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Journal articles on the topic 'Health Data Analytics'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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5

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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10

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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11

Nandish Shivaprasad. "Data Modelling for Health Insurance Claims Analytics." Kuwait Journal of Advanced Computer Technology 2, no. 1 (2024): 01–12. https://doi.org/10.52783/kjact.258.

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This paper aims at discussing the analysis of health insurance claim through risk classification, fraudulence and cost prediction models. Combined with state-of-art data preprocessing and modelling techniques, insurers can better drive decision, minimize fraud, and better plan for financials. Logistic regression, random forest, gradient boost and models of similar category help in pattern analysis and cost of claim forecasting. They further effectiveness, equity and customer relations for implementing sound insurance that is sustainable. This work therefore emphatically speaks to the Bar on th

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Parke, David W., and Anne L. Coleman. "New Data Sets in Population Health Analytics." JAMA Ophthalmology 137, no. 6 (2019): 640. http://dx.doi.org/10.1001/jamaophthalmol.2019.0410.

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13

J.Archenaa and Anita E.A.Mary. "Health Recommender System using Big data analytics." Journal of Management Science and Business Intelligence 2, no. 2 (2017): 17–24. https://doi.org/10.5281/zenodo.835606.

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This paper gives an insight on how to use big data analytics for developing effective health recommendation engine by analyzing multi structured healthcare data. Evidence-based medicine is a powerful tool to help minimize treatment variation and unexpected costs. Large amount of healthcare data such as Physician notes, medical history, medical prescription, lab and scan reports generated is useless until there is a proper method to process this data interactively in real-time. In this world filled with the latest technology, healthcare professionals feel more comfortable to utilize the social

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Garg, Dr Hemant. "Big Data And Healthcare Analytics." Journal of Science & Technology 02, no. 03 (2021): 01–14. http://dx.doi.org/10.55662/jst.2021.2301.

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This paper delves into the use of mobile-based or computer-based apps for reducing patient readmission rates in the American healthcare industry. Such interventions are required for improving population health, increasing patient satisfaction, and reducing costs per capita. The aim of the quadruple aim is to simultaneously achieve its three major goals that are mentioned above. It will also investigate the concept of big data from a generalized perspective before inspecting its application in health analytics and information management systems. One potentially effective approach to introducing

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Kamran Ul haq, Ayesha, Amira Khattak, Noreen Jamil, M. Asif Naeem, and Farhaan Mirza. "Data Analytics in Mental Healthcare." Scientific Programming 2020 (July 4, 2020): 1–9. http://dx.doi.org/10.1155/2020/2024160.

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Worldwide, about 700 million people are estimated to suffer from mental illnesses. In recent years, due to the extensive growth rate in mental disorders, it is essential to better understand the inadequate outcomes from mental health problems. Mental health research is challenging given the perceived limitations of ethical principles such as the protection of autonomy, consent, threat, and damage. In this survey, we aimed to investigate studies where big data approaches were used in mental illness and treatment. Firstly, different types of mental illness, for instance, bipolar disorder, depres

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Abidi, Syed Sibte Raza, and Samina Raza Abidi. "Intelligent health data analytics: A convergence of artificial intelligence and big data." Healthcare Management Forum 32, no. 4 (2019): 178–82. http://dx.doi.org/10.1177/0840470419846134.

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Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intel

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Adekunle, Oyeyemi Adenyi, Anthonia Okolo Chioma, Olorunsogo Tolulope, and Babawarun Oloruntoba. "Leveraging big data and analytics for enhanced public health decision-making: A global review." GSC Advanced Research and Reviews 18, no. 2 (2024): 450–56. https://doi.org/10.5281/zenodo.11216658.

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In recent years, the proliferation of big data and analytics technologies has revolutionized various sectors, including public health. This review presents a comprehensive review of how leveraging big data and analytics has enhanced public health decision-making on a global scale. The review encompasses diverse applications, methodologies, challenges, and opportunities within this burgeoning field. Big data analytics in public health encompasses the collection, processing, and analysis of vast datasets from heterogeneous sources, including electronic health records, social media, wearable devi

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Kumar Chinnathambi, Jinesh. "Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 1325–30. http://dx.doi.org/10.21275/sr24724191449.

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19

Vyvyenne Michelle Chigboh, Stephane Jean Christophe Zouo, and Jeremiah Olamijuwon. "Health data analytics for population health management: A review of best practices and challenges." International Journal of Frontiers in Medicine and Surgery Research 6, no. 2 (2024): 106–16. http://dx.doi.org/10.53294/ijfmsr.2024.6.2.0050.

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Health data analytics has become an indispensable tool for optimizing population health management by offering data-driven insights that improve healthcare outcomes, enhance preventive care, and inform policy-making. This review explores best practices in health data analytics, including predictive analytics, risk stratification, patient segmentation, and data integration. It highlights successful implementations in healthcare systems that demonstrate the positive impact of these practices on care coordination, resource allocation, and disease prevention. However, the paper also addresses seve

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Mansour, Ezzat Abdulaziz. "Big Data Analytics Changes in Health Care Industry." Tehnički glasnik 16, no. 2 (2022): 182–86. http://dx.doi.org/10.31803/tg-20220124132449.

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The present research is to find out the information, which are related with big data analytics in health care industry. It enhances the electronic health and electronic medical records in hospitals. Many medical services are experiencing a drastic change in maintaining the medical records in health care industry. Big data analytics is the primary factor that combines many bits of information together to focus on maintaining medical records in a systematic manner. A proper practice of documentation helps and assists the medical procedure easier. There is always a life cycle in maintaining medic

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Bychkov, Oleksii S., Oleksandr V. Gezerdava, Kseniia K. Dukhnovska, Oksana I. Kovtun, and Olga O. Leshchenko. "Fitness Tracker Data Analytics." Control Systems and Computers, no. 2 (306) (July 2024): 65–76. http://dx.doi.org/10.15407/csc.2024.02.065.

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The health status of patients is recorded in various sources, such as medical records, portable devices (smart watches, fitness trackers, etc.), forming a characteristic current health status of patients. The goal of the study was the development of medical card software for the analysis of data from fitness bracelets. This will provide an opportunity to collect data for further use of cluster analysis and improvement of the functionality and accuracy of medical monitoring. The object of the study is the use of linear regression to analyze and predict heart rate based on data collected using f

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Mehta, S., E. Hume, A. Troxel, et al. "DIGITAL TECHNOLOGIES, DATA, AND ANALYTICS." Health Services Research 55, S1 (2020): 40–41. http://dx.doi.org/10.1111/1475-6773.13381.

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Moskovitch, Robert, Yuval Shahar, Fei Wang, and George Hripcsak. "Temporal biomedical data analytics." Journal of Biomedical Informatics 90 (February 2019): 103092. http://dx.doi.org/10.1016/j.jbi.2018.12.006.

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24

Oliver, Nuria, Michael Marschollek, and Oscar Mayora. "Machine Learning and Data Analytics in Pervasive Health." Methods of Information in Medicine 57, no. 04 (2018): 194–96. http://dx.doi.org/10.1055/s-0038-1673243.

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Summary Introduction: This accompanying editorial provides a brief introduction to this focus theme, focused on “Machine Learning and Data Analytics in Pervasive Health”. Objective: The innovative use of machine learning technologies combining small and big data analytics will support a better provisioning of healthcare to citizens. This focus theme aims to present contributions at the crossroads of pervasive health technologies and data analytics as key enablers for achieving personalised medicine for diagnosis and treatment purposes. Methods: A call for paper was announced to all participant

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25

Nalubega Moses H.F., Mugo. "Wearable Health Devices and Data Analytics: Trends and Insights." RESEARCH INVENTION JOURNAL OF BIOLOGICAL AND APPLIED SCIENCES 5, no. 2 (2025): 29–32. https://doi.org/10.59298/rijbas/2025/522932.

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Wearable health devices have emerged as a transformative force in healthcare, bridging the gap between digital technology and personalized medicine. These devices enable users to monitor various health metrics, from heart rate and activity levels to sleep quality and stress, contributing to a proactive approach to health management. Concurrently, data analytics has advanced the utility of these devices by offering actionable insights, enhancing clinical decision-making, and driving personalized care. This paper examines the trends in wearable health technology, the role of data analytics in im

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Scherz, Paul. "Data Ethics, AI, and Accompaniment: The Dangers of Depersonalization in Catholic Health Care." Theological Studies 83, no. 2 (2022): 271–92. http://dx.doi.org/10.1177/00405639221096770.

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Health-care systems use AI-driven data analytics to target high-cost patients for early interventions. Many ethicists see these programs as enacting a preferential option for the poor. Ethnographic studies, however, find that their data analytic framework emphasizes efficiency, cost containment, and constant evaluation of patients. Ongoing evaluation and surveillance can undermine other goals of Catholic health care like personal encounter and accompaniment. While targeted care programs can be implemented well, the use of AI in data analytics to serve the poor creates dangers of depersonalizat

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Varshney, Monika. "DECREASE HEALTH ISSUES BY BIG DATA ANALYSIS." Journal of Science Innovations and Nature of Earth 3, no. 3 (2023): 09–12. http://dx.doi.org/10.59436/https://jsiane.com/archives3/3/93.

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The goal of "Swastha Bharat," or a healthy India, is to help every Indian realise his or her dream of living in an incredible India. Article 21 of the Constitution of India guarantees every citizen the right to health care. Health, as defined by the World Health Organisation (WHO), is not only the absence of disease but rather the whole mental, physical, and social flourishing of a person. New computer technologies have had a profound impact on the health industry, driving the generation of more medical data and spawning new subdisciplines of study. In order to make Swastha Bharat (Healthy Ind

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Mohammed, Sabah, Tai-Hoon Kim, Ruay-Shiung Chang, and Carlos Ramos. "Guest Editorial: Data Analytics for Public Health Care." IEEE Journal of Biomedical and Health Informatics 26, no. 4 (2022): 1409–10. http://dx.doi.org/10.1109/jbhi.2022.3159347.

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29

Dhar, Vasant. "Big Data and Predictive Analytics in Health Care." Big Data 2, no. 3 (2014): 113–16. http://dx.doi.org/10.1089/big.2014.1525.

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Witt, Daniel R., Ryan A. Kellogg, Michael P. Snyder, and Jessilyn Dunn. "Windows into human health through wearables data analytics." Current Opinion in Biomedical Engineering 9 (March 2019): 28–46. http://dx.doi.org/10.1016/j.cobme.2019.01.001.

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Suzuki, K. "New platform of data analytics for mental health." European Psychiatry 33, S1 (2016): S33. http://dx.doi.org/10.1016/j.eurpsy.2016.01.863.

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IntroductionMental disorder is a key public health challenge and a leading cause of disability-adjusted life years (DALYs) due to its high level of disability and mortality. Therefore, a slight improvement on mental care provision and management could generate solid benefits on relieving the social burden of mental diseases.ObjectivesThis paper presents a long-term vision of strategic collaboration between Fujitsu Laboratories, Fujitsu Spain, and Hospital Clinico San Carlos to generate value through predictive and preventive medicine improving healthcare outcomes for every clinical area, benef

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Berros, Nisrine, Fatna El Mendili, Youness Filaly, and Younes El Bouzekri El Idrissi. "Enhancing Digital Health Services with Big Data Analytics." Big Data and Cognitive Computing 7, no. 2 (2023): 64. http://dx.doi.org/10.3390/bdcc7020064.

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Medicine is constantly generating new imaging data, including data from basic research, clinical research, and epidemiology, from health administration and insurance organizations, public health services, and non-conventional data sources such as social media, Internet applications, etc. Healthcare professionals have gained from the integration of big data in many ways, including new tools for decision support, improved clinical research methodologies, treatment efficacy, and personalized care. Finally, there are significant advantages in saving resources and reallocating them to increase prod

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Raghupathi, Wullianallur, Viju Raghupathi, and Aditya Saharia. "Analyzing Health Data Breaches: A Visual Analytics Approach." AppliedMath 3, no. 1 (2023): 175–99. http://dx.doi.org/10.3390/appliedmath3010011.

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This research studies the occurrence of data breaches in healthcare provider settings regarding patient data. Using visual analytics and data visualization tools, we study the distribution of healthcare breaches by state. We review the main causes and types of breaches, as well as their impact on both providers and patients. The research shows a range of data breach victims. Network servers are the most popular location for common breaches, such as hacking and information technology (IT) incidents, unauthorized access, theft, loss, and improper disposal. We offer proactive recommendations to p

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Hemalatha, Sweetlin, and Apurva Waghmare. "PREDICTIVE ANALYTICS OF HEALTHCARE DATA." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 333. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19750.

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Predictive analytics is employed to improve the ability to take precautionary measures during medical emergencies. In health care, the sensor-baseddata are generated daily which can be used to predict future data using regression model. In this paper, pain dataset from integrating data for analysis,anonimyzation, and sharing repository is used for experimenting different machine algorithms. The results show that logistic regression gives moreaccuracy than other algorithms.

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Chinedu, James Ezeh, Chioma Anioke Sandra, Oyewole Sola, and Garnvwa David Munakur. "The role of predictive analytics in enhancing public health surveillance: Proactive and data-driven interventions." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3059–77. https://doi.org/10.5281/zenodo.15259551.

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Predictive analytics has transformed public health surveillance, shifting it from reactive to proactive care. Leveraging advanced mathematical tools, artificial intelligence (AI), and machine learning (ML) algorithms, healthcare systems now analyze data to detect patterns, predict outbreaks, and implement timely interventions. This study examines the role of predictive analytics in strengthening disease surveillance, prioritizing resources, and building effective early warning systems. Using qualitative assessments of implemented systems in various healthcare organizations, data was synthesize

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Abdulraheem, Olaide Babarinde, Ayo-Farai Oluwatoyin, Paschal Maduka Chinedu, Chinaemelum Okongwu Chiamaka, and Sodamade Olamide. "Data analytics in public health, A USA perspective: A review." World Journal of Advanced Research and Reviews 20, no. 3 (2023): 211–24. https://doi.org/10.5281/zenodo.12739670.

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The integration of data analytics into public health practices represents a transformative paradigm shift in the United States. This review provides a comprehensive analysis of the impact and implications of data analytics on public health strategies, with a focus on disease surveillance and health policy within the USA. In the context of disease surveillance, data analytics has emerged as a crucial tool for real-time monitoring and early detection of health threats. Leveraging diverse datasets, including electronic health records and social media, allows for swift identification of trends and

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Polancich, Shea, Terri Poe, Natalie Hackney, and Jason Williamson. "Using Technology to Support Data Analytics." Journal for Healthcare Quality 39, no. 5 (2017): 315–20. http://dx.doi.org/10.1097/jhq.0000000000000109.

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Arca, Sevgi, and Rattikorn Hewett. "Analytics on Anonymity for Privacy Retention in Smart Health Data." Future Internet 13, no. 11 (2021): 274. http://dx.doi.org/10.3390/fi13110274.

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Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making

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Geneva Tamunobarafiri Igwama, Janet Aderonke Olaboye, Chukwudi Cosmos Maha, Mojeed Dayo Ajegbile, and Samira Abdul. "Big data analytics for epidemic forecasting: Policy Frameworks and technical approaches." International Journal of Applied Research in Social Sciences 6, no. 7 (2024): 1449–60. http://dx.doi.org/10.51594/ijarss.v6i7.1334.

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This review paper explores the intersection of big data analytics and epidemic forecasting, highlighting both technical approaches and policy frameworks. It delves into data collection methods from IoT, mobile data, and social media. It discusses analytical techniques such as machine learning and predictive modelling. The paper also addresses the regulatory and ethical considerations necessary for effective data use, emphasizing the need for adaptive policy frameworks to support innovation. The importance of international collaboration and global initiatives for data integration and sharing is

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Adekunle Oyeyemi Adenyi, Chioma Anthonia Okolo, Tolulope Olorunsogo, and Oloruntoba Babawarun. "Leveraging big data and analytics for enhanced public health decision-making: A global review." GSC Advanced Research and Reviews 18, no. 2 (2024): 450–56. http://dx.doi.org/10.30574/gscarr.2024.18.2.0078.

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In recent years, the proliferation of big data and analytics technologies has revolutionized various sectors, including public health. This review presents a comprehensive review of how leveraging big data and analytics has enhanced public health decision-making on a global scale. The review encompasses diverse applications, methodologies, challenges, and opportunities within this burgeoning field. Big data analytics in public health encompasses the collection, processing, and analysis of vast datasets from heterogeneous sources, including electronic health records, social media, wearable devi

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Singh, Kiran Deep, Prabh Deep Singh, Rohan Verma, and Harsh Taneja. "Optimizing health data analytics in fog computing using hyperparameter tuning and grid search." Journal of Information and Optimization Sciences 45, no. 2 (2024): 429–38. http://dx.doi.org/10.47974/jios-1560.

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The integration of fog computing with health data analytics signifies a paradigm shift in the field of healthcare, offering the potential for streamlined and prompt analysis of patient welfare. The increasing volume of health data necessitates the development of efficient analytical models in fog computing settings. The objective of this research is to examine the integration of fog computing and health data analytics, specifically emphasizing the utilization of hyperparameter tuning and grid search techniques to enhance optimization approaches. Hyperparameter tuning and grid search are two te

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Houtmeyers, Kobe C., Arne Jaspers, and Pedro Figueiredo. "Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics." International Journal of Sports Physiology and Performance 16, no. 11 (2021): 1719–23. http://dx.doi.org/10.1123/ijspp.2020-0958.

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Elite sport practitioners increasingly use data to support training process decisions related to athletes’ health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in bu

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B, Vaishnavi, Uma V, and Sunitha Ram C. "Research Guidelines on Big Data and Data Analytics: A Survey." Shanlax Internatioal Journal of Arts, Science and Humanities 6, no. 3 (2019): 7–17. https://doi.org/10.5281/zenodo.2550001.

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Large number of devices and objects are now linked to the internet,to transmit data,and collect data back for analytics. The goal is here to utilize this data to make a positive impact on our lifestyle, energy conservation, transportation,and health. The term “Big Data” existed before IoT arrived to carry out the analytics. Themanagement of Big Data in a constantly expandingnetwork gives rise to non-trivial concerns regarding datacollection efficiency, data processing, analytics, and security. In this effort, thereforewe carry out a survey on Big Data technologies in different doma

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Ayush Ahluwalia, Aanchal Sharma, and Manasi Sharma. "Health Informatics & Data Analytics as A Career Choice." International Healthcare Research Journal 6, no. 2 (2022): RV11—RV13. http://dx.doi.org/10.26440/0602.05536.

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Data analytics and informatics both have become essential for the success and reputation of healthcare organizations and with the current increasing demand for the same by such organisations, a career path seems to be full of success and blooming opportunities. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various -omic data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data.

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45

Chinedu James Ezeh, Sandra Chioma Anioke, Sola Oyewole, and Munakur Garnvwa David. "The role of predictive analytics in enhancing public health surveillance: Proactive and data-driven interventions." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3059–77. https://doi.org/10.30574/wjarr.2024.24.3.3909.

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Predictive analytics has transformed public health surveillance, shifting it from reactive to proactive care. Leveraging advanced mathematical tools, artificial intelligence (AI), and machine learning (ML) algorithms, healthcare systems now analyze data to detect patterns, predict outbreaks, and implement timely interventions. This study examines the role of predictive analytics in strengthening disease surveillance, prioritizing resources, and building effective early warning systems. Using qualitative assessments of implemented systems in various healthcare organizations, data was synthesize

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46

Sharma, Nikita, and Dhrubasish Sarkar. "Healthcare Data Analytics Using Power BI." International Journal of Software Innovation 10, no. 1 (2022): 1–10. http://dx.doi.org/10.4018/ijsi.293267.

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Innovations in computer technologies have revolutionized attention in recent years. Data analytics has emerged as a promising tool for determination problems in various health care connected disciplines. The effective utilization of knowledge mining in deeply noticeable fields like e-business, promoting and retail has prompted application in completely different businesses and divisions. Among these components merely finding is the medical services. Medical services organizations can reduce down on medical services expense and furnish better consideration with the help of predictive analysis.

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Abdulraheem Olaide Babarinde, Oluwatoyin Ayo-Farai, Chinedu Paschal Maduka, Chiamaka Chinaemelum Okongwu, and Olamide Sodamade. "Data analytics in public health, A USA perspective: A review." World Journal of Advanced Research and Reviews 20, no. 3 (2023): 211–24. http://dx.doi.org/10.30574/wjarr.2023.20.3.2462.

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The integration of data analytics into public health practices represents a transformative paradigm shift in the United States. This review provides a comprehensive analysis of the impact and implications of data analytics on public health strategies, with a focus on disease surveillance and health policy within the USA. In the context of disease surveillance, data analytics has emerged as a crucial tool for real-time monitoring and early detection of health threats. Leveraging diverse datasets, including electronic health records and social media, allows for swift identification of trends and

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Rostamzadeh, Neda, Sheikh S. Abdullah, and Kamran Sedig. "Visual Analytics for Electronic Health Records: A Review." Informatics 8, no. 1 (2021): 12. http://dx.doi.org/10.3390/informatics8010012.

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The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years,

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Juhola, Martti, Henry Joutsijoki, Kirsi Penttinen, Disheet Shah, Risto-Pekka Pölönen, and Katriina Aalto-Setälä. "Data analytics for cardiac diseases." Computers in Biology and Medicine 142 (March 2022): 105218. http://dx.doi.org/10.1016/j.compbiomed.2022.105218.

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Aliyuda, Christian. "Leveraging Health Analytics to Address Public Health Crises: A Comprehensive Analysis." International Journal of Scientific Research and Management (IJSRM) 10, no. 11 (2022): 999–1005. http://dx.doi.org/10.18535/ijsrm/v10i11.ec01.

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Public health crises, whether originating from pandemics, natural disasters, bioterrorism, or other emergent threats, present significant challenges to global health systems, necessitating rapid and organized responses. Health analytics, encompassing the application of data analytics, machine learning, and statistical models to health-related data, has emerged as an essential tool for predicting, managing, and mitigating the effects of such crises. In this article, we explore the evolving role of health analytics in addressing public health emergencies by leveraging real-time data, advanced mo

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