C. D.COMBS, JOHN A.SOKOLOWSKI and CATHERINE M.BANKS. The Digital Patient: Advancing Healthcare, Research, and Education. Hoboken: Wiley. CHANDAN K. REDDY and CHARU C. AGGARWAL. Healthcare Data Analytics. Boca Raton: CRC Press.

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.

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Chronic Kidney Disease for Collaborative Healthcare Data Analytics using Random Forest Classification Algorithms

2021 International Conference on Computer Communication and Informatics (ICCCI) ◽

10.1109/iccci50826.2021.9402574 ◽

2021 ◽

Author(s):

V. Shanmugarajeshwari ◽

M. Ilayaraja

Keyword(s):

Chronic Kidney Disease ◽

Random Forest ◽

Kidney Disease ◽

Data Analytics ◽

Classification Algorithms ◽

Random Forest Classification ◽

Healthcare Data ◽

Forest Classification

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

International Journal of Privacy and Health Information Management ◽

10.4018/ijphim.2020010102 ◽

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.

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Big Healthcare Data Analytics: Challenges and Applications

Handbook of Large-Scale Distributed Computing in Smart Healthcare - Scalable Computing and Communications ◽

10.1007/978-3-319-58280-1_2 ◽

2017 ◽

pp. 11-41 ◽

Cited By ~ 13

Author(s):

Chonho Lee ◽

Zhaojing Luo ◽

Kee Yuan Ngiam ◽

Meihui Zhang ◽

Kaiping Zheng ◽

...

Keyword(s):

Data Analytics ◽

Healthcare Data

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On Delay-Sensitive Healthcare Data Analytics at the Network Edge Based on Deep Learning

2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) ◽

10.1109/iwcmc.2018.8450475 ◽

2018 ◽

Cited By ~ 8

Author(s):

Zubair Md. Fadlullah ◽

Al-Sakib Khan Pathan ◽

Haris Gacanin

Keyword(s):

Deep Learning ◽

Data Analytics ◽

Healthcare Data ◽

Delay Sensitive ◽

Edge Based

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Big Data Analytics in Healthcare Sector

Advances in Business Information Systems and Analytics - Machine Learning Techniques for Improved Business Analytics ◽

10.4018/978-1-5225-3534-8.ch005 ◽

2019 ◽

pp. 94-106 ◽

Cited By ~ 1

Author(s):

Sheik Abdullah A. ◽

Selvakumar S. ◽

Parkavi R. ◽

Suganya R. ◽

Abirami A. M.

Keyword(s):

Big Data ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Implementation Process ◽

Healthcare Sector ◽

Data Preparation ◽

Healthcare Data ◽

R Programming ◽

Python Programming

The importance of big data over analytics made the process of solving various real-world problems simpler. The big data and data science tool box provided a realm of data preparation, data analysis, implementation process, and solutions. Data connections over any data source, data preparation for analysis has been made simple with the availability of tremendous tools in data analytics package. Some of the analytical tools include R programming, python programming, rapid analytics, and weka. The patterns and the granularity over the observed data can be fetched with the visualizations and data observations. This chapter provides an insight regarding the types of analytics in a big data perspective with the realm in applicability towards healthcare data. Also, the processing paradigms and techniques can be clearly observed through the chapter contents.

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Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach

Computing ◽

10.1007/s00607-019-00785-6 ◽

2020 ◽

Vol 102 (5) ◽

pp. 1187-1198 ◽

Cited By ~ 1

Author(s):

Khaled Fawagreh ◽

Mohamed Medhat Gaber

Keyword(s):

Random Forest ◽

Mobile Devices ◽

Data Analytics ◽

High Accuracy ◽

Data Sets ◽

Random Forest Regression ◽

Healthcare Data ◽

Processing Power ◽

Fast Prediction ◽

Speed And Accuracy

AbstractIn predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF, which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases.

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