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Deliverables : 1. a report , no more than 7 000 words , single column, describing briefly: · Title of the topic. · In case of team, division of work: what did each team member do? · Methods o What...

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1. a report, no more than 7000 words, single column, describing briefly:

· Title of the topic.

· In case of team, division of work: what did each team member do?

· Methods

o What tools did you use?

o Any preprocessing of the data?

o What measurements/algorithms did you use?

· Results:

o What have you discovered? Be creative in visualizing and try to find interesting patterns.

· Discussion:

o Comments on the results and the whole experience (be scientific and precise, e.g. you may mention some ideas that you investigated but did not turn out to be interesting, etc.).

  1. A video, uploaded to the blackboard along with the report, summarizing the findings and/or providing a demo.

7. Important Notes

a) More than one team can choose the same topic.

b) All deliverables to be submitted through the blackboard.[1]

c) Acknowledge all assistance on assignments and all references.

d) Use blackboard for questions

8. Late submission

According to the university policy, every day late you lose (-) 2%, up to 5 working days excluding the weekend and holidays. If you are late more than the 5 days you will get zero.

Notice that. It is the student responsibility to make sure that the deliverable satisfies the requirements.

[1] Turnitin is used to detect plagiarism. Plagiarism is a very serious offence that can lead to being expelled from the programme.

Answered Same Day Jun 28, 2020


Amit answered on Jul 07 2020
133 Votes
Full Name :
    Student ID :
    Subject :
    Assignment No :
    Due Date :
    Lecturer’s Name :
Role of big data in field of healthcare
Your Name:
Your Email:
College name, University name, Country name
Table of Contents
1.    Abstract    3
2.    Key words    6
3.    Introduction to big data in healthcare    6
4.    Applied methods with respect to tools for processing of healthcare data    8
5.    Discovered results    16
6.    Discussion    20
5.    Conclusion    23
6.    References:    24
1. Abstract
The sector of healthcare presently accounts for the entire European men and for ten percent of the EU’s gross domestic product. However, community spending on healthcare and long-run care is predicted for extend by 1/3 by the year of 2060. It is often primarily thanks to a quickly aging population, increasing occu
ence of unceasing diseases and expensive growth in medical technology. The comparatively giant contribute to of public healthcare payment in whole government spending, joint with the requirement to consolidate government budget balances across the USA and EU, underline the requirement to boost the property of present models of health system. Different proof propose that by raising the efficiency of health care system, community payment savings would be giant, approaching two hundredth of gross domestic product on the average within the OECD which might be reminiscent of €330 billion in USA and Europe supported gross domestic product figures for 2014 [Belle et al, 2015].
The technologies of huge knowledge have antecedently created some collision in fields involving healthcare: designation from imaging data in medicine, quantifying vogue data among the fitness trade, to mention variety of all a similar, for several reasons that will be mentioned among the report, the care has been insulation in usurping large data approaches, that will be a incomprehensible state of affairs, since it completely was already countable by the ECU organization in 2012 that unit of time of all the electronic data storage among the globe was occupied by the care trade. It’s evident that within existing mounds of large data there is hidden knowledge which may modification the life of a patient or, at associate degree awfully big extent, modification the world itself. Extracting this knowledge is that the fastest, least dear and best path to rising peoples’ health. huge knowledge technologies will definitely open new opportunities and alter
eakthroughs involving, among the others care data analytics addressing wholly totally different perspectives: (i) descriptive to answer what's already happened, (ii) diagnostic to answer the explanation why it's happened, (iii) prognostic to understand what is going on to be happen and (iv) prescriptive to get but we'll produce it happen. It’s out of any doubt that the potential impact large data technology can cause technology, economic and society has connectedness, boosting innovations in organizations and leading to the event of business models. This paper will demonstrate that huge knowledge technologies have the potential to unlock vast productivity bottlenecks and radically improve the quality and accessibility of the care system and discuss steps that need to be taken towards this goal.
The speedily aging population is contributory to the ever increasing demands as chronic diseases are additional rife within the older. The quantity of individuals aged eighty five years and older is projected to rise from fourteen million to nineteen million by 2020 and to forty million by 2050. The impact of those ever increasing demands is clearly illustrated by a study conducted by Accenture organization in 2014 that found that a 1/3 of European hospitals had rumored operative losses. This solely exace
ates the very fact that countries in Europe are finding it progressively difficult to supply sensible quality care at an inexpensive value to their patients once it's needed. The conception of the Triangle of Healthcare is commonly quoted to explain this te
ibly challenge. The elements of constellation are quality, access and price. The efficacy, worth and related outcome of the care replicates the standard of a health care system. Access describes UN agency will receive care once they want it. Value represents the value tag of the care and therefore the affordability of the patients and payers. The matter is that each one the elements are generally in competition with each other within the health care sector. So whereas it's going to be potential to boost anyone or elements, in most of the cases this comes at the expense of the third as illustrated in below Figure [Ganjir et al, 2016].
However, whereas this health care optimization approaches could facilitate introduce minor changes within the balance of the Triangle of Health, solely a radical
eakthrough has the potential to wholly disrupt the Iron Triangle of Health such all elements together with Quality, Access and price are all more optimized at the same time. As long as health care is one among the foremost information intensive industries around, the multitude of high-volume, high selection, high truthfulness and worth of information sources among the health care sector have the potential to disrupt the Triangle. Whereas most of this health care information was antecedently hold on during a text format, this trend is towards conversion of those giant amounts of information, which might facilitate this method.
2. Key words
Health care, big data, analysis methods, optimization, information
3. Introduction to big data in healthcare
Even though there is previously a massive amount of health care information around the world associate degreed whereas it's growing at Associate in nursing exponential rate, nearly all of the knowledge is confine individual silos .The information collected by any doctor clinic or by a hospital is sometimes un
oken within the boundaries of the health care provider. Moreover, information keep within a hospital is rarely integrated across multiple IT systems parenthetically, if we have a tendency to tend to require into consideration all the out there information at a hospital from one patient’s perspective, information regarding the patient will exist inside the EMR system, laboratory, imaging system and prescription databases. The information describing that doctors and nurses attended to the precise patient can exist. However, inside the overwhelming majority of cases, each provided information mentioned here is un
oken in separate silos. Thus account insights and therefore worth from the aggregation of these information sets is not accomplishable at this stage. It’s jointly necessary to grasp that in today’s world a patient’s medical information does not alone reside within the boundaries of a health care provider. The medical insurance and pharmaceuticals industries jointly hold information regarding specific claims and so the characteristics of prescribed drugs severally. a lot of and a lot of, patient-generated information from IoT devices resembling fitness trackers, sign monitors and thought scales are providing essential information regarding the daily life vogue characteristics of a non-public. Insights derived from such information generated by the linking among EMR information, vital data, laboratory information, medication information, to say variety of those and their aggregation, even a great deal of with doctor notes, patient discharge letters, patient diaries, medical publications, specifically linking structured with unstructured information, area unit usually crucial to vogue employment programmers that may facilitate improve peoples’ lifestyles and eventually cut back incidences of chronic illness, medication and hospitalization [Raghupathi & Raghupathi, 2014].
As the tending sector transitions from a volume to value-based care model, it's essential for numerous stakeholders to urge a whole and proper understanding of treatment trajectories of specific patient populations. The only real because of win this can be often to be able to mixture the disparate information sources not merely within one hospital, GP clinic, IT infrastructure, but jointly across multiple tending suppliers, completely different tending players and even consumer-generated information. Such unified information sets would profit not alone every player within the health care business which ends in allowing higher quality care and access to tending at lower prices, but would additionally most importantly profit the patient by providing first time right treatment, supported a property analysis model.
However, achieving such a vision that involves the mix of such disparate tending information sets (in terms of data coarseness, quality, type (e.g. ranging from free text, images, (streaming) detector information to structured information sets) poses major legal, business Associate in Nursing technical challenges from an data perspective, in terms of the quantity, variety, honesty and speed of the knowledge sets. the only real because of successfully address these challenges is to utilize large information technologies.
“Big data” contains a large choice of definitions within the field of health analysis. However, a viable definition of what large information suggests that for health is that the following: “Big information in health” encompasses high volume, high diversity biological, clinical, environmental, and life vogue information collected from single folks to large cohorts, in respect to their health and welfare standing, at one or several time points. a great deal of general definition of huge information refers to “knowledge sets whose size is on the so much facet the ability of typical information software package tools to capture, store, manage and analyze”. This definition puts the accent on the scale and volume aspect but, as we have a tendency to tend to pictured on high of, the scale unit of measurement many: choice by handling with a multiplicity of types, sources and format, information honesty regarding the quality and validity of these information, and information speed convenience in real time). To boot, there unit of measurement various factors that need to even be thought of resembling information attribute, information protection, and privacy attributable to the sensitivity of data managed. From these aspects end in the need for complete
ight algorithms, techniques and approaches to handle these new challenges.
4. Applied methods with respect to tools for processing of healthcare data
The screen shots of data selection https: in field of healthcare are shown below:
When the view button of database is clicked by me, I am able to view the desired data set of healthcare. The occu
ed screen to represent this process is supplied below:
After clicking the view data set in occu
ed page, the required data can be used by me or any other for different implementation and analysis works. The occu
ed screen to represent this process is supplied below:
The ngram viewer available on Google is used by me to make a graph for showing my analysis in a certain time frame. As this required data is publically and freely available, so that, I can create my analysis work on easy bases. The create graph with ngram is represented below:
This analysis work of big data implementation on the field of health care is solo conducted by myself only. It is possible that any other researcher may also use the same data as it is publically available. The deep learning approach is applied by me for this analysis work which helps me to create a hierarchy according to different levels of data sets.
Deep learning usually refe
ed to the algorithms set describing Machine Learning which deduce bottomless hierarchical models which capture relationships of very non-linear from low level (unstructured) computer file to form ideas of high level. The advantage of these deep learning algorithms is that they'll be parallelized to change the analysis of te
ibly and really refined knowledge, love medical photos or videos, text data, or utterly completely different unstructured information. Parenthetically,...

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