilitation Harvard Medical School Boston MA United States Department of Rehabilitation and Human Performance Brain Injury Research Center Icahn School of Medicine at Mount Sinai New York NY United States Department of Neurology Icahn School of Medicine at Mount Sinai New York NY United States Department of Biostatistics Boston University School of Public Health Boston MA United States Department of Ophthalmology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Biomedical Electrical & Computer Engineering Boston University College of Engineering Boston MA United States Departments of Radiology and Psychiatry Boston University Chobanian & Avedisia

Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impact (RHI) exposure. Genetic variation in the 17q21.31 region, containing microtubule-associated protein tau (MAPT), has been implicated in tauopathies but has not been investigated in CTE. The region includes a megabase-long inversion (H1/H2) and copy-number variations, including α, β, and γ segments, which can be characterized as nine segregating structural haplotypes. We leveraged array SNP data and a reference panel across the 17q21.31 region to impute structural haplotypes and test their association with CTE endophenotypes in 447 European ancestry brain donors with RHI exposure. The H1β1γ1 haplotype was significantly associated with dementia and semi-quantitative tau burden in multiple cortical and medial temporal regions commonly affected in CTE. H1β1γ1 differential expression analyses in dorsolateral frontal cortex implicated cis-acting genes and inflammatory pathways. Taken together, the H1β1γ1 haplotype may help explain CTE heterogeneity among those with similar RHI exposure. © 2025 The Authors

关键词： 17q21.31 chronic traumatic encephalopathy dementia differential gene expression immune system MAPT repetitive head impacts structural haplotypes tauopathy traumatic brain injury

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Event detection in micro-PMU data: A generative adversarial network scoring method

arXiv

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arXiv 2019年

作者： Aligholian, Armin Shahsavari, Alireza Cortez, Ed Stewart, Emma Mohsenian-Rad, Hamed Department of Electrical and Computer Engineering University of California RiversideCA United States Energy Delivery Engineering Group Riverside Public Utilities RiversideCA United States Defense Infrastructure Group Lawrence Livermore National Laboratory LivermoreCA United States

A new data-driven method is proposed to detect events in the data streams from distribution-level phasor measurement units, a.k.a., micro-PMUs. The proposed method is developed by constructing unsupervised deep learning anomaly detection models;thus, providing event detection algorithms that require no or minimal human knowledge. First, we develop the core components of our approach based on a Generative Adversarial Network (GAN) model. We refer to this method as the basic method. It uses the same features that are often used in the literature to detect events in micro-PMU data. Next, we propose a second method, which we refer to as the enhanced method, which is enforced with additional feature analysis. Both methods can detect point signatures on single features and also group signatures on multiple features. This capability can address the unbalanced nature of power distribution circuits. The proposed methods are evaluated using real-world micro-PMU data. We show that both methods highly outperform a state-of-the-art statistical method in terms of the event detection accuracy. The enhanced method also outperforms the basic method. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Scoping review of the potential health effects of exposure to extremely low-frequency electric and magnetic fields

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Critical Reviews in Biomedical engineering 2019年第4期47卷 323-347页

作者： Habash, Mara Gogna, Priyanka Krewski, Daniel Habash, Riadh Department of Public Health Sciences Queen’s University 62 Fifth Field Company Lane KingstonONK7L3N6 Canada McLaughlin Centre for Population Health Risk Assessment University of Ottawa 600 Peter Morand Crescent OttawaONK1G 5Z3 Canada School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Avenue OttawaONK1N 6N5 Canada

Previous studies suggest that extremely low-frequency (ELF) electric and magnetic fields (EMFs) may impact human health. However, epidemiologic studies have provided inconsistent results on the association between exposure to ELF EMFs and various health outcomes. This scoping review reports on primary investigations that were published during the ten-year period of 2007-2017 on the association between ELF EMFs and cancer, cardiovascular disease (CVD), reproductive health effects, and neurodegenerative diseases. We identified a total of 361 articles from two bibliographic databases (PubMed and EMBASE). Of these, 39 articles (19 cancer studies, two CVD studies, nine reproductive health studies, and ten neurodegenerative disease studies [with one repeated for two outcomes]) met inclusion criteria. Articles identified in this study focus on three different types of exposure: occupational (22 studies), residential (15 studies), and electric blanket (two studies). This review suggests that ELF EMFs may be associated with neurodegenerative diseases, specifically Alzheimer’s disease;however, limited evidence was found to suggest that ELF EMFs are associated with several types of cancer, CVD, and reproductive outcomes. Additional epidemiological studies in large study populations with improved exposure assessments are needed to clarify current inconclusive relationships. © 2019 by Begell House, Inc.

关键词： Neurodegenerative diseases

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Author Correction: Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations

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NPJ digital medicine 2024年第1期7卷 220页

作者： Jane J Huang Roomasa Channa Risa M Wolf Yiwen Dong Mavis Liang Jiangxia Wang Michael D Abramoff T Y Alvin Liu Wilmer Eye Institute Johns Hopkins University School of Medicine Baltimore MD USA. University of Wisconsin-Madison School of Medicine and Public Health Madison WI USA. Johns Hopkins Pediatric Diabetes Center Johns Hopkins University School of Medicine Baltimore MD USA. Department of Biostatistics Johns Hopkins University Baltimore MD USA. Department of Ophthalmology and Visual Sciences University of Iowa Iowa City IA USA. Department of Electrical and Computer Engineering University of Iowa Iowa City IA USA. Wilmer Eye Institute Johns Hopkins University School of Medicine Baltimore MD USA. tliu25@jhmi.edu.

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Practical posterior error bounds from variational objectives

arXiv

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arXiv 2019年

作者： Huggins, Jonathan H. Kasprzak, Mikolaj Campbell, Trevor Broderick, Tamara Department of Biostatistics Harvard T. H. Chan School of Public Health Mathematics Research Unit University of Luxembourg Department of Statistics University of British Columbia Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology

Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the widespread use of variational methods is the lack of post-hoc accuracy measures that are both theoretically justified and computationally efficient. In this paper, we provide rigorous bounds on the error of posterior mean and uncertainty estimates that arise from full-distribution approximations, as in variational inference. Our bounds are widely applicable as they require only that the approximating and exact posteriors have polynomial moments. Our bounds are computationally efficient for variational inference in that they require only standard values from variational objectives, straightforward analytic calculations, and simple Monte Carlo estimates. We show that our analysis naturally leads to a new and improved workflow for variational inference. Finally, we demonstrate the utility of our proposed workflow and error bounds on a real-data example with a widely used multilevel hierarchical model. Copyright © 2019, The Authors. All rights reserved.

关键词： Computational efficiency

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Development of a smartphone-based app for determination of formaldehyde concentration indoors 15

Development of a smartphone-based app for determination of f...

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15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018

作者： Zhang, Siyang Shapiro, Nicholas Gehrke, Gretchen Castner, Jessica Liu, Zhenlei Guo, Beverly Zhang, Jianshun Haines, Sarah Kormos, David Frey, Paige Qin, Rongjun Dannemiller, Karen C. Department of Computer Science and Engineering Ohio State University United States Public Laboratory for Open Technology and Science Department of Mechanical and Aerospace Engineering Syracuse University Environmental Science Graduate Program Ohio State University United States Department of Civil Environmental and Geodetic Engineering College of Engineering Ohio State University United States Strand Associates Incorporated Department of Electrical and Computer Engineering Ohio State University United States Department of Environmental Health Sciences College of Public Health Ohio State University United States

ISBN: (纸本)9781713826514

Formaldehyde is a ubiquitous contaminant in the indoor environment that is both a carcinogen and irritant. The goal of this project is to develop a smartphone-based app for use by citizen scientists or researchers that can be paired with low-cost colorimetric badges for detection of indoor formaldehyde. We conducted both national and geographically-targeted surveys prior to app design. We have subsequently developed the app and conducted a beta test. Surveys of 147 individuals indicated broad public interest in formaldehyde testing using smartphone technology. Testing of the badges indicated consistent change in illumination with increasing formaldehyde exposure. Beta testing indicated users were able to navigate the existing app, and highlighted areas for improvement including incorporation of public surveys. Planned future analysis includes extensive calibration testing and a community-based field test. © 2018 15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018. All rights reserved.

关键词： Formaldehyde

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