Relevant bibliographies by topics / Azure Health Data Services

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Azure Health Data Services'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Azure Health Data Services"

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Praveen, Borra. "Impact and Innovations of Azure IoT: Current Applications, Services, and Future Directions." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 21–26. https://doi.org/10.35940/ijrte.B8111.13020724.

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<strong>Abstract:</strong> Azure IoT, developed by Microsoft, is a leading platform in the realm of Internet of Things (IoT), revolutionizing industries through enhanced connectivity, robust data management, and advanced analytics. This paper provides an in-depth exploration of Azure IoT's foundational technologies such as Azure IoT Hub and Azure Sphere, pivotal for ensuring secure device communication and comprehensive IoT security. Azure IoT Hub facilitates secure bidirectional communication between IoT devices and the cloud, supporting scalability to millions of devices across diverse secto
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Borra, Praveen. "Impact and Innovations of Azure IoT: Current Applications, Services, and Future Directions." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 21–26. http://dx.doi.org/10.35940/ijrte.b8111.13020724.

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Azure IoT, developed by Microsoft, is a leading platform in the realm of Internet of Things (IoT), revolutionizing industries through enhanced connectivity, robust data management, and advanced analytics. This paper provides an in-depth exploration of Azure IoT's foundational technologies such as Azure IoT Hub and Azure Sphere, pivotal for ensuring secure device communication and comprehensive IoT security. Azure IoT Hub facilitates secure bidirectional communication between IoT devices and the cloud, supporting scalability to millions of devices across diverse sectors. In manufacturing, it em
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Dheerendra, Yaganti. "Designing Privacy-First Health Monitoring Architectures Using Federated AI and FHIR-Compliant Azure Health Data Services In .NET." Journal of Scientific and Engineering Research 11, no. 8 (2024): 215–20. https://doi.org/10.5281/zenodo.15241066.

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The rapid advancement of digital health technologies necessitates architectures that ensure data privacy, regulatory compliance, and intelligent analytics. This paper presents a privacy-first health monitoring system that integrates Federated Artificial Intelligence (AI), Fast Healthcare Interoperability Resources (FHIR), and Azure Health Data Services within a .NET framework. The proposed architecture enables real-time health data collection and decentralized machine learning while preserving patient privacy and meeting HIPAA and FHIR compliance standards. By leveraging federated learning, th
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Venkata, Tadi. "Evaluating Advanced Azure Monitoring and Alerting Strategies in Diverse Large-Scale Enterprise Environments." Journal of Scientific and Engineering Research 9, no. 9 (2022): 94–107. https://doi.org/10.5281/zenodo.13338082.

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This study evaluates the performance and scalability of advanced monitoring and alerting strategies for data-intensive applications deployed on Microsoft Azure within diverse, large-scale enterprise environments. Leveraging key Azure services such as Azure Functions, Azure Data Factory (ADF) Pipelines, Kusto, Azure Service Bus, and Event Grid, the research aims to bridge the gap between theoretical best practices and real-world applications. By conducting a comprehensive analysis across various industry sectors, the study identifies the practical challenges, solutions, and adaptations necessar
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Ravi Kiran Pagidi, Jaswanth Alahari, Aravind Ayyagiri, Prof.(Dr) Punit Goel, Prof.(Dr.) Arpit Jain, and Er. Aman Shrivastav. "Best Practices for Implementing Continuous Streaming with Azure Databricks." Universal Research Reports 8, no. 4 (2021): 268–92. http://dx.doi.org/10.36676/urr.v8.i4.1428.

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Continuous data streaming is essential for modern applications that require real-time processing of large data sets. Azure Databricks, a scalable data analytics platform, is widely used to implement such streaming systems. This paper presents best practices for implementing continuous streaming with Azure Databricks, focusing on key aspects such as architecture design, data ingestion, and stream processing optimization. The integration of Apache Spark within Databricks enables efficient, fault-tolerant stream processing at scale, making it ideal for handling high-throughput data streams. Key c
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Eliwa, Entesar Hamed I., Amr Mohamed El Koshiry, Tarek Abd El-Hafeez, and Ahmed Omar. "Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure." Advances in Respiratory Medicine 92, no. 5 (2024): 395–420. http://dx.doi.org/10.3390/arm92050037.

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Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote consultation and classification of lung and colon cancer, leveraging blockchain technology and Microsoft Azure cloud services. Dataset and Features: The framework utilizes the LC25000 dataset, containing 25,000 histopathological images, for training a
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Urvangkumar, Kothari. "The Revolutionizing Dairy Industry Analytics through Real-Time Data Engineering." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 3 (2020): 1–8. https://doi.org/10.5281/zenodo.14945246.

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The dairy industry is experiencing substantial changes because of powerful real-time data engineering and cloud computing technologies. Dairy enterprises employ SQL and NoSQL databases together with Microsoft Azure services as well as Power BI and Microsoft BI tools which help improve efficiency and optimize production and maintain regulatory compliance. This paper examines data engineering functions across predictive maintenance operations while analyzing livestock health surveillance alongside supply chain management and quality control systems. The research investigates the influence of AI-
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Moret-Tatay, Carmen, Hernán Mario Radawski, and Cecilia Guariglia. "Health Professionals’ Experience Using an Azure Voice-Bot to Examine Cognitive Impairment (WAY2AGE)." Healthcare 10, no. 5 (2022): 783. http://dx.doi.org/10.3390/healthcare10050783.

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Virtual Assistants (VA) are a new groundbreaking tool for screening cognitive impairment by healthcare professionals. By providing the volume of data needed in healthcare guidance, better treatment monitoring and optimization of costs are expected. One of the first steps in the development of these items is the experience of the healthcare professionals in their use. The general goal of the current project, WAY2AGE, is to examine healthcare professionals’ experience in using an Azure voice-bot for screening cognitive impairment. In this way, back-end services, such as the ChatBot, Speech Servi
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Holilah, Holilah, Husyen Ali Alhabsy, Tb Muhammad Farhan Adnan, et al. "Aplikasi Kebugaran Defense In Depth Untuk Aplikasi Kebugaran Perusahaan Start-Up Menggunakan Microsoft Azure." Jurnal Ilmiah Sains dan Teknologi 9, no. 1 (2025): 10–19. https://doi.org/10.47080/saintek.v9i1.3867.

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A start-up company in the fitness and health sector has developed an innovative virtual training platform. The platform allows users to take live fitness classes via their computer, tablet or smartphone, leveraging artificial intelligence (AI) and high-quality video streaming for an interactive and personalized training experience. The platform consists of three main components: the frontend layer (web and mobile applications), the backend layer (application servers), and the data storage layer (cloud database). As the popularity of these platforms increases, companies face challenges in manag
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Wagholikar, Kavishwar B., Shreekanth V. Joshi, Vishal V. Pai Vernekar, et al. "Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform." BioMed Research International 2020 (July 8, 2020): 1–8. http://dx.doi.org/10.1155/2020/2851713.

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Despite the widespread use of the “Informatics for Integrating Biology and the Bedside” (i2b2) platform, there are substantial challenges for loading electronic health records (EHR) into i2b2 and for querying i2b2. We have previously presented a simplified framework for semantic abstraction of EHR records into i2b2. Building on our previous work, we have created a proof-of-concept implementation of cloud services on an i2b2 data store for cohort identification. Specifically, we have implemented a graphical user interface (GUI) that declares the key components for data import, transformation, a
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Dissertations / Theses on the topic "Azure Health Data Services"

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Järvelä, Andreas, and Sebastian Lindmark. "Evaluation and comparison of a RabbitMQ broker solution on Amazon Web Services and Microsoft Azure." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158242.

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In this thesis, a scalable, highly available and reactive RabbitMQ cluster is implemented on Amazon Web Services (AWS) and Microsoft Azure. An alternative solution was created on AWS using the CloudFormation service. These solutions are performance tested using the RabbitMQ PerfTest tool by simulating high loads with varied parameters. The test results are used to analyze the throughput and price-performance ratio for a chosen set of instances on the respective cloud platforms. How performance changes between instance family types and cloud platforms is tested and discussed. Additional conclus
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Sadikaj, Ylli. "Personalized Health Insurance Services Using Big Data." Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/27978.

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Cloud computing paradigm has significantly affected the healthcare sector like various other business domains. Persistently growing healthcare data over the Internet has called for the development of methodologies to efficiently handle the health big data. This study presents a framework that utilizes the cloud computing services to offer personalized recommendations about the most apposite health insurance plans. The users are offered implicit and explicit recommendations. A standard ontology is presented to offer a unified representation to the health insurance plans. The plans are ranked b
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Chitondo, Pepukayi David Junior. "Data policies for big health data and personal health data." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2479.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.<br>Health information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, eve
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Burns, Kara R. "Engagement, empowerment and patient generated health data." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/114076/1/Kara%20Burns%20Thesis.pdf.

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This thesis examines patient generated health data in the context of healthcare services where information asymmetry traditionally enshrined the doctor as all-powerful and the patient as a passive recipient. Through vignette-led interviews and a clinical trial, the research highlights the role of this information in the co-created process of patient engagement, then demonstrates how it promotes healthcare self-determination and consumer empowerment. Patient generated health data aids diagnosis and management of health conditions, and provides significant emotional value for patients and their
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Brewster, David H. "An assessment of the quality on cancer registration data in Scotland." Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274626.

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Channon, Andrew Richard. "Birth weight data in 15 demographic and health surveys." Thesis, University of Southampton, 2007. https://eprints.soton.ac.uk/378836/.

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Roberts, Julia Denise. "Senior student nurses data acquisition and care-planning skills : an exploratory study using simulation." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300316.

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Pack, Robert P., Angela Hagaman, S. Loyd, S. Livesay, and A. McAffrey. "Prescription Drug Abuse: The Present Situation & Local Data and Services." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etsu-works/1350.

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Chalmers, Kelsey. "Utilising Big Data in the search for low-value health care." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20574.

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Low-value health care provides little benefit relative to its cost. Australian policy makers, health care payers and providers want to reduce its use due to unnecessary costs and harms. These decisions, however, need to be informed by the measurement of low-value care in the context of Australia’s mixed public-private health care system. This thesis investigated low-value procedures using routinely collected data and direct measures. Direct measures use patient- or episode-level clinical information to distinguish low-value from appropriate care. Based on our review of the literature, we intr
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Chan, Jason. "The Burden of Biopsy-Proven Pediatric Celiac Disease in Ontario, Canada: Derivation of Health Administrative Data Algorithms and Determination of Health Services Utilization." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/34193.

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Introduction: The main objective of this thesis is to develop an algorithm to accurately identify cases of biopsy-proven Celiac Disease (CD) in children aged 6 months-14 years old from Ontario health administrative data. Method: CD cases diagnosed in 2005-2011 were identified from CHEO, and linked to the health administrative data to serve as reference for algorithms derivation. Algorithms based on outpatient physician visits for CD plus endoscopy billing code were constructed and tested. Results: The best algorithm selected based on performance from derivation study and clinical expertise c
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Books on the topic "Azure Health Data Services"

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Weissman, Ben, and Anthony E. Nocentino. Azure Arc-enabled Data Services Revealed. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8085-0.

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Weissman, Ben, and Anthony E. Nocentino. Azure Arc-Enabled Data Services Revealed. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6705-9.

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Washington (State). Dept. of Health. Division of Epidemiology and Health Statistics. Health Services Information System data plan. 4th ed. HSIS Office, 1994.

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Ommeren, Marijke van. Inventory of Australian health data collections. Australian Govt. Pub. Service, 1991.

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Sobolev, Boris, Adrian Levy, and Sarah Goring, eds. Data and Measures in Health Services Research. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7673-4.

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Institute of Medicine (U.S.). Committee on the Role of Institutional Review Boards in Health Services Research Data Privacy Protection. Protecting data privacy in health services research. National Academy Press, 2000.

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Center, Pennsylvania Health Data, ed. Guide to health data for Pennsylvania. State Health Data Center, 1986.

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Illinois. Dept. of Public Health. 1992-1994 human services plan: Part I, human services data report. Illinois Dept. of Public Health, 1994.

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Dana, Hughes, and Children's Defense Fund (U.S.), eds. The Health of America's children: Maternal and child health data book. Adolescent Pregancy Prevention, Prenatal Care Campaign, Children's Defense Fund, 1988.

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Whitman, Steven. Urban health: Combating disparities with local data. Oxford University Press, 2011.

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Book chapters on the topic "Azure Health Data Services"

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Soh, Julian, Marshall Copeland, Anthony Puca, and Micheleen Harris. "Azure Data Services." In Microsoft Azure. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5958-0_17.

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Soh, Julian, and Priyanshi Singh. "Azure AI Services." In Data Science Solutions on Azure. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0914-9_9.

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Mrozek, Dariusz. "Azure Cloud Services." In Scalable Big Data Analytics for Protein Bioinformatics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98839-9_3.

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Klein, Scott, and Herve Roggero. "SQL Azure Data Sync Services." In Pro Sql Azure. Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2962-9_11.

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Kumar, Vinodh. "Windows Azure Mobile Services." In Beginning Windows 8 Data Development. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-4993-1_9.

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Weissman, Ben, and Anthony E. Nocentino. "Azure Arc-enabled Data Services." In Azure Arc-enabled Data Services Revealed. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8085-0_2.

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Weissman, Ben, and Anthony E. Nocentino. "Azure Arc-Enabled Data Services." In Azure Arc-Enabled Data Services Revealed. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6705-9_2.

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Swinbank, Richard. "Tools and Other Services." In Azure Data Factory by Example. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0218-8_13.

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Feasel, Kevin. "Configuring Azure Cognitive Services Anomaly Detector." In Finding Ghosts in Your Data. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8870-2_18.

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Weissman, Ben, and Anthony E. Nocentino. "Deploying a Data Controller." In Azure Arc-Enabled Data Services Revealed. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6705-9_4.

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Conference papers on the topic "Azure Health Data Services"

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Samanta, Debajit, Subhodeep Roy, Pratima Sarkar, and Bitan Misra. "Extracting, Transforming, and Analyzing Olympic Data Using Azure Services: A Case Study." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743877.

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Wu, Peiping, Wangni Xu, and Xin Yu. "Proactive Platform for Active Health Services (AHS) Considering Data Securities." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933007.

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Bella Gracia, S. V. Juno, J. Godwin Ponsam, Hari Prasanth A, and Naveen Dravid Tr. "Integrating Real-Time Health Data with Emergency Services for Improved Outcomes." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780142.

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Khaled, Ahmed, and Muhammad Bangash. "Layered Data Pipeline for Context-Aware Electronic Health Records and Healthcare Services." In 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2024. https://doi.org/10.1109/healthcom60970.2024.10880711.

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Sawadsitang, Suttinee, Waranya Mahanan, and Dusit Niyato. "Optimal Data Privacy Protection: K-Anonymity Approach." In 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2024. https://doi.org/10.1109/healthcom60970.2024.10880733.

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Wang, Xueyi, Channah A. Bellink, C. J. C. Claudine Lamoth, and Elisabeth Wilhelm. "Multivariate Stress Forecast from Sparse Data during Lifestyle Interventions." In 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2024. https://doi.org/10.1109/healthcom60970.2024.10880832.

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Zhu, Yiwen, Yuanyuan Tian, Joyce Cahoon, et al. "Towards Building Autonomous Data Services on Azure." In SIGMOD/PODS '23: International Conference on Management of Data. ACM, 2023. http://dx.doi.org/10.1145/3555041.3589674.

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Verma, Ankita, Dhutima Malla, Amrit Kaur Choudhary, and Vasudha Arora. "A Detailed Study of Azure Platform & Its Cognitive Services." In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019. http://dx.doi.org/10.1109/comitcon.2019.8862178.

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Hansen, Pang, and Maeder. "HDI: integrated services for health data." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527926.

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Murray, Michael, Mario Macedo, and Carole Glynn. "Delivering Health Intelligence For Healthcare Services." In 2019 First International Conference on Digital Data Processing (DDP). IEEE, 2019. http://dx.doi.org/10.1109/ddp.2019.00026.

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Reports on the topic "Azure Health Data Services"

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Miall, Naomi, Gillian Fergie, and Anna Pearce. Health Inequalities in Scotland: trends in deaths, health and wellbeing, health behaviours, and health services since 2000. University of Glasgow, 2022. http://dx.doi.org/10.36399/gla.pubs.282637.

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Following a global pandemic and entering a cost-of-living crisis, concern around how health inequalities in Scotland have and will be impacted is considerable. This report synthesises a wide range of existing data and new analysis to establish the magnitude of the problem, where improvements or deterioration is evident and who is most affected. Over four detailed chapters, trends in social inequalities in health, health-related behaviours and, health and social care services in Scotland are presented.
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Schey, Steve, and Jim Francfort. AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for Department of Health and Human Services – ASPR. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1194015.

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Ainul, Sigma, Md Hossain, Md Hossain, et al. Trends in maternal health services in Bangladesh before, during and after COVID-19 lockdowns: Evidence from national routine service data. Population Council, 2020. http://dx.doi.org/10.31899/rh14.1037.

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Rahman, Kazi, Grace Lee, Kristina Vine, Amba-Rose Atkinson, Michael Tong, and Veronica Matthews. Impacts of climate change on health and health services in northern New South Wales: an Evidence Check rapid review. The Sax Institute, 2022. http://dx.doi.org/10.57022/xlsj7564.

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This rapid review investigated the effects of climate change on health and health services in northern NSW—a known ‘hotspot’ for natural disasters—over the next 10-20 years. It included 92 peer-reviewed articles and 9 grey literature documents, with 17% focused on Northern NSW. Climate change will cause both an increase in average temperatures and in extreme weather events and natural disasters. Impacts particularly affecting Northern NSW are expected to include increases and exacerbations of: mental illness; infectious diseases, including those transmitted by mosquitoes, water and food; heat-
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Jenkins, J. Lee, Enid Chung Roemer, Edbert B. Hsu, et al. Mental Health and Occupational Stress in the Emergency Medical Services and 911 Workforces. Agency for Healthcare Research and Quality (AHRQ), 2025. https://doi.org/10.23970/ahrqepcsr911.

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Objectives. This Systematic Review addresses the mental health of the Emergency Medical Services (EMS) and 911 telecommunicator workforces. We addressed Key Questions (KQs) related to the: (1) incidence, prevalence, and severity of mental health and occupational stress issues; (2) benefits and harms of interventions to promote resistance and resilience regarding these issues; (3) contextual and implementation factors for practices to address these issues; and (4) future research needs. Data sources. We searched Medline®, Embase®, Cochrane CENTRAL, PsycINFO®, CINAHL®, journals not indexed in Me
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Aguirre De Mora, Florencia, Benjamin Roseth, and Julieth Santamaria. Does Reluctance to Share Personal Data Reduce Citizen Demand for Personalized Services? Evidence from a Survey Experiment. Inter-American Development Bank, 2024. https://doi.org/10.18235/0012990.

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Two seemingly contradictory trends have accompanied the rise of digital transformation: a demand for better and more customized services, which require the use of personal data, and a concern for data protection. How do we reconcile these divergent trends? The answer to this question may influence not only the design of personalized services but also the strategies for their widespread adoption. This study explores how to mitigate the impact of citizens reluctance to share data on the uptake of personalized public services. Through a survey experiment, we offered two hypothetical personalized
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Santo, Loredana, Susan Schappert M., and Jill Ashman. Characteristics of Visits to Health Centers, 2020. National Center for Health Statistics (U.S.), 2022. http://dx.doi.org/10.15620/cdc:117898.

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This report examines health center visit rates by various characteristics, like age, sex, insurance status, reason for visit, and services, using data from the 2020 National Ambulatory Medical Care Survey—Community Health Centers.
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Mian, Anam, and Holly Gross. ARL Academic Health Sciences Library Statistics 2022. Association of Research Libraries, 2023. http://dx.doi.org/10.29242/hslstats.2022.

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This edition of the ARL Academic Health Sciences Library Statistics is a compilation of data that describes collections, expenditures, personnel, and services in medical libraries at ARL member institutions in the US and Canada in 2022.
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Lendon, Jessica, Priyanka Singh, and Zhaohui Lu. Adult Day Services Center Participant Characteristics: United States, 2022. National Center Health Statistics (U.S.), 2024. http://dx.doi.org/10.15620/cdc/157498.

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Singh, Priyanka, Jessica Lendon, and Manisha Sengupta. Alzheimer disease or other dementias in adult day services centers, 2020. National Center for Health Statistics (U.S.), 2024. http://dx.doi.org/10.15620/cdc:135024.

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