What is big data? Interpretation of AI/ ML in big data analytics – Pubrica

1. WHATIS BIG DATA. DISCUSS THE INTERPRETATION OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNINGINBIGDATAANALYTICS An Academic presentationby Dr.NancyAgnes,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline In-Brief Introduction Development of BigData ArtificialIntelligencevsBigDataAnalytics Conclusion

3. 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 abilityto analyze and interpret those data. Each data can benefit from the other, and it can improve clinical practice is explained briefly in pubrica blogforClinical biostatisticsservices.

4. Introduction Advancements in digital technology have createdto develop the ability to multiplex measurements on a singlesample. It may provide in hundreds, thousands or even millions of sizes being produced concurrently, alwayscombining technologies to give rapid measures ofDNA,protein, RNA, function along with the clinical features including measures of disease, progression and relatedmetadata. “Big data” is best considered of itspurpose. Contd..

5. The ultimate characteristic of such experimental approaches is not the vast scaleof measurement but the hypothesis-free method to the experimentaldesign. In this blog, we define “Big data” experiments as hypothesis-generating ratherthan hypothesis-driven studies. They inevitably involve rapid measurement of many variables and aretypically “Bigger” than their counterparts driven by a priorhypothesis. They probe the unknown workings of complex systems: if we can measure it alland do so in an attempt to describe it, maybe we can understand itall. Contd..

6. This approach is less dependent on prior information and has moresignificant potential to indicate unsuspected pathways relevant to disease inbiostatistics consultingservices. In contrast, others argued that new techniques were an irrelevant distractionfrom establishedmethods. Hypothesis-generating systems are not only synergistic with traditionalmethods, but they are also dependent uponthem. In this way, Big data analyses are useful to ask novel questions, withconventional experimental techniques remaining just as relevant for testing them byusing Statistical Programming Services.

7. Development of BigData The development of Big datahas drastically approaching to enhance our ability to probe the“parts” of biology may bedefective. The goal of precision medicine aims leads the approach one step by making that informationof practical value to theclinician. Precision medicine can be briefly defined as an approach to provide the right treatments to theright patients at the righttime. Contd..

8. For most clinical problems, precision strategies remainyearning. The challenge of reducing biology to its parts, then analyzing which must be measured to choose an optimal intervention, the patient population will getbenefits. Still, the increasing use of hypothesis-free, Big data approaches promises tohelp us reach this aspirational goal using medical biostatisticalServices. Contd..

10. Artificial Intelligence vsBig The health care improvements brought by theapplication of Big data techniques in are mostly 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 datasets. Data Analytics One such place is in clinical imagingfor biostatisticsfor clinical researchwhere data is invariably digitizedand housed in dedicated picture archivingsystems. Also, this imaging data is connected with clinical datain the form of image reports, the electronic health record and also carries its extensivedata. Contd..

11. 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 provideclinical benefit at leastexperimentally. The requirement of the computing techniques in part reflects the need to extracthidden information from images which are not readily available from the originaldatasets. These techniques are opposite to parametric data within the clinical record, including physiological readings such as pulse rate or results from blood tests or bloodpressure. The need for similar data processing in digitized pathology image specimensis present with the help of biostatistics consultingfirms. Contd..

12. Big data may provide annotated data sets to be used to train artificialintelligence algorithms to recognize clinically relevant conditions orfeatures. For the algorithm to learn the relevant parts, which are not pre-programmed, significant numbers of cases with the element or disease under scrutiny arerequired. Subsequently, similar, but different large volumes of patients to test thealgorithm against standard goldannotations. After they are trained to an acceptable level, these techniques have the opportunityto provide pre-screening of images with a high likelihood of disease to look for cases, allowing prioritization of formalreading. Contd..

13. The Screening tests such as breast mammography will undergo pre-reading by artificial intelligence/machine learning to identify the few positive issues amongmany regular studies allowing rapididentification. Pre-screening of the complex in high acuity cases allows a focused approachto identify and review areas of concern Quantification of structures within amedical image such as tumour volume, monitoring growthor cardiac ejection volumeor response to therapy, or following heart attack,to manage drug therapy of heartfailure will be incorporated into artificial intelligencealgorithms. They are undertaken automatically rather than requiring detailed segmentation ofthe structures obtained from the statistics in clinicaltrials Contd..

14. The artificial intelligence continues to improve, and it can recognize image features regardless of any pre-training through the significances of artificial andconvolutional neural networkswhich can assimilate different sets of medicaldata. The resulting algorithms will be applied to similar, new clinical information topredict individual patient responses based on large prior patientcohorts. Alternatively, similar techniques can be used for images to identifysubpopulations that are otherwise very complextolocate. Contd..

15. The artificial intelligence may find a role in hypothesis production by identifying, unique image features or a combination of components or unrecognized imagethat relate to diseaseoutcome. A subset of patients with loss of memory that potentially performs to dementiamay have features detectable before symptomdevelopment. 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 thedegenerative braindisease/disorder. Contd..

16. 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 andtransformation of new advances into the clinicaldomain. These problems address before the successful application of thesenew methodologies using biostatistics in clinicaltrials.

17. Conclusion The field of biomedical research has seen a detonation in recent years, with a variety ofinformation available, that has collectively known as “Bigdata.” It is a hypothesis-generating method to science bestin consideration, but rather a complementary meansof identifying and inferring meaning from patterns indata. Contd..

18. An increasing range of “artificial intelligence” methods allow thesepatterns to be directly learned from the data itself, rather than pre-specifiedby researchers depending on priorknowledge. Together, these advances are cause for significant development inmedical sectors with the biostatistics Support Services inPubrica.

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