Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing

It is entering an era of big data, which facilitated great improvement in various sectors. Particularly, assisted by wireless communications and mobile computing, mobile devices have emerged with a great potential to renovate the healthcare industry. Although the advanced techniques will make it possible to understand what is happening in our body more deeply, it is extremely difficult to handle and process the big health data anytime and anywhere. Therefore, data analytics and mobile computing are significant for the healthcare systems to meet many technical challenges and problems that need to be addressed to realize this potential. Furthermore, the advanced healthcare systems have to be upgraded with new capabilities such as machine learning, data analytics, and cognitive power for providing human with more intelligent and professional healthcare services. To explore recent advances and disseminate state-of-the-art techniques related to data analytics and mobile computing on designing, building, and deploying novel technologies, to enable intelligent healthcare services and applications, this paper presents the detailed design for developing intelligent healthcare systems assisted by data analytics and mobile computing. Moreover, some representative intelligent healthcare applications are discussed to show that data analytics and mobile computing are available to enhance the performance of the healthcare services.

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HealthTEC Innovation Design - a proposal for a novel Master degree program based on Unmet Clinical Need, global Healthcare Challenges, and 21st century skills

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3153 ◽

2020 ◽

Vol 6 (3) ◽

pp. 599-603

Author(s):

Michael Friebe

Keyword(s):

Machine Learning ◽

21St Century ◽

21St Century Skills ◽

Data Analytics ◽

Industrial Revolution ◽

Data Gathering ◽

Healthcare Systems ◽

Disruptive Innovation ◽

Incremental Innovation ◽

Healthcare Provision

AbstractThe effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.

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Big Data Analytics in Healthcare

BioMed Research International ◽

10.1155/2015/370194 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 178

Author(s):

Ashwin Belle ◽

Raghuram Thiagarajan ◽

S. M. Reza Soroushmehr ◽

Fatemeh Navidi ◽

Daniel A. Beard ◽

...

Keyword(s):

Big Data ◽

Data Analytics ◽

Care Delivery ◽

Healthcare Delivery ◽

Big Data Analytics ◽

Healthcare Systems ◽

Medical Data ◽

Unstructured Data ◽

Process Of Care ◽

Multimodal Data

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

10.22514/sv.2021.253 ◽

2021 ◽

Keyword(s):

Data Analytics ◽

Exploratory Data Analysis ◽

Reproduction Number ◽

Healthcare Systems ◽

Regional Level ◽

Data Driven ◽

Effective Response ◽

Reproduction Numbers ◽

Exploratory Data ◽

Using Data

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.

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A Study on Ubiquitous Intelligent Healthcare Systems in Home Service Aggregation Business Model

Ubiquitous Intelligence and Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-540-73549-6_96 ◽

2007 ◽

pp. 983-992

Author(s):

Mun-Suck Jang ◽

Kwang-Sik Shin ◽

Eung-Huyk Lee ◽

Sang-Bang Choi

Keyword(s):

Business Model ◽

Healthcare Systems ◽

Home Service ◽

Service Aggregation ◽

Intelligent Healthcare

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A Unified Framework for Differentiated Services in Intelligent Healthcare Systems

IEEE Transactions on Network Science and Engineering ◽

10.1109/tnse.2021.3127942 ◽

2021 ◽

pp. 1-1

Author(s):

Abubakr O. Al-Abbasi ◽

Lutfi Samara ◽

Saeed Salem ◽

Ridha Hamila ◽

Naofal Al-Dhahir

Keyword(s):

Differentiated Services ◽

Healthcare Systems ◽

Unified Framework ◽

Intelligent Healthcare

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Cloud-Based Healthcare Systems

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Emerging Research in Cloud Distributed Computing Systems ◽

10.4018/978-1-4666-8213-9.ch010 ◽

2015 ◽

pp. 315-338

Author(s):

Ahmed Shawish ◽

Maria Salama

Keyword(s):

Gross Domestic Product ◽

Mobile Computing ◽

Population Health ◽

Developed Countries ◽

Healthcare Systems ◽

It Infrastructure ◽

Research Issues ◽

Comprehensive Review ◽

Smart Healthcare ◽

And Storage

Healthcare is one of the most important sectors in all countries and significantly affects the economy. As such, the sector consumes an average of 9.5% of the gross domestic product across the most developed countries; they should invoke smart healthcare systems to efficiently utilize available resources, vastly handle spontaneous emergencies, and professionally manage the population health records. With the rise of the Cloud and Mobile Computing, a vast variety of added values have been introduced to software and IT infrastructure. This chapter provides a comprehensive review on the new Cloud-based and mobile-based applications that have been developed in the healthcare field. Cloud's availability, scalability, and storage capabilities, in addition to the Mobile's portability, wide coverage, and accessibility features, contributed to the fulfillment of healthcare requirements. The chapter shows how Cloud and Mobile opened a new environment for innovative services in the healthcare field and discusses the open research issues.

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