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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Zhang, Dongyu Hu, Ruofan Tao, Dandan Feng, Hao Rundensteiner, Elke Aml ByteDance San Jose United States Worcester Polytechnic Institute United States Worcester Polytechnic Institute Data Science Program Worcester United States Tsinghua University Vanke School of Public Health Beijing China North Carolina A&t State University College of Ag&Environ Sciences Greensboro United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350362480

Foodborne illnesses pose a threat to public health, leading to morbidity, mortality, and economic burden annually. Social media, while providing a rich timely source for training AI models for surveillance, requires effective tools for annotation. While Large Language Models (LLMs) have shown promise for generating simple labels, here hierarchical labels composed of entity types like food type and symptom (at individual word level) and the foodborne illness event (at complete post level) are required. For this, we introduce ICL2FID, the first LLM-based hierarchical labeling framework designed to annotate social media posts for foodborne illness detection at two levels using only a few demonstration examples. To utilize the interconnection between post and word levels, ICL2FID instructs the LLM to leverage information from one level when predicting the other level. To combat model hallucination and cyclic dependencies, a verification step improves evidence propagation between interconnected word and post-level labeling tasks. Strategies for custom selection of demonstration examples are designed reducing biases and increasing representation. We compare ICL2FID against traditional supervised learning and other LLM methods, demonstrating that it not only achieves superior accuracy but does so at a fraction of the cost and time. These findings highlight ICL2FID's potential as a viable alternative for hierarchical label generation in scenarios with limited resources and huge data sets. Code is available at https://***/zdy93/ICL2FID. © 2024 IEEE.

关键词： Demonstrations

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Hu, Ruofan Zhang, Dongyu Tao, Dandan Zhang, Huayi Feng, Hao Rundensteiner, Elke Worcester Polytechnic Institute Data Science Program Worcester United States Tsinghua University Vanke School of Public Health Beijing China ByteDance San Jose United States North Carolina A&T State University College of Ag&Environ Sciences Greensboro United States Worcester Polytechnic Institute Computer Science/Data Science Program Worcester United States

ISBN: (纸本)9798350324457

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts. © 2023 IEEE.

关键词： foodborne illness learning with noisy labels semi-supervised learning social media text classification

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast healthcare Interoperability Resources (FHIR) 9

E-Government Interoperability: Provincial-Level Architecture...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Wijayanti, Diana Urbaya, Satriawansyah Sitompul, Taufiq Adrian, Verry BINUS Graduate Program Bina Nusantara University Department of Information Systems Jakarta11480 Indonesia Program USAID Indonesia Jakarta12930 Indonesia University of Oslo Department of Informatics Oslo0316 Norway Binus Graduate Program - Doctor Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Provincial Health Office Center of Data and Information Technology Jakarta10160 Indonesia

ISBN: (纸本)9798350390025

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users. © 2024 IEEE.

关键词： e-government

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Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental health Assessment

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Biomedical Materials and Devices 2024年第2期2卷 778-810页

作者： Bryan, Anastasia C. Heinz, Michael V. Salzhauer, Abigail J. Price, George D. Tlachac, M.L. Jacobson, Nicholas C. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College Department of Psychiatry Geisel School of Medicine Dartmouth College Quantitative Biomedical Sciences Program Dartmouth College Department of Biomedical Data Science Geisel School of Medicine Dartmouth College Department of Information Systems and Analytics Bryant University Center for Health and Behavioral Sciences Bryant University

Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive and impairing, representing considerable challenges for both individual well-being and public health. Often the first challenges to treatment include financial, geographic, and stigmatic barriers, which limit the accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need for effective, accessible, and scalable approaches to identification and management. Considering advances in computing and the ubiquitous nature of personal mobile and wearable technology, this narrative review examines the utilization of passive sensor data as a screening and diagnostic tool for mental disorders. As an alternative to traditional screening measures, passive sensing offers a tool to overcome barriers that prevent many from seeking services. We critically assess the literature up to September 2023, exploring the use of passive data—such as heart rate variability, movement patterns, and geolocation—to predict mental health outcomes across a spectrum of disorders. From a translational perspective, our review explores the state of passive sensing science, with special emphasis on the capacity for the science to be implemented in real world clinical and general populations, a novelty specific to this review to the best of our knowledge. Toward this aim, we consider multiple study factors, including participant demographics, data collection methods, sensor modalities, outcome measures, and analytic modeling approaches. We find that passive sensing features, such as GPS, heart rate, and actigraphy offer promise for enhancing early detection and improving the diagnostic process for mental disorders. Despite this promise, however, our findings highlight important limitations in passive sensing research including (1) a trend toward smaller, specialized samples, (2) a predominance of data collection apps built on

关键词： Digital biomarkers Digital mental health Digital phenotyping Mental health assessment Passive sensing Ubiquitous sensing

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Detecting Longitudinal Trends between Passively Collected Phone Use and Anxiety among College Students

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Digital Biomarkers 2024年第1期8卷 181-193页

作者： Gyorda, Joseph A. Lekkas, Damien Jacobson, Nicholas C. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College LebanonNH United States Quantitative Biomedical Sciences Program Dartmouth College HanoverNH United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College LebanonNH United States Department of Psychiatry Geisel School of Medicine Dartmouth College HanoverNH United States

Introduction: Existing theories and empirical works link phone use with anxiety;however, most leverage subjective self-reports of phone use (e.g., validated questionnaires) that may not correspond well with true behavior. Moreover, most works linking phone use with anxiety do not interrogate associations within a temporal framework. Accordingly, the present study sought to investigate the utility of passively sensed phone use as a longitudinal predictor of anxiety symptomatology within a population particularly vulnerable to experiencing anxiety. Methods: Using data from the GLOBEM study, which continuously collected longitudinal behavioral data from a college cohort of N = 330 students, weekly PHQ-4 anxiety subscale scores across 3 years (2019-2021) were paired with median daily phone use records from the 2 weeks prior to anxiety self-report completion. Phone use was operationalized through unlock duration which was passively curated via Apple's "Screen Time"feature. GPStracked location data was further utilized to specify whether an individual's phone use was at home or away from home. Within-individual and temporal associations between phone use and anxiety were modeled within an ordinal mixed-effects logistic regression framework. Results: While there was no significant association between anxiety levels and either median total phone use or median phone use at home, participants in the top quartile of median phone use away from home were predicted to exhibit clinically significant anxiety levels 20% more frequently than participants in the bottom quartile during the first study year;however, this association weakened across successive years. Importantly, these associations remained after controlling for age, physical activity, sleep, and baseline anxiety levels and were not recapitulated when operationalizing phone use with unlock frequency. Conclusions: These findings suggest that phone use may be leveraged as a means of mitigating or coping with anxiety in soc

关键词： Students

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Innovation in Geriatric Care: An AI Assistant with LLM Integration Based on health Guidelines

Innovation in Geriatric Care: An AI Assistant with LLM Integ...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Basulo-Ribeiro, Juliana Matos, Nádia C. G. Araujo, Sabrina Magalhães Capela, Nuno Bischoff, Francisco Teixeira, Leonor Cruz-Correia, Ricardo University of Aveiro Aveiro Portugal Faculty of Medicine University of Porto Porto Portugal Faculty of Medicine of the University of Porto Health Data Science Ph.D. Program Porto Portugal CINTESIS - Center for Health Technology and Services Research Faculty of Medicine University of Porto Porto Portugal VirtualCare Portugal

With the advance of artificial intelligence and natural language processing technology, a new tool is standing out in the field of understanding and generating natural language in a sophisticated way: the Large Language Model (LLM). According to several authors, LLMs can be used for various types of medical cases, providing access to different sources of information, and have opened up countless opportunities in the healthcare sector. This work aims to share the lessons learned during the process of developing an LLM-based assistant aimed at specific pathologies that are more prevalent in the elderly in order to support caregivers, whether they are private individuals, home care organizations, nursing homes or others. This study has a significant potential impact on the community by providing access to detailed information on developing an LLM-based assistant. © 2025 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electronic health record

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GCN2 inhibition reduces mutant SOD1 clustering and toxicity and delays disease progression in an amyotrophic lateral sclerosis mouse model

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Translational Neurodegeneration 2024年第1期13卷 351-355页

作者： Didio Alberto Ortiz Nuria Peregrín Miguel Valencia Rodrigo Vinueza-Gavilanes Elisa Marín-Ordovas Roberto Ferrero María Jesús Nicolás Gloria González-Aseguinolaza Montserrat Arrasate Tomás Aragón DNA and RNA Medicine Program Center for Applied Medical Research(CIMA)University of NavarraPamplonaSpain Biomedical Engineering Program Center for Applied Medical Research(CIMA)University of NavarraPamplonaSpain Institute of Data Science and Artificial Intelligence(DATAI) University of NavarraPamplonaSpain Navarra Institute for Health Research(IdiSNA) PamplonaSpain Neuroimmunology Department Multiple Sclerosis Center of Catalonia(Cemcat)Vall d’Hebron Research Institute(VHIR)BarcelonaSpain School of Medicine University of NavarraPamplonaSpain Brain Lab Biobizkaia Health Research InstituteBarakaldoSpain Present Address:Ikerbasque Basque Foundation for ScienceBilbaoSpain

Main text The disruption of protein folding homeostasis in motoneurons(MNs)and the accumulation of protein aggregates are some of the main molecular hallmarks of amyotrophic lateral sclerosis(ALS).Evidence from sporadic and familial ALS(fALS)patients and from ALS models suggests that protein aggregation directly participates in *** turn,the loss of MN homeostasis triggers a coping mechanism,the integrated stress response(ISR)[1].The ISR is initiated by four independent stress-sensing kinases,each of them activated by distinct stresses:protein kinase R(PKR)by double-strand RNA,protein kinase RNA-like endoplasmic reticulum kinase(PERK)by protein misfolding at the endoplasmic reticulum(ER),general control nonderepressible 2(GCN2)by nutrient starvation,and heme-regulated inhibitor(HRI)by heme deprivation.

关键词： homeostasis amyotrophic sclerosis

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An ounce of prevention: using conversational interviewing and avoiding agreement response scales to prevent acquiescence

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Quality and Quantity 2024年第1期58卷 471-495页

作者： Davis, Rachel E. Conrad, Frederick G. Dong, Shaohua Mesa, Anna Lee, Sunghee Johnson, Timothy P. Arnold School of Public Health Department of Health Promotion Education and Behavior University of South Carolina Columbia SC United States Institute for Social Research Michigan Program in Survey and Data Science University of Michigan Ann Arbor MI United States College of Urban Planning and Public Affairs Department of Public Administration University of Illinois at Chicago Chicago IL United States

Acquiescent response style (ARS), the tendency for survey respondents to agree with survey items, is of particular concern for increasing measurement error in surveys with populations who are more likely to acquiesce, such as Latino respondents in the U.S. In order to develop methods for reducing ARS, this study addressed two questions: (1) Does administering a questionnaire using conversational interviewing (CI) yield less ARS than standardized interviewing (SI)? (2) Do bipolar disagree/agree (DA) response scales lead to higher ARS than unipolar response scales that do not assess agreement (non-AG)? A total of 891 Latino telephone survey respondents were screened for ARS and randomly assigned to four experimental groups determined by crossing interviewing technique (CI or SI) and response format (non-AG or DA): (1) SI/non-AG (n = 301);(2) SI/DA (n = 295);(3) CI/non-AG (n = 149);and (4) CI/DA (n = 146). CI yielded lower ARS than SI (p < 0.001), but there was no difference in ARS between DA and non-AG response scales. A subset of coded interview recordings indicated that the CI interviewers reduced ARS by clarifying questions even in the absence of evidence of respondent confusion and helping with response mapping. These results suggest that difficulty answering questions associated with cognitive decline and cultural norms may have prompted higher use of ARS, but that conversational interviewers were able to mitigate these difficulties and cultural tendencies. Findings from this study suggest that using CI to administer survey questions may decrease ARS and improve data quality among survey respondents who are more likely to engage in ARS. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Acquiescence Conversational interviewing Interviewing Latino Response styles Survey error Surveys

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dataset Comparison Tool: Utility and Privacy 32

Dataset Comparison Tool: Utility and Privacy

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32nd Medical Informatics Europe Conference, MIE 2022

作者： Coutinho-Almeida, João Cruz-Correia, Ricardo João Rodrigues, Pedro Pereira Cintesis-Centre for Health Technologies and Services Research University of Porto Portugal Medcids-Faculty of Medicine of University of Porto Portugal Health Data Science PhD Program Faculty of Medicine of the University of Porto Portugal

ISBN: (纸本)9781643682846

Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since there are different methods of evaluating these issues, which are dependent on data types, use cases and purpose, a generic method for evaluating utility and privacy does not exist at the moment. So, we introduced a compilation of the most recent methods for evaluating privacy and utility into a single executable in order to create a report of the similarities and potential privacy breaches between two datasets, whether it is related to synthetic or not. We catalogued 24 different methods, from qualitative to quantitative, column-wise or table-wise evaluations. We hope this resource can help scientists and industries get a better grasp of the synthetic data they have and produce more easily and a better basis to create a new, more broad method for evaluating dataset similarities. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Medical informatics

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Respiratory syncytial virus vaccine effectiveness among US veterans, September, 2023 to March, 2024: a target trial emulation study

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The Lancet Infectious Diseases 2025年第6期25卷 625-633页

作者： Bajema, Kristina L Yan, Lei Li, Yuli Argraves, Stephanie Rajeevan, Nallakkandi Fox, Alexandra Vergun, Robert Berry, Kristin Bui, David Huang, Yuan Lin, Hung-Mo Hynes, Denise M Lucero-Obusan, Cynthia Schirmer, Patricia Cunningham, Francesca Huang, Grant D Aslan, Mihaela Ioannou, George N Veterans Affairs Portland Health Care System Portland OR United States Division of Infectious Diseases Department of Medicine Oregon Health & Science University Portland OR United States Veterans Affairs Cooperative Studies Program Clinical Epidemiology Research Center Veterans Affairs Connecticut Health Care System West Haven CT United States Department of Biostatistics Yale School of Public Health New Haven CT United States Biomedical Informatics & Data Science Yale School of Medicine New Haven CT United States Department of Internal Medicine Yale School of Medicine New Haven CT United States Seattle Epidemiologic Research and Information Center Veterans Affairs Puget Sound Health Care System Seattle WA United States Research and Development Veterans Affairs Puget Sound Health Care System Seattle WA United States Center of Innovation to Improve Veteran Involvement in Care Veterans Affairs Portland Health Care System Portland OR United States Health Management and Policy College of Health Oregon State University Corvallis OR United States Health Data and Informatics Program Center for Quantitative Life Sciences Oregon State University Corvallis OR United States Public Health National Program Office Veterans Health Administration Palo Alto CA United States Public Health National Program Office Veterans Health Administration Washington DC United States Veterans Affairs Center for Medication Safety—Pharmacy Benefit Management Services Hines IL United States Office of Research and Development Veterans Health Administration Washington DC United States Division of Gastroenterology Veterans Affairs Puget Sound Health Care System and University of Washington Seattle WA United States

Background: New respiratory syncytial virus (RSV) vaccines have been approved in the USA for the prevention of RSV-associated lower respiratory tract disease in adults aged 60 years and older. Information on the real-world effectiveness of these vaccines is needed. Methods: We used electronic health records in the Veterans health Administration to emulate a target trial comparing a single dose of a recombinant stabilised prefusion F protein RSV vaccine versus no vaccination among veterans aged 60 years and older. We matched eligible vaccine recipients with up to four unvaccinated individuals in four monthly nested sequential trials from Sept 1 to Dec 31, 2023. Outcomes were ascertained up to March 31, 2024. The primary outcome was any positive RSV test from day 14 following the matched index date. Secondary outcomes included hospitalisation and emergency department or urgent care encounter occurring within 1 day before or after a positive RSV test. We estimated vaccine effectiveness as 100 × (1 – risk ratio). Findings: We included 146 852 vaccinated individuals matched to 582 936 unique control individuals, weighted equally to represent 146 852 individuals. Across the two groups, 276 039 (94·0%) of 293 704 veterans were male, 17 665 (6·0%) were female, and median age was 75·9 years (IQR 71·7–79·7). Over a median follow-up of 124 days (IQR 102–150), the incidence rate of documented RSV infection was 1·7 (95% CI 1·4–2·1) events per 1000 person-years (88 total events) in the vaccinated group and 7·3 (6·6–8·1) per 1000 person-years in the unvaccinated group (372 total events), and vaccine effectiveness was estimated as 78·1% (72·6–83·5). Among the secondary outcomes, vaccine effectiveness was estimated at 78·7% (72·2–84·8) against RSV-associated emergency department or urgent care encounters, and 80·3% (65·8–90·1) against RSV-associated hospitalisation. Interpretation: RSV vaccination was effective in preventing RSV-related illness, including associated health-care use,

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