Applications of Domain-Specific Predictive Analytics Applied to Big Data

2019 ◽  
pp. 289-306 ◽  
Author(s):  
Ravi Kumar Poluru ◽  
Bharath Bhushan ◽  
Basha Syed Muzamil ◽  
Praveen Kumar Rayani ◽  
Praveen Kumar Reddy
Keyword(s):  
Big Data ◽  
Predictive Analytics ◽  
Credit Scoring ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Social Media Analytics ◽  
Product Reviews ◽  
Telecommunication Sector ◽  
Customer Lifetime ◽  
Good Productivity

Performing prediction on every domain belonging to industry/firm is measured as effective management. This prediction helps the firm effectively manage human power and other resources, which leads to good productivity. In this chapter, the authors discuss applications where predictive analytics are applied. The applications are as follows: evaluation of customer lifetime value used in retail industry, customer churn management in the telecommunication sector, credit scoring in banking, sentiment analysis on product reviews to understand the customer opinion, clinical decision support systems, news analytics, and social media analytics. They conclude the application areas of predictive analytics will drive the research community towards developing novel methods for handling big data.

scholarly journals The Benefits of Big Data Analytics in the Healthcare Sector

2015 ◽  
pp. 406-439 ◽  
Author(s):  
Andrea Darrel ◽  
Margee Hume ◽  
Timothy Hardie ◽  
Jeffery Soar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Sector ◽  
Improved Outcomes ◽  
Health Initiatives ◽  
Remote Patient

The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

Big Data and Analytics

2020 ◽  
pp. 47-65
Author(s):  
Sheik Abdullah A. ◽  
Priyadharshini P.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Analytics ◽  
Data Warehousing ◽  
Big Data Analytics ◽  
Structured Data ◽  
Social Media Analytics ◽  
Data Set ◽  
Data Volume ◽  
Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

Towards an Intelligent Integrated Approach for Clinical Decision Support

Big Data ◽  
2016 ◽  
pp. 1987-2005
Author(s):  
Rajendra Akerkar
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Information ◽  
Clinical Decision Support Systems ◽  
Integrated Approach ◽  
Clinical Decision ◽  
Research Direction ◽  
Integrated System ◽  
Intelligent Integrated System

Nowadays, making use of big data is becoming mainstream in different enterprises and industry sectors. The medical sector is no exception. Specifically, medical services, which generate and process enormous volumes of medical information and medical device data, have been quickening big data utilization. In this chapter, we present a concept of an intelligent integrated system for direct support of decision making of physicians. This is a work in progress and the focus is on decision support for pharmacogenomics, which is the study of the relationship between a specific person's genetic makeup and his or her response to drug treatment. Further, we discuss a research direction considering the current shortcomings of clinical decision support systems.

scholarly journals The Benefits of Big Data Analytics in the Healthcare Sector

2016 ◽  
pp. 842-875 ◽  
Author(s):  
Andrea Darrel ◽  
Margee Hume ◽  
Timothy Hardie ◽  
Jeffery Soar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Sector ◽  
Improved Outcomes ◽  
Health Initiatives ◽  
Remote Patient

The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.

Mental Health Clinical Decision Support Exploiting Big Data

2021 ◽  
pp. 983-1000
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

scholarly journals Big Data as a Revolutionary Tool in Finance

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Aureliano Angel Bressan
Keyword(s):  
Big Data ◽  
Portfolio Management ◽  
Predictive Analytics ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Product Reviews ◽  
Reputational Risk ◽  
Learning Techniques ◽  
Model Complex ◽  
Complex Relationships

A data driven culture is arising as a research field and analytic tool in Finance and Management since the advent of structured, semi-structured and unstructured socio-economic and demographic information from social media, mobile devices, blogs and product reviews from consumers. Big Data, the expression that encompasses this revolution, involves the usage of new tools for financial professionals and academic researchers due to the size of data involved, which require more powerful manipulation tools. In this sense, Machine Learning techniques can allow more effective ways to model complex relationships that arise from the interaction of different types of data, regarding issues such as Operational and Reputational Risk, Portfolio Management, Business Intelligence and Predictive Analytics. The following books can be a good start for those interested in this new field.

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