What is big data. Interpretation of AI ML in big data analytics - Pubrica
                     W H A T  I S  B I G  D ATA .
D I S C U S S  T H E  I N T E R P R E TAT I O N  O F   
A R T I F I C I A L  I N T E L L I G E N C E /  M A C H I N E  
 L E A R N I N G  I N  B I G  D A T A  A N A LY T I C S
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, 
Pubrica  Group: www.pubrica.com
Email: [email protected]
Today's Discussion
Outline
In-Brief  
Introductio
n
Developme
nt of Big 
Data
Artificial Intelligence vs Big Data 
Analytics  Conclusion
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
b iostatistics services.
Introduction
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.
Contd.
.
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.
Contd.
.
This approach is less dependent on prior information and has more significant 
 potential to indicate unsuspected pathways relevant to disease in biostatistics
c onsulting services.
In contrast, others argued that new techniques were an irrelevant distraction from 
 established methods.
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.
Development 
The d evelopment of Big data has drastically  
 of Big Data 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.
Contd.
.
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.
Contd.
.

Artificial The health care improvements brought by the application 
Intelligence  of Big data techniques in are mostly to transform into  
clinical practice, the possible benefits of doing so can be  
 vs Big seen in those clinical areas already with large, readily  
Data available and usable data sets.
Analytics
One such place is in c linical 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.
Contd.
.
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.
Contd.
.
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 disease to look for cases,  
allowing prioritization of formal reading.
Contd.
.
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 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 a rtificial intelligence algorithms.
They are undertaken automatically rather than requiring detailed segmentation of the 
 structures obtained from the statistics in clinical trials
Contd.
.
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.
Contd.
.
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.
Contd.
.
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.
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.
Contd.
.
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.
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