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Bigdata & IoT role in healthcare, Schemes and Mind Maps of Health sciences

California Lutheran University (CLU)Health sciences

The use of big data in healthcare for management and analytics. It highlights the challenges associated with handling big data and the potential benefits of its analysis in healthcare. The document also provides a literature review of various journal papers related to the topic and explores the applicability of big data analytics in healthcare. It discusses the use of Random Forest technique for identifying new symptoms of diseases and the data mining process for classification, clustering, and decision tree analysis. The document emphasizes the complexity of big data analytics tools and the need for appropriate infrastructure to systematically generate and analyze big data.

Typology: Schemes and Mind Maps

2020/2021

Available from 01/13/2022

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Use of Big data in Healthcare for Management and
Analytics
Problem Statement
The term big data mean for vast amount of data, In health care industry
various sources of data include hospital records, medical records of patients ,
result of medical tests and various IOT (Internet of Things) devices. Biomedical
research also generates a significant portion of big data relevant to public
healthcare. These data requires proper management and analysis in order to
derive meaningful information. Big data is will solve this problem of
management of large data and it’s analysis, otherwise seeking solution by
analysing vast amount data quickly becomes comparable to finding a needle in
the haystack.
There are various challenges associated with each step of handling big data
which can only be surpassed by using high-end computing solutions for big
data analysis. That is why, to provide relevant solutions for improving public
health, healthcare providers are required to be fully equipped with appropriate
infrastructure to systematically generate and analyse big data. An efficient
management, analysis, and interpretation of big data can change the game by
opening new avenues for modern healthcare. That is exactly why various
industries, including the healthcare industry, are taking vigorous steps to
convert this potential into better services and financial advantages. In
fact,'healthcare analytics'has the potential to reduce costs of treatment,
predict outbreaks of epidemics, avoid preventable diseases, and improve the
quality of life in general. The average human lifespan is increasing across the
world population, which poses new challenges to today’s treatment delivery
methods. Health professionals, just like business entrepreneurs, are capable of
collecting massive amounts of data and look for the best strategies to use
these numbers.
This project will solve the problem of storage and process of large amount of
data in health care system, which is gathered from various sources. Processing
of these data through tradition system is very difficult as data grow and big
data can process and analyse easily.
Literature Review
I did review of various literature and journal available on internet and these
three journal papers is very useful of my project.
1. Big data in healthcare: management, analysis and future prospects
Sabyasachi Dash,'Sushil Kumar Shakyawar,'Mohit Sharma'&'Sandeep
Kaushik'
Journal of Big Data volume 6, Articlenumber:54(2019)
2. Big data analytics in healthcare
Sayantan Khanra,'Amandeep Dhir, A.K.M. Najmul Islam
Published online: 12 Oct 2020 (https://doi.org/10.1080/17517575.2020.1812005)
3. Exploring big data analytics in health care
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Use of Big data in Healthcare for Management and

Analytics

Problem Statement

The term big data mean for vast amount of data, In health care industry various sources of data include hospital records, medical records of patients , result of medical tests and various IOT (Internet of Things) devices. Biomedical research also generates a significant portion of big data relevant to public healthcare. These data requires proper management and analysis in order to derive meaningful information. Big data is will solve this problem of management of large data and it’s analysis, otherwise seeking solution by analysing vast amount data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyse big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population, which poses new challenges to today’s treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for the best strategies to use these numbers. This project will solve the problem of storage and process of large amount of data in health care system, which is gathered from various sources. Processing of these data through tradition system is very difficult as data grow and big data can process and analyse easily.

Literature Review

I did review of various literature and journal available on internet and these three journal papers is very useful of my project.

1. Big data in healthcare: management, analysis and future prospects Sabyasachi Dash, Sushil Kumar Shakyawar, Mohit Sharma & Sandeep Kaushik Journal of Big Data volume 6 , Article number: 54 (2019) 2. Big data analytics in healthcare Sayantan Khanra, Amandeep Dhir, A.K.M. Najmul Islam Published online: 12 Oct 2020 (https://doi.org/10.1080/17517575.2020.1812005) 3. Exploring big data analytics in health care

T. Ramesh, V. Santhi Journal of Big Data volume 1 , Pages: 135-140(2020) These papers provide me various idea to explore the usages of big data in health care industry for analytics. Big data analytics will allow us to predict the various trends of disease and diagnostics. First article allowed me to explore the big data analysis and future prospects of it’s usages I healthcare. Healthcare is a multi-dimensional system established with the sole aim for the prevention, diagnosis, and treatment of health-related issues or impairments in human beings. The major components of a healthcare system are the health professionals (physicians or nurses), health facilities (clinics, hospitals for delivering medicines and other diagnosis or treatment technologies), and a financing institution supporting the former two. The health professionals belong to various health sectors like dentistry, medicine, midwifery, nursing, psychology, physiotherapy, and many others. Healthcare is required at several levels depending on the urgency of situation. Professionals serve it as the first point of consultation (for primary care), acute care requiring skilled professionals (secondary care), advanced medical investigation and treatment (tertiary care) and highly uncommon diagnostic or surgical procedures (quaternary care). At all these levels, the health professionals are responsible for different kinds of information such as patient’s medical history (diagnosis and prescriptions related data), medical and clinical data (like data from imaging and laboratory examinations), and other private or personal medical data. Previously, the common practice to store such medical records for a patient was in the form of either handwritten notes or typed report. The development and usage of wellness monitoring devices and related software that can generate alerts and share the health related data of a patient with the respective health care providers has gained momentum, especially in establishing a real-time biomedical and health monitoring system. These devices are generating a huge amount of data that can be analysed to provide real-time clinical or medical care. The use of big data from healthcare shows promise for improving health outcomes and controlling costs. Second paper allowed me to research on the applicability of big data analytics (BDA) in healthcare. A discussion on the use of BDA in healthcare has been developing recently and it gained major attention from several research. In the quest to meet this need, prior research has followed two approaches to reviewing the literature. The first approach has been to review narrow areas within the literature on the use of BDA in healthcare. For instance, in 2nd article highlighted the promising opportunities that BDA offers to advance research on Alzheimer’s disease. The second approach to reviewing this body of literature has focused on summarising broad topics related to the use of BDA in healthcare. For instance, studies often attempt to summarise the sources of big data, the technologies used in the analysis of big data, the benefits offered by BDA, and the challenges involved in harnessing those benefits in healthcare. We appreciate the valuable knowledge about the use of BDA in healthcare offered by these studies. However, none of these studies have evaluated the quality of the documents in the sample under review. Consequently, the findings of these

probability and Likelihood for obtaining posterior probability. This posterior probability will be used for reducing multi-dimensional data into number of one dimensional ones without affecting the classification of deceases from historical data.

In order to predict new symptoms of deceases Random Forest technique can be applied where each node of a tree constitute a point where a decision must be taken based on Input from the previous node and it moves to next node which keep repeats until it reaches leaf node which depicts predicted output. At the leaf level unknown symptoms can be identified based on the decision taken at previous nodes. This particular technique is very much effective for identifying new symptoms of a deceases which are unknown. By this treatment can be given for patients effectively with minimum cost. Random Forests uses different sample of data in order to train each tree, and this can be stored in a memory such that it avoids replication of data explicitly. In order to process data mining process can be applied which involves Classification, Clustering and Decision tree analysis. Big data analytics tools, on the other hand, are extremely complex, programming intensive, and require the application of a variety of skills. As shown in Fig. 1 , Big data in healthcare can come from internal (e.g., electronic health records, clinical decision support systems, CPOE, etc.) and external sources (government sources, laboratories, pharmacies, insurance companies & HMOs, etc.) as the complexity increases with data as shown in Fig. 1 , often in multiple formats (flat files,.csv, relational tables, ASCII/text, etc.) and residing at multiple locations (geographic as well as in different healthcare providers’ sites) in numerous legacy and other applications (transaction processing applications, databases, etc.). In general large datasets will give better insight about in making decisions about healthcare.