Intelligent health data analytics: A convergence of artificial intelligence and big data

2019 ◽  
Vol 32 (4) ◽  
pp. 178-182 ◽  
Author(s):  
Syed Sibte Raza Abidi ◽  
Samina Raza Abidi
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Health Outcomes ◽  
Health System ◽  
Data Analytics ◽  
Heterogeneous Data ◽  
Health Data ◽  
Care Quality ◽  
System Management ◽  
Health Data Analytics

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 intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.

scholarly journals Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

2022 ◽  
Vol 70 (3) ◽  
pp. 4467-4483
Author(s):  
Nithya Rekha Sivakumar ◽  
Ahmed Zohair Ibrahim
Keyword(s):  
Artificial Intelligence ◽  
Data Analytics ◽  
Health Data ◽  
Health Data Analytics

Advances for Artificial Intelligence in Health Data Analytics to Drive Digital Systems Innovation

2020 ◽  
pp. 1-12
Author(s):  
Kamaljeet Sandhu
Keyword(s):  
Artificial Intelligence ◽  
Health Services ◽  
Data Analytics ◽  
Health Data ◽  
Modern Technology ◽  
Digital Systems ◽  
Clear Understanding ◽  
Vested Interests ◽  
The Government ◽  
Health Data Analytics

Artificial intelligence in health (AIH) and health data has become a focus of attention for customers of health services, organizations providing health services, and the government organization monitoring the performance and outcome for health services. These three groups have vested interests in how, where, and when the health data can be used and delivered to facilitate and streamline the delivery and process for health services from adopting AIH. The driving force in AIH for health data analytics stems from the discovery of new information, analysis that seeks to provide a clear understanding of a problem, interpretation in making clear sense of the problem, and communication of meaningful data patterns that can be effectively used in finding solutions to drive digital systems innovation. Modern technology provides an important platform for the health data transformation at different stages of the process to deliver different kinds of health services adopting artificial intelligence.

scholarly journals Big data analytics in the health sector: challenges and potentials

2019 ◽  
Vol 24 (1) ◽  
pp. 23 ◽  
Author(s):  
Marina Jovanovic Milenkovic ◽  
Aleksandra Vukmirovic ◽  
Dejan Milenkovic
Keyword(s):  
Big Data ◽  
Healthcare System ◽  
Data Analytics ◽  
Research Question ◽  
Health Sector ◽  
Big Data Analytics ◽  
Health Data ◽  
Vital Role ◽  
Healthcare Sector ◽  
Health Data Analytics

Research Question: The introduction of the Big Data concept in the healthcare sector points to a major challenge and potential. Motivation: Our goal is to indicate the importance of analyzing and processing large amounts of data that go beyond the typical ways of storing and processing information. Тhе data have their own characteristics: volume, velocity and variety. There are different structures. Analysis of these data is possible with the Big Data concept. Its importance is most evident in the health sector, because the preservation of the health status of the population depends on adequate data analysis. Idea: The idea of the paper is that big health data analytics contributes to a better quality provision of health services. The process is more efficient and effective. Data: Health analytics suggests that more and more resources are being utilized globally. In order to achieve improvements, health analytics and Big data concepts play a vital role in overcoming the obstacles, working more efficiently and aiming at providing adequate medical care. Tools: The Big data concept will help identify patients with developed chronic diseases. Big data can identify outbreaks of flu or other epidemics in real time. In this way, they are managed by the healthcare system, reducing overall healthcare costs over time, and increasing revenues. Findings: A key policy challenge is to improve the outcomes of the healthcare system,  data collection and analysis, security, storage and transfers. Big data are the potential to improve quality of care, improve predictions of diseases, improve the treatment methods, reduce costs. Contribution: This paper points to the challenges and potentials of Big Health Data analytics and formulates good reasons to apply the Big Data concept in healthcare.

A health data analytics maturity model for hospitals information systems

2019 ◽  
Vol 46 ◽  
pp. 278-285 ◽  
Author(s):  
João Vidal Carvalho ◽  
Álvaro Rocha ◽  
José Vasconcelos ◽  
António Abreu
Keyword(s):  
Information Systems ◽  
Data Analytics ◽  
Health Data ◽  
Maturity Model ◽  
Health Data Analytics

Predictive Analysis for Digital Marketing Using Big Data

2017 ◽  
pp. 259-283
Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Cognitive Computing ◽  
Internet Resources ◽  
To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

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