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Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
Concepts, Methodologies, Tools and Applications
Sunil Kumar Dhal, Subhendu Kumar Pani, Srinivas Prasad, Sudhir Kumar Mohapatra, Sunil Kumar Dhal, Srinivas Prasad, Sudhir Kumar Mohapatra, Subhendu Kumar Pani
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eBook - ePub
Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
Concepts, Methodologies, Tools and Applications
Sunil Kumar Dhal, Subhendu Kumar Pani, Srinivas Prasad, Sudhir Kumar Mohapatra, Sunil Kumar Dhal, Srinivas Prasad, Sudhir Kumar Mohapatra, Subhendu Kumar Pani
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BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS
Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics.
The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data.
The 12 chapters in?? Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT).
New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches.
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Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.
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1
An Introduction to Big Data Analytics Techniques in Healthcare
Anil Audumbar Pise*
University of the Witwatersrand, Johannesburg, South Africa
AbstractThere is a notable rise in the amount of data being generated in the healthcare industries. Trying to improve the health outcomes and cut the costs derived from better utilization of healthcare data has been of great interest to healthcare providers (and the abundance of the data has brought that about big change), whereas the nature of healthcare data presents specific problems when it comes to processing and looking at big data, particularly, as well as analyzing the abundance of it. Some new ideas about how to deal with these problems are discussed in this chapter. According to this chapter, there are two ways in which advances in processing healthcare data have been made in the last 10 years that may make generating better predictions from the medical data feasible. Firstly by using advancing technological methods of analysis and secondly developing novel models that can handle large quantities of data.Keywords: Healthcare analytics, predictive analytics, healthcare informatics, big data
1.1 Introduction
Big Data has the potential to transform all sorts of business sectors, from the wellness of individuals to the provision of healthcare. For the purposes of most current day, Big Data is defined as āstoring, arranging, and processing, the current huge amounts of heterogeneous data, getting results, and then reorganized and measured data is called Clean/Big Dataā. This pattern emerges because businesses are using technology to accomplish more and to help customers generate more data which creates a greater volume of data that consumers then produce, who generate bigger volumes of data in social networks. A variety of new developments involving more modern sources and different ways of processing data is currently emerging in the healthcare and medical industries. One thing is clear from the research point of view is the field of āomicsā in which previously used, pre-owned data offers new approaches to e-health records, open data, and the āquantified selfā methodologies for enhancing data analytics. We have made tremendous advances in text data extraction, which unlocks a lot of information in the medical records for analytics. On the other hand, big data use in healthcare, adoption of new medical and healthcare practices are moving more slowly than people may be expected. These difficulties can be found to their varying levels of data complexity, to issues regarding data, organization, and regulations, and also issues concerning ethical issues. It is very likely that new ideas and better practices for data acquisition and data analysis will emerge from larger scales of the accumulation of big data and the best practices. This paper takes a comprehensive look at the possibilities of Big Data holds for the medical and healthcare professions.
Although big data analytics is relatively new in its role in-flux in healthcare, it is nevertheless having a significant impact in practices and research. The system has given healthcare researchers the ability to gather, store, and manage disparate, structured, and unstructured data generated by current healthcare systems, as well as data sets for analysis. Larger databases and powerful computer software have recently been used in medical research to help with delivery and disease exploration. Some of the most basic big data principles cannot be escaped, even though advances have been made; as long as there are these limitations, they may persist in preventing further development in this sector. A concern that we wanted to tackle in this paper is the obstacles we encounter in three exciting new and emergent medical research areas: Genomic Data Analysis, Signal Detection, and Medical Image Processing. In the most recent studies, the focus has been on employing high volume data of medical information, which integrates multimodal information from diverse sources. In order to evaluate the capabilities and opportunities for healthcare delivery, research focuses on areas with the ability to make a positive difference as well as well as potential.
The remainder of this chapter is organized as follows. In Section 1.2, a brief idea of Big Data in Healthcare is explained with basic introduction and concept of the five Vs of big data with aspects that explore the use of big data in medical field. In Section 1.3, Areas of Big Data Analytics in medicine are discussed. In Section 1.4, the Concept of Healthcare a Big Data Repository is briefly explained. Then, Section 1.5 presents Applications of Healthcare Big Data with examples and in Section 1.6 Challenges in Big Data Analytics are provided. Big Data Privacy and Security policies are explained in Section 1.7. The remaining sections provide a conclusion and future work.
1.2 Big Data in Healthcare
The term āBig Dataā refers to the volume, velocity, and variety of data generated over time by healthcare providers and containing information pertinent to a patientās care, such as demographics, diagnoses, medical procedures, medications, vital signs, immunizations, laboratory results, and radiology images. Figure 1.1 depicts above mentioned healthcare entities.
Figure 1.1 Big data in healthcare.
Figure 1.2 Five vs of big data.
According to Thota et al. [1], electronic health sources such as sensor devices, streaming machines, and high-throughput instruments are accumulating more data as medical data collection advances. This big data in healthcare is used for a variety of purposes, including diagnosis, drug discovery, precision medicine, and disease prediction. Big data has been critical in a variety of fields, including healthcare, scientific research, industry, social networking, and government administration [1]. The five Vs of big data are as follows as shown in the Figure 1.2 for better understanding:
- 1. Variety: Without a doubt, the variety of data represents big data. For instance, among the various data formats are database, excel, and CSV, all of which can be stored in a plain text file. Additionally, structured, unstructured, and semi-structured health data exist. Clinical data is an example of structured information; however, unstructured or semi-structured data includes doctor notes, paper prescriptions, office medical records, images, and radio-graph films.
- 2. Veracity: This dataās legitimacy in the form of veracity can be challenged only if it is inaccurate. It is not about the accuracy of the data; it is about the capacity to process and interpretation of data. In healthcare, the trustworthiness function gives details on correct diagnosis, treatment, appropriate prescriptions, or otherwise established health outcomes.
- 3. Volume: Without a doubt, the large volume represents large amounts of data. To process massive amounts of data such as text, audio, video, and large-format images, existing data processing platforms and techniques must be strengthened. Personal information, radiology images, personal medical records, genomics, and biometric sensor readings, among other things, are gradually integrated into a healthcare database. All of this information adds significantly to the databaseās size and complexity.
- 4. Velocity: Big data is completely represented by the amount of information produced every second is considered as velocity. The information burst of social media has brought about a wide range of new and interesting data. Data on overall health condition and growth of the plant size and food bacteria are stored on paper, as well as various X-ray images and written reports, is up dramatically.
- 5. Value: Big data truly embodies the value of data. When it comes to big data analytics, the benefits and costs of analyzing and collecting big data are more important. In healthcare, the creation of value for patients should dictate how all other actors in the system are compensated. The primary goal of healthcare delivery must be to maximize value for patients.
1.3 Areas of Big Data Analytics in Medicine
It is of critical importance to pay attention to a multitude of events that impact the health, both physiologically and pathologically. Occurring at once and expressed in various ways (systemic) aspects of the body lead to interaction between different cardiovascular parameters (i.e. such as minute ventilation and blood pressure) which results in accurate clinical evaluation. As a result, understanding and predicting diseases necessitate an integrated data collection of both structured and unstructured methods that draw on the enormous spectrum of clinical and non-clinical data to create a more thorough picture of disease depiction. Big data analytics has recently made its entrance into the healthcare industry, medical researchers are excited about an entirely new aspect of this research known as incorporating the newer concepts. Researchers are conducting research on healthcare data pertaining to both the data itself and the taxonomy of useful analytics that can be done on it.
Figure 1.3 Areas of big data analytics in medicine.
Expanding on this one would include three areas of big data analytics in medicine which is discussed in this chapter. These three research areas do not comprehensively showcase the many ways big data analytics are applied in medicine; instead, they provide a collection of loosely defined use cases where big data analytics is being employed as shown in Figure 1.3.
1.3.1 Genomics
In [2] the author suggested that the estimated price of sequencing the human DNA (the ācombing costā of) has dropped significantly in the past few years [cost to combing the 30,000 to 35,000 gene map is now inversely proportional to how many genes are found] on the grand scale, and as it is to computational biology, developing genome-scale solutions that are applied to the field of public health can have implications for current and future public health policies and services. In 2013 [3] researcher claimed that, the most important factor in making recommendations in a clinical setting is the cost and time to put them in place. Prospective/preventive, and proctical health-focused strategies aim to acquire information on 100,000 individuals for more than two decades, known as P4-predicted (stating only if it is possible); research using the predictive-targeted, or integrated omics, referred to as personalomics (using your personal data). In [4] the author suggested to include seeking solutions ...