Relevant bibliographies by topics / Health Data Analytics

Contents

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

Academic literature on the topic 'Health Data Analytics'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Health Data Analytics"

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Nayak, Mandar. "Managing Mental Health Using Data Analytics." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 1009–12. http://dx.doi.org/10.21275/sr241013021121.

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Bourne, Shawna, and Tarun Rihal. "Big Data Analytics." University of Western Ontario Medical Journal 87, no. 2 (2019): 24–26. http://dx.doi.org/10.5206/uwomj.v87i2.1149.

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Utilizing big data to guide decision-making for environmental health outcomes can provide the next level of health outcome improvements on a population basis.
 Historical shifts in overall health and longevity came with environmental health interventions such as safe food and water supplies, the treatment of waste and the establishment of standards that have reduced acute illnesses in the population.
 Big data analysis approaches have the potential to have a similar impact on quality and length of life by analyzing the factors leading to chronic illness in the population, and improvi
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Sukumar, Sreenivas R., Ramachandran Natarajan, and Regina K. Ferrell. "Quality of Big Data in health care." International Journal of Health Care Quality Assurance 28, no. 6 (2015): 621–34. http://dx.doi.org/10.1108/ijhcqa-07-2014-0080.

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Purpose – The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. Design/methodology/approach – The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of h
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Boman, Magnus, and Pedro Sanches. "Sensemaking in Intelligent Health Data Analytics." KI - Künstliche Intelligenz 29, no. 2 (2015): 143–52. http://dx.doi.org/10.1007/s13218-015-0349-0.

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Hiller, Janine S. "Healthy Predictions? Questions for Data Analytics in Health Care." American Business Law Journal 53, no. 2 (2016): 251–314. http://dx.doi.org/10.1111/ablj.12078.

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Dost, Muhammad Khan. "Data Streaming of Healthcare from Internet of Things (IoTs) using Big Data Analytics." Global Social Sciences Review 4, no. 1 (2019): 287–95. https://doi.org/10.5281/zenodo.4362047.

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The present study aims at the concept of the IoTs (IoT) and its relation with the healthcare sector. Nowadays, IoT is the main focus of researchers and scientists while this concept illustrates the data stream generated from IoT devices in massive amounts like big data with a continuous stream that requires its proper handling. This study aims at the analytical processing of big datasets having a medical history of patients and their diseases. The data cleansing is applied before going through the analytics phase due to the existence of some noisy and missing data. The analytics of data identi
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Nasibah, Husna Mohd Kadir, and Aliman Sharifah. "Text analysis on health product reviews using r approach." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 3 (2020): 1303–10. https://doi.org/10.11591/ijeecs.v18.i3.pp1303-1310.

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In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. I
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Ranjan, Rajeev, and Shreya Bhargav. "Changing Health-Related Behaviour Leveraging Data Analytics." European Journal of Medicine and Natural Sciences 3, no. 2 (2020): 91. http://dx.doi.org/10.26417/179kke60g.

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This paper accentuates Data Analytics as a broad term that encapsulates the issues in processing of a large structured and unstructured data in clinical healthcare. The paper explores the potential of Data Analytics in Changing Health Behavior (CHB), especially amongst the youth with regard to biomedical and well-being informatics, which plots the essential attributes of Big Data and delineates the relations among restorative and well-being informatics. The translational bio-informatics, sensor informatics and clinical diagnosis help a person in effective management of their wellness and prosp
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Ramesh, T., and V. Santhi. "Exploring big data analytics in health care." International Journal of Intelligent Networks 1 (2020): 135–40. http://dx.doi.org/10.1016/j.ijin.2020.11.003.

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Harerimana, Gaspard, Beakcheol Jang, Jong Wook Kim, and Hung Kook Park. "Health Big Data Analytics: A Technology Survey." IEEE Access 6 (2018): 65661–78. http://dx.doi.org/10.1109/access.2018.2878254.

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Dissertations / Theses on the topic "Health Data Analytics"

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Liu, Xiao, and Xiao Liu. "Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/620913.

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Pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug events (WHO 2004). Post-approval adverse drug events are a major health concern. They attribute to about 700,000 emergency department visits, 120,000 hospitalizations, and $75 billion in medical costs annually (Yang et al. 2014). However, certain adverse drug events are preventable if detected early. Timely and accurate pharmacovigilance in the post-approval period is an urgent goal of the public health system. The availability of various sources of h
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Killada, Parimala. "Data Analytics using Regression Models for Health Insurance Market place Data." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1501721348961437.

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Santhanagopalan, Meena. "Biopsychosocial Data Analytics and Modeling." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177435.

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Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement
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Li, Yuanxu Li. "HealthyLifeData Analytics: A DATA ANALYTICS TOOL FOR THE HealthyLifeHRA HEALTH RISK ASSESSMENT SYSTEM." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1465510000.

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Komolafe, Tomilayo A. "Data Analytics for Statistical Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87468.

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The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing oper
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Diallo, Ousmane Nasr. "A data analytics approach to gas turbine prognostics and health management." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/42845.

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As a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equi
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Mahmud, S. "Cloud enabled data analytics and visualization framework for health-shock prediction." Thesis, Coventry University, 2016. http://curve.coventry.ac.uk/open/items/deba667c-5142-4330-9fd0-c86db4a8c088/1.

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Health-shock can be defined as a health event that causes severe hardship to the household because of the financial burden for healthcare payments and the income loss due to inability to work. It is one of the most prevalent shocks faced by the people of underdeveloped and developing countries. In Pakistan especially, policy makers and healthcare sector face an uphill battle in dealing with health-shock due to the lack of a publicly available dataset and an effective data analytics approach. In order to address this problem, this thesis presents a data analytics and visualization framework for
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de, Battista Nicholas. "Wireless technology and data analytics for structural health monitoring of civil infrastructure." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/6161/.

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The aim of this research was to investigate how wireless technology, combined with data analytics, can be used for effective structural health monitoring (SHM) of civil infrastructure. Two main applications were investigated, for which wireless sensor networks (WSNs) were integrated into complete SHM solutions: (1) long-term quasi-static monitoring on a suspension bridge, and (2) temporary monitoring of pedestrian bridge vibration. In the first application, a commercial off-the-shelf WSN was used to acquire and transmit data from extensometers measuring the longitudinal deck displacement of th
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Leis, Machín Angela 1974. "Studying depression through big data analytics on Twitter." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671365.

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Mental disorders have become a major concern in public health, since they are one of the main causes of the overall disease burden worldwide. Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Language is one of the main tools on which mental health professionals base their understanding of human beings and their feelings, as it provides essential information for diagnosing and monitoring patients suffering from mental disorders. In parallel, social media platforms such as Twitter, allow us to observe the activity, though
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Peng, Zhen. "Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/89064.

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This thesis focuses on developing novel data analytics and damage detection methods that are applicable to the condition assessment of civil engineering structures subjected to operational and environmental condition changes, nonlinearity and/or measurement noise. Comprehensive numerical and experimental studies validate the effectiveness and performance of using the proposed approaches for practical structural health monitoring applications.
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Books on the topic "Health Data Analytics"

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Giabbanelli, Philippe J., Vijay K. Mago, and Elpiniki I. Papageorgiou, eds. Advanced Data Analytics in Health. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77911-9.

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Wen, Tzai-Hung, Ting-Wu Chuang, and Mathuros Tipayamongkholgul, eds. Earth Data Analytics for Planetary Health. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8765-6.

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Wang, Baoying, Ruowang Li, and W. Perrizo. Big data analytics in bioinformatics and healthcare. Medical Information Science Reference, 2015.

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A, McGlynn Elizabeth, ed. Health information systems: Design issues and analytic applications. Rand, 1998.

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Advanced Data Analytics in Health. Springer, 2019.

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Big Data and Health Analytics. Auerbach Publishers, Incorporated, 2014.

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Papageorgiou, Elpiniki I., Philippe J. Giabbanelli, and Vijay K. Mago. Advanced Data Analytics in Health. Springer, 2018.

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Marconi, Katherine, and Harold Lehmann. Big Data and Health Analytics. Taylor & Francis Group, 2014.

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Statistics and Data Analytics for Health Data Management. Elsevier - Health Sciences Division, 2016.

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Ahima. Introduction to Health Data Analytics Toolkit. American Health Information Management Association, 2022.

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Book chapters on the topic "Health Data Analytics"

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Ahmed, Mohiuddin, and Abu Saleh Shah Mohammad Barkat Ullah. "Health Care Security Analytics." In Data Analytics. CRC Press, 2018. http://dx.doi.org/10.1201/9780429446177-16.

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Bansal, Arvind Kumar, Javed Iqbal Khan, and S. Kaisar Alam. "Clinical Data Analytics." In Introduction to Computational Health Informatics. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781003003564-8.

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Bhalla, Subhash, Shelly Sachdeva, and Shivani Batra. "Semantic Interoperability in Electronic Health Record Databases: Standards, Architecture and e-Health Systems." In Big Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72413-3_16.

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Boland, Mary Regina. "Clinical Data and Health Data Types." In Health Analytics with R. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-74383-2_7.

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Talukder, Asoke K. "Big Data Analytics Advances in Health Intelligence, Public Health, and Evidence-Based Precision Medicine." In Big Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72413-3_17.

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Ozkaynak, Mustafa, and Diane Skiba. "Data Analytics, Artificial Intelligence and Data Visualization." In Health Informatics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91237-6_19.

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Gray, Thomas R. "Medical Liability Insurance Data Analytics." In Health Informatics. Productivity Press, 2022. http://dx.doi.org/10.4324/9780429423109-26.

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Batra, Shivani, and Shelly Sachdeva. "Managing Large-Scale Standardized Electronic Health Records." In Big Data Analytics. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3628-3_11.

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Boland, Mary Regina. "Environmental Health Data Types for Health Analytics." In Health Analytics with R. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-74383-2_11.

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Cullen, Theresa, and Jean E. Garcia. "Data Mining, Data Analytics, and Bioinformatics." In Innovations in Global Mental Health. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-70134-9_141-1.

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Conference papers on the topic "Health Data Analytics"

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Barkhade, Jayesh, Sahil Jagtap, Gayatri Shahare, Kamlesh Kalbande, Pooja Yerunkar, and Prasheel Thakre. "AI BOT: Mental Health Detection and Counselling." In 2024 International Conference on Big Data Analytics in Bioinformatics (DABCon). IEEE, 2024. https://doi.org/10.1109/dabcon63472.2024.10919413.

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Celin, J. Jeya A., Abhinaya Nakirikanti, Vadlamudi Pavani, Sravani Namburu, and Anamika. "Health Guard: Patient Records and Wellness Detection." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961097.

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Murase, Senju, and Jarutas Andritsch. "Integrating Sentiment Analysis to Enhance Mental Health Support Chatbots." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867341.

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S, Nivishna, Sneha R, Sunmathi S, and Yakshini M. "Health Centric Driver Safety and Vehicle Speed Optimization System." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961539.

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Kleitou, Georgios, and Jarutas Andritsch. "Predicting Health Care Facility Stay Duration: A Machine Learning Approach." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867387.

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Mohanraj, S., R. Ramesh Krishna, M. Shan Adams, and C. Nallusamy. "Machine Learning for Mental Health Prediction From Social Media Activity." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961560.

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Raymond, David Angira, Praveen Kumar, and Palash Goureshettiwar. "Intergration of Wearable Biosensors and Data Analytics for Remote Health Monitoring." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696310.

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Marripudugala, Manideep. "Healthcare Outcome Data Analytics through Electronic Health Records (EHR) and LLMs." In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2025. https://doi.org/10.1109/iciss63372.2025.11076450.

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Sivagurunathan, P. T., Keerthana S. P, Lakshana K, and Madhusri J. "Low Power Wearable ECG and Health Monitoring System for Multiple-Patient." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961340.

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Kavitha, A., Aarthy A, Dhanusha R, Dhanusri R. P, and Indhuja V. "Internet of Things (IoT) Based Wearable Device for Monitoring Women Health." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10960811.

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Reports on the topic "Health Data Analytics"

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Bagchi, Devarati, Vidya Prabhu, and Divya Sussana Patil. AI and big data analytics in health care: Opportunity or threat? World Evidence-based Healthcare Day, 2024. http://dx.doi.org/10.70253/vnhr9587.

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Adams, Jonathan, Gaurav Agnihotri, David Pendlebury, and Ed White. Research impact in society and the economy: The digital health revolution in medical care. Clarivate, 2024. http://dx.doi.org/10.14322/isi.grr.research.impact.in.society.

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This Global Research Report from the Institute for Scientific Information (ISI) examines the transformative impact of digital health on society and the global economy. Leveraging insights from our collaboration with Times Higher Education at the Digital Health 2024 event at Stanford University, the report integrates research and patent data to highlight the societal benefits of digital health innovations. Key findings include a 70-fold increase in digital health publications since 2013, with AI and advanced analytics driving innovation. The report identifies leading contributors to digital hea
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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business
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Sett, Dominic, Florian Waldschmidt, Alvaro Rojas-Ferreira, et al. Climate and disaster risk analytics tool for adaptive social protection. United Nations University - Institute for Environment and Human Security, 2022. http://dx.doi.org/10.53324/wnsg2302.

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Adaptive Social Protection (ASP) as discussed in this report is an approach to enhance the well-being of communities at risk. As an integrated approach, ASP builds on the interface of Disaster Risk Management (DRM), Climate Change Adaptation (CCA) and Social Protection (SP) to address interconnected risks by building resilience, thereby overcoming the shortcomings of traditionally sectoral approaches. The design of meaningful ASP measures needs to be informed by specific information on risk, risk drivers and impacts on communities at risk. In contrast, a limited understanding of risk and its d
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Collins, Kimberly, Raffi Der Wartanian, Francisca Beer, and Yunfei Hou. Moving Towards the Electrification of Medium- and Heavy-Duty Vehicles in the Inland Empire. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2305.1.

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This report investigates the transition to zero-emission medium- and heavy-duty vehicles (MDHD) within California’s Inland Empire (IE), emphasizing the significance of electric vehicle charging infrastructure and opportunity charging strategies in facilitating sustainable transportation. Utilizing a mixed-methods approach that combines a systematic literature review, geospatial and big data analytics, and thematic analysis of expert interviews, the study explores the multifaceted challenges and opportunities of electrification. The literature review assesses policies at various governance leve
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Zhao, Hui Yan, Sungha Kim, ChangSop Yang, and Mi Ju Son. Comparing acupoint catgut embedding and acupuncture therapies in simple obesity: Protocol for a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.10.0014.

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Review question / Objective: How effective are acupoint catgut embedding therapy sole or with other treatment? Condition being studied: Simple obesity. Information sources: We will search for trials from the following electronic databases: MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), and the Cumulative index to Nursing and Allied Health Literature (CINAHL). Trials will also be searched from three Korean medical databases (Oriental Medicine Advanced Searching Integrated System [OASIS], Science-On and KoreaMed), a Chinese database (Chinese National Knowledge Inf
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Zhao, Hui Yan, Sungha Kim, ChangSop Yang, and Mi Ju Son. Comparing acupoint catgut embedding and acupuncture therapies in simple obesity: Protocol for a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.10.0014.

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Review question / Objective: How effective are acupoint catgut embedding therapy sole or with other treatment? Condition being studied: Simple obesity. Information sources: We will search for trials from the following electronic databases: MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), and the Cumulative index to Nursing and Allied Health Literature (CINAHL). Trials will also be searched from three Korean medical databases (Oriental Medicine Advanced Searching Integrated System [OASIS], Science-On and KoreaMed), a Chinese database (Chinese National Knowledge Inf
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Gautier, M. A. Health and environmental chemistry: analytical techniques, data management, and quality assurance. Volume 1. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/5107848.

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Gautier, M. A. Health and environmental chemistry: Analytical techniques, data management, and quality assurance. Volume 1, Manual. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10136159.

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Svynarenko, Radion, Theresa L. Profant, and Lisa C. Lindley. Effectiveness of concurrent care to improve pediatric and family outcomes at the end of life: An analytic codebook. Pediatric End-of-Life (PedEOL) Care Research Group, College of Nursing, University of Tennessee, Knoxville, 2022. http://dx.doi.org/10.7290/m5fbbq.

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Implementation of the section 2302 of the 2010 Patient Protection and Affordable Care Act (ACA) enabled children enrolled in Medicaid/Children's Health Insurance Program with a prognosis of 6 months to live to use hospice care while continuing treatment for their terminal illness. Although concurrent hospice care became available more than a decade ago, little is known about the socio-demographic and health characteristics of children who received concurrent care; health care services they received while enrolled in concurrent care, their continuity, management, intensity, fragmentation; and t
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