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What is Big Data. Discuss the Interpretation of Artificial Intelligence/

Machine Learning in Big Data Analytics

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In-Brief

Over a decade, the “Big data” showcases

the rapid increase in variety and volumeof

information, particularly in medical

research. As scientists, rapidly generate,

store and analyze data that would have

taken many years to compile. “Big data”

means expanded and large data volume,

possess increasing ability to analyze and

interpret those data. Each data can benefit

from the other, and it can improve clinical

practice is explained briefly in pubrica blog

for Clinical biostatistics services.

Keywords:

Clinical biostatistics services, biostatistics

consulting services, biostatistics CRO,

Statistical Programming Services,

BiostatisticalServices, biostatistics

consulting firms, Biostatistics for clinical

research, statistics in clinical trials,

biostatistics in clinical trials, Biostatistics

CRO, Biostatistics Support Services

I. INTRODUCING BIG DATA

Advancements in digital technology have

created to develop the ability to multiplex

measurements on a single sample. It may

provide in hundreds, thousands or even

millions of sizes being produced

concurrently, always combining

technologies to give rapid measures of

DNA,protein, RNA, function along with the

clinical features including measures of

disease, progression and related metadata.

“Big data” is best considered of its purpose.

The ultimate characteristic of such

experimental approaches is not the vast scale

of measurement but the hypothesis-free

method to the experimental design. In this

blog, we define “Big data” experiments as

hypothesis-generating rather than

hypothesis-driven studies. They inevitably

involve rapid measurement of many

variables and are typically “Bigger” than

their counterparts driven by a prior

hypothesis. They probe the unknown

workings of complex systems: if we can

measure it all and do so in an attempt to

describe it, maybe we can understand it all.

This approach is less dependent on prior

information and has more significant

potential to indicate unsuspected pathways

relevant to disease in biostatistics consulting

services

In contrast, others argued that new

techniques were an irrelevant distraction

from established methods. With history, it is

clear that neither extreme was accurate.

Hypothesis-generating systems are not only

synergistic with traditional methods, but

they are also dependent upon them. In this

way, Big data analyses are useful to ask

novel questions, with conventional

experimental techniques remaining just as

relevant for testing them by using Statistical

Programming Services

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II. DEVELOPMENT OF BIG DATA

The development of Big data has drastically

approaching to enhance our ability to probe

the “parts” of biology may be defective. The

goal of precision medicine aims leads the

approach one step by making that

information of practical value to the

clinician. Precision medicine can be briefly

defined as an approach to provide the right

treatments to the right patients at the right

time. For most clinical problems, precision

strategies remain yearning. The challenge of

reducing biology to its parts, then analyzing

which must be measured to choose an

optimal intervention, the patient population

will get benefits. Still, the increasing use of

hypothesis-free, Big data approaches

promises to help us reach this aspirational

goal using medical biostatistical Services

III. ARTIFICIAL INTELLIGENCE VS BIG

DATA ANALYTICS

The health care improvements brought by

the application of Big data techniques in are

still, mostly, yet to transform into clinical

practice, the possible benefits of doing so

can be seen in those clinical areas already

with large, readily available and usable data

sets. One such place is in clinical imaging

for biostatistics for clinical research where

data is invariably digitized and housed in

dedicated picture archiving systems. Also,

this imaging data is connected with clinical

data in the form of image reports, the

electronic health record and also carries its

extensive data. Due to the ease of handling

of this data, it has been easy to show, that

artificial intelligence via machine learning

techniques, can exploit big data to provide

clinical benefit at least experimentally. The

requirement of the computing techniques in

part reflects the need to extract hidden

information from images which are not

readily available from the original datasets.

These techniques are opposite to parametric

data within the clinical record, including

physiological readings such as pulse rate or

results from blood tests or blood pressure.

The need for similar data processing in

digitized pathology image specimens is

present with the help of biostatistics

consulting firms.

Big data may provide annotated data sets to

be used to train artificial intelligence

algorithms to recognize clinically relevant

conditions or features. For the algorithm to

learn the relevant parts, which are not pre-

programmed, significant numbers of cases

with the element or disease under scrutiny

are required. Subsequently, similar, but

different large volumes of patients to test the

algorithm against standard gold annotations.

After they are trained to an acceptable level,

these techniques have the opportunity to

provide pre-screening of images with a high

likelihood of diseaseto look for cases,

allowing prioritization of formal reading.

The Screening tests such as breast

mammography will undergo pre-reading by

artificial intelligence/machine learning to

identify the few positive issues among many

regular studies allowing rapid identification.

Pre-screening of the complex in high acuity

cases allows a focused approach to identify

and review areas of concern Quantification

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of structures within a medical image such as

tumour volume, monitoring growthor

cardiac ejection volume or response to

therapy, or following heart attack,to manage

drug therapy of heart failure will be

incorporated into artificial intelligence

algorithms. They are undertaken

automatically rather than requiring detailed

segmentation of the structures obtained from

the statistics in clinical trials

The artificial intelligence continues to

improve, and it can recognize image features

regardless of any pre-training through the

significances of artificial and convolutional

neural networks which can assimilate

different sets of medical data. The resulting

algorithms will be applied to similar, new

clinical information to predict individual

patient responses based on large prior

patient cohorts. Alternatively, similar

techniques can be used for images to

identify subpopulations that are otherwise

very complex tolocate. The artificial

intelligence may find a role in hypothesis

production by identifying, unique image

features or a combination of components or

unrecognized image that relate to disease

outcome. A subset of patients with loss of

memory that potentially performs to

dementia may have features detectable

before symptom development. This

approach allows massive volume population

interrogation with prospective clinical

follow-up and identification of the most

clinically relevant image fingerprints, rather

than analyzing retrospective data in patients

already having the degenerative brain

disease/disorder.

Even after the vast wealth of data contained

in the clinical information technology

systems within hospitals, the extraction of

medical usage data from the clinical domain

is not a trivial task, for several diverse

reasons including philosophy of data

handling, the data format, biological data

handling infrastructure and transformation

of new advances into the clinical domain.

These problems address before the

successful application of these new

methodologies using biostatistics in clinical

trials.

IV. CONCLUSION

The field of biomedical research has seen a

detonation in recent years, with a variety of

information available, that has collectively

known as “Big data.” It is a hypothesis-

generating method to science best in

consideration, but rather a complementary

means of identifying and inferring meaning

from patterns in data. An increasing range of

“artificial intelligence” methods allow these

patterns to be directly learned from the data

itself, rather than pre-specified by

researchers depending on prior knowledge.

Together, these advances are cause for

significant development in medical sectors

with the biostatistics Support Services in

Pubrica.

REFERENCES

1. Hulsen, T., Jamuar, S. S., Moody, A. R., Karnes, J. H.,

Varga, O., Hedensted, S., ... & McKinney, E. F.

(2019). From big data to precision medicine. Frontiers

in Medicine, 6, 34.

2. Zikopoulos, P., & Eaton, C. (2011). Understanding

big data: Analytics for enterprise-class Hadoop and

streaming data. McGraw-Hill Osborne Media.