Healthcare Big Data

2022 ◽  
pp. 119-147
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Use Cases ◽  
Healthcare Industry ◽  
Healthcare Data ◽  
Prescriptive Analytics ◽  
Big Data Technologies ◽  
Good Number

Big data has unlocked a new opening in healthcare. Thanks to the considerable benefits and opportunities, it has attracted the momentous attention of all the stakeholders in the healthcare industry. This chapter aims to provide an overall but thorough understanding of healthcare big data. The chapter covers the 10 ‘V's of healthcare big data as well as different healthcare data analytics including predictive and prescriptive analytics. The obvious advantages of implementing big data technologies in healthcare are meticulously described. The application areas and a good number of practical use cases are also discussed. Handling big data always remains a big challenge. The chapter identifies all the possible challenges in realizing the benefits of healthcare big data. The chapter also presents a brief survey of the tools and platforms, architectures, and commercial infrastructures for healthcare big data.

Healthcare Big Data

2019 ◽  
pp. 72-100 ◽  
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Use Cases ◽  
Healthcare Industry ◽  
Healthcare Data ◽  
Prescriptive Analytics ◽  
Big Data Technologies ◽  
Good Number

Big data has unlocked a new opening in healthcare. Thanks to the considerable benefits and opportunities, it has attracted the momentous attention of all the stakeholders in the healthcare industry. This chapter aims to provide an overall but thorough understanding of healthcare big data. The chapter covers the 10 ‘V's of healthcare big data as well as different healthcare data analytics including predictive and prescriptive analytics. The obvious advantages of implementing big data technologies in healthcare are meticulously described. The application areas and a good number of practical use cases are also discussed. Handling big data always remains a big challenge. The chapter identifies all the possible challenges in realizing the benefits of healthcare big data. The chapter also presents a brief survey of the tools and platforms, architectures, and commercial infrastructures for healthcare big data.

Data Mining Problems Classification and Techniques

2020 ◽  
pp. 70-93
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

Data Mining Problems Classification and Techniques

2018 ◽  
Vol 3 (1) ◽  
pp. 38-57 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

Big Data in Healthcare

2014 ◽  
pp. 35-58 ◽  
Author(s):  
Yiannis Koumpouros
Keyword(s):  
Big Data ◽  
Healthcare Professionals ◽  
Healthcare Providers ◽  
Open Data ◽  
Healthcare Systems ◽  
Tipping Point ◽  
Healthcare Industry ◽  
Healthcare Data ◽  
Big Data Technologies ◽  
Legal Barriers

The era of open data in healthcare is under way. The progress in technologies along with their adoption by the healthcare providers and the maturity of the citizens has brought the healthcare industry to the tipping point. An unprecedented amount of healthcare data is being generated today. This data comes from researchers, healthcare professionals and organizations, and patients. If we can harness this data, it can help us improve our understanding of disease and pinpoint new and improved therapies more efficiently than ever before. Big Data technologies are coming to market in a rapid way. The challenges, however, are still there due to fragmented systems and databases, semantic differences, legal barriers, and others. The hidden and unexploited knowledge is hindered by these barriers. The big data revolution promises a solution both to this situation, as well as to act as a catalyst to the viability of the healthcare systems. This is supported by the numerous efforts and explored in this chapter.

Big Data in Healthcare

2015 ◽  
pp. 23-46 ◽  
Author(s):  
Yiannis Koumpouros
Keyword(s):  
Big Data ◽  
Healthcare Professionals ◽  
Healthcare Providers ◽  
Open Data ◽  
Healthcare Systems ◽  
Tipping Point ◽  
Healthcare Industry ◽  
Healthcare Data ◽  
Big Data Technologies ◽  
Legal Barriers

The era of open data in healthcare is under way. The progress in technologies along with their adoption by the healthcare providers and the maturity of the citizens has brought the healthcare industry to the tipping point. An unprecedented amount of healthcare data is being generated today. This data comes from researchers, healthcare professionals and organizations, and patients. If we can harness this data, it can help us improve our understanding of disease and pinpoint new and improved therapies more efficiently than ever before. Big Data technologies are coming to market in a rapid way. The challenges, however, are still there due to fragmented systems and databases, semantic differences, legal barriers, and others. The hidden and unexploited knowledge is hindered by these barriers. The big data revolution promises a solution both to this situation, as well as to act as a catalyst to the viability of the healthcare systems. This is supported by the numerous efforts and explored in this chapter.

scholarly journals Big Data Analytics in urology: the story so far and the road ahead

2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Early Stage ◽  
Big Data Analytics ◽  
Positive Trend ◽  
The Road ◽  
Healthcare Data ◽  
Urological Disorders ◽  
Different Sources

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

Applications of Big Data Analytics in Healthcare Management Systems

2021 ◽  
Vol 23 (06) ◽  
pp. 1167-1182
Author(s):  
Shreyas Nopany ◽  
◽  
Prof. Manonmani S ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Management ◽  
Healthcare Sector ◽  
Future Research ◽  
Healthcare Industry ◽  
Strategy Formulation ◽  
Data Governance ◽  
Number Of Patients

The healthcare industry has become increasingly demanding in recent years. The growing number of patients makes it difficult for doctors and staff to manage their work effectively. In order to achieve their objectives, data analysts collect a large amount of data, analyze it, and use it to derive valuable insights. Data analytics may become a promising solution as healthcare industry demands increase. The paper discusses the challenges of data analytics in the healthcare sector and the benefits of using big data for healthcare analytics. Aside from focusing on the opportunities that big data analytics has in the healthcare sector, the paper will also discuss data governance, strategy formulation, and improvements to IT infrastructure. Implementation techniques include Hadoop, HDFS, MapReduce, and Apache in Big Data Analytics. A Healthcare Management System can be categorized into five divisions, namely, Drug discovery, Disease prevention, diagnosis and treatment, Hospital operations, post-care, requiring comprehensive data management. Big Data analysis support transformation is identified as a required component in future research for the application of Big Data in HealthCare.

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