big data & hadoop applications in healthcare
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Applications of BIG Data in Healthcare
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Introduction to Big Data
Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications
Systems / Enterprises generate huge amount of data from Terabytes to Petabytes
Understanding Big Data can give you innovative solutions to various queries.
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Applications of BIG Data in Healthcare
• Early Disease Detection• Personalized Medicine• Clinical Trials• Increased Profitability via Detection of High Cost Patients
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Early Disease Detection
BIG Data analytics can predict the advent of epidemics. It can go one step ahead and also predict the location of carriers. This was done in the case of Ebola to negate its spread.
Even in terms of personal care, body composition data can predict the side-effects of certain medicine as well as the on-set and spread of a disease. This is crucial information for doctors.
Big Data Applications in Healthcare
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Personalized Medicine
BIG Data is making personalized healthcare a reality. This involves creating a genetic & body composition profile for an individual based on which a customized healthcare plan is created. The plan is also integrated with social & lifestyle habits.
This aids the prediction & on-set of disease as well as provides recommendations regarding the best course of treatment.
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Applications of BIG Data in Healthcare
Increased Profitability via Detection of High Cost Patients
A minority group of patients account for major healthcare spending, for example merely 5% of patients in US account for more than half of healthcare spending. How can a hospital ensure they get a share of these patients?
Hospitals can utilize BIG Data to track patient lifecycle accruals based on their health patterns. Once that shortlist is ready, they just have to ensure a high standard of healthcare & customer service.
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Clinical Trials
BIG Data can be used for on-scale clinical & drug trials. This starts with the selection of a sample set, doctors can scan through medical profiles which meet their requirement. Then, BIG Data can enable real-time exhaustive tracking & reporting which was impossible earlier. This has increased the trial success rate as well as ensured lapses do not occur from both sides.
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Current Examples of Big Data Application
IBM and Apple Apple & IBM are collaborating on a Big Data health platform that will allow iPhone and Apple Watch users to
share data to IBM’s Watson Health, a cloud analytics service. The objective is to access data regarding real-time activity which can serve as a data-bank for the discovery of new medical and fitness insights. Users will also have the option to directly share this data with their doctors.
Pfizer Pfizer developed Xalkori, a drug approved by the FDA in 2011 specifically for lung cancer patients via BIG Data
analytics. They combed through data regarding genomic profiles, clinical trials and EMR reports to develop the drug. Pfizer is also exploring the possibility of developer drugs for smaller patient groups based on their health & lifestyle activities.
Carolina Healthcare System They run more than 900 care centers which are visited by thousands of patients everyday. After mining through
the patient database, they developed an algorithm to immediately identify high-risk patients so that doctors can provide them with priority healthcare.
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How Are Insights Derived from BIG Data?
Say Hello to Hadoop Development :)
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Introduction to Hadoop
Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of commodity computers using a simple programming model.
It is an Open-Source Data Management technology with extensive storage and distributed processing.
Hadoop CharacteristicsFlexible
Reliable
Economical
Scalable Get Started with BIG Data & Hadoop
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Why Should You Learn Hadoop?
• It uses a simple programming model to process large data sets across clusters of computers.
• It has become the standard for storing, processing and analyzing big data.
• It is cheaper to use in comparison to other traditional proprietary technologies such as Oracle, IBM etc. [It can run on low cost commodity hardware]
• It is the big data of unstructured data from multiple sources.
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Reports – 2015
Here are some of the other Reports that convey the same message:
• “It is expected that Hadoop and Big Data Analytics will grow to around $13.9 billion from 2012 to 2017.” (Markets and Markets Research Report)
• “The Data market currently with the fastest growth are Hadoop and NoSQL software and services.” (Technology Research Organization, Wikibon)
• “The Big Data market will likely hit $23.8 billion with a 31.7% rise per year.” (IDC Report)
• “Almost 90% organizations have embarked on Hadoop related projects and thus Hadoop skills are in huge demand.” (According to the Big Data Executive Survey 2013)
• “Companies hiring Hadoop professionals Analytics professionals obtain a 250 percent hike in their salaries.” (According to Analytics Industry Report)
Gartner had predicted, “Hadoop will be in most advanced analytics products by 2015.” By far and large, this prediction has turned out to be true. Hadoop is “The Technology” to harness Big Data
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Predictions for 2015
According to the Forrester Report:
• A new data economy will arise, Hadooponomics. Due to its vast data prowess, enterprise adoption of Hadoop will become compulsory. All those companies, which were unsure of adopting Hadoop till 2014, are bound to adopt it in 2015
• The surge in demand for Hadoop job professionals will be fulfilled by Java professionals & other software engineers who will upgrade their skills
• The creation of SQL-on-Hadoop options will further enhance usability & adoption
• Due to the introduction of Yarn (Version 2.0), the usage of Hadoop will go beyond analytics. It will transform into an application platform giving birth to new frameworks in the Hadoop ecosystem
• New Hadoop sources will emerge from organizations like Oracle, HP, Tibco and SAP. Vendors such as Red Hat, VMware and Microsoft will include Hadoop in their operating Systems
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Course Topics
Module 1
Introduction to Big Data and Hadoop
Module 2
HDFS Internals, Hadoop
Configurations and Data Loading
Module 3
Introduction to Map Reduce
Module 4
Advanced Map Reduce Concepts
Module 5
Introduction to Pig
Module 6
Advanced Pig and Introduction to Hive
Module 7
Advanced Hive Concepts
Module 8
Extending Hive and HBase Introduction
Module 9
Advanced HBase and Oozie Introduction
Module 10
Project Set-up Discussion
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