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Big Data Analytics inUSA and Turkey MotivationCan we learn from the past tobecome better in the.Healthcare Data isbecoming more complex In 2012 worldwide digital healthcaredata was estimated to be equal to500 petabytes and is expected to.reach 25 000 petabytes in 2020 Hersh W Jacko J A Greenes R Tan J Janies D Embi P J Payne P R 2011 Health care hit or miss Nature 470 7334 327 Organization of this Tutorial Introduction Motivating Examples. Sources and Techniques for Big Data inHealthcare Structured EHR Data Unstructured Clinical Notes Medical Imaging Data. Genetic Data Other Data Epidemiology Behavioral Final Thoughts and Conclusion INTRODUCTION Definition of Big Data. A collection of large and complex data sets which are difficult toprocess using common database management tools ortraditional data processing applications Big data refers to the tools Volumeprocesses and procedures allowing.an organization to create manipulate and manage very largedata sets and storage facilities according to zdnet com Velocity VarietyBig data is not just about size . Finds insights from complex noisy DATheterogeneous longitudinal and Avoluminous data It aims to answer questions thatwere previously unanswered .The challenges include capturing Veracitystoring searching sharing The four dimensions V s of Big Data 5analyzing Volume of data.ProteomicsDB 8 covers 92 18 097 of 19 629 of known humangenes that are annotated in the Swiss Prot database ProteomicsDB has a data volume of 5 17 TB Volume of dataData from millions of patients have already been collected and stored in.an electronic format and these accumulated data could potentiallyenhance health care services and increase research opportunities 10 11Visible Human Project which has archived 39 GB of femaledatasets 12 variety of data types and structures .For example sequencing technologies produce omics datasystematically at almost all levels of cellular components fromgenomics proteomics and metabo lomics to protein interaction andphenomics 13Much of the data that are unstructured14 eg notes from EHRs 15 16.clinical trial results 17 18 medical images 19 and medical sensors provide many opportunities and a unique challenge to formulate newinvestigations velocityrefers to producing and processing data .The new generation of sequencing technologies enables the productionof billions of DNA sequence data each day at a relatively low cost Because faster speeds are required for gene sequencing 1 20 big datatechnologies will be tailored to match the speed of producing data as isrequired to process them. VeracityVeracity is important for big data as for example personal healthrecords may contain typographical errors abbreviations andcryptic notes Ambulatory measurements are sometimes taken within less reliable .uncontrolled environments compared to clinical data which arecollected by trained practi tioners The use of spontaneous unmanaged data such as those from socialmedia can lead to wrong predictions as the data context is not alwaysFurthermore sources are often biased toward those young internet.savvy and expressive online Last but not least real value to both patients and healthcare systemscan only be realized if challenges to analyze big data can be addressedin a coherent fashion It should be noted that many of the underlying principles of big data.have been explored by the research community for years in otherNevertheless new theories and approaches are needed for analyzingbig health data The total projected healthcare spending in the UK by 2021 will make6 4 of the gross domestic product GDP whilst the total projected.healthcare share of the GDP in the United States is expected to reach19 9 by 2022 4 Big Data TechnologiesIn most of the cases reported we found multiple technologies that wereused together such as artificial intelligence AI along with.Hadoop 24 and data mining tools Parallel computingIn recent years novel parallel computing models such asMapReduce25 by Google have been proposed for a new big datainfrastructure .More recently an open source MapReduce package called Hadoop24was released by Apache for distributed data management The Hadoop Distributed File System HDFS supports concurrentdata access to clustered machines Hadoop based services can also be viewed as cloud computing.platforms which allow for centralized data storage as well as remoteaccess across the Internet cloud computingcloud computing is a novel model for sharing con figurablecomputational resources over the network26 and can serve as an.infrastructure platform and or software for providing an integrated Reasons for Growing Complexity Abundance of Healthcare Data Standard medical practice is moving from relatively ad hoc andsubjective decision making to evidence based healthcare More incentives to professionals hospitals to use EHR technology .Additional Data Sources Development of new technologies such as capturing devices sensors and mobile applications Collection of genomic information became cheaper Patient social communications in digital forms are increasing . More medical knowledge discoveries are being accumulated Big Data Challenges in Healthcare Inferring knowledge from complex heterogeneous patientsources Leveraging the patient data correlations in longitudinal Understanding unstructured clinical notes in the right context . Efficiently handling large volumes of medical imaging data andextracting potentially useful information and biomarkers Analyzing genomic data is a computationally intensive task andcombining with standard clinical data adds additional layers ofcomplexity . Capturing the patient s behavioral data through severalsensors their various social interactions and communications 2 Overall Goals of Big Data Analytics in HealthcareElectronic AnalyticsHealth Records Lower costs.Behavioral Evidence InsightsImproved outcomesPublic Health through smarter Take advantage of the massive amounts of data and provide.right intervention to the right patient at the right time Personalized care to the patient Potentially benefit all the components of a healthcare systemi e provider payer patient and management 8 Purpose of this Presentation.Two fold objectives Introduce the data mining researchers to the sources available and thepossible challenges and techniques associated with using big data inhealthcare domain Introduce Healthcare analysts and practitioners to the advancements in the.computing field to effectively handle and make inferences from voluminous andheterogeneous healthcare data The ultimate goal is to bridge data mining and medical informatics communities tofoster interdisciplinary works between the two communities PS Due to the broad nature of the topic the primary emphasis will be on.introducing healthcare data repositories challenges and concepts to datascientists Not much focus will be on describing the details of any particulartechniques and or solutions Disclaimers Being a recent and growing topic there might be several other.resources that might not be covered here Presentation here is more biased towards the data scientists perspective and may be less towards the healthcaremanagement or healthcare provider s perspective Some of the website links provided might become obsolete in.the future Since this topic contains a wide varieties of problems theremight be some aspects of healthcare that might not becovered in the presentation MOTIVATING EXAMPLES. EXAMPLE 1 Heritage Health Prizehttp www heritagehealthprize c... Over 30 billion was spent on unnecessary hospital admissions Identify patients at high risk and ensure they get the treatment they need Develop algorithms to predict the number of days a patient will.spend in a hospital in the next year Health care providers can develop new strategies to care for patientsbefore its too late reduces the number of unnecessary hospitalizations Improving the health of patients while decreasing the costs of care Winning solutions use a combination of several predictive models . EXAMPLE 2 Penalties for Poor Care 30 Day Readmissions Hospitalizations account for more than 30 of the 2trillion annual cost of healthcare in the UnitedStates Around 20 of all hospital admissions occurwithin 30 days of a previous discharge . not only expensive but are also potentially harmful and most importantly they are often preventable Medicare penalizes hospitals that have high rates of readmissionsamong patients with heart failure heart attack and pneumonia Identifying patients at risk of readmission can guide ef cient resource.utilization and can potentially save millions of healthcare dollars each Effectively making predictions from such complex hospitalization datawill require the development of novel advanced analytical models 26 EXAMPE 3 White House unveils BRAIN Initiative The US President unveiled a new bold 100 million.research initiative designed to revolutionize ourunderstanding of the human brain BRAIN Brain Researchthrough Advancing Innovative Neurotechnologies Initiative Find new ways to treat cure and even prevent braindisorders such as Alzheimer s disease epilepsy and.traumatic brain injury Every dollar we invested to map the human genome returned 140 to oureconomy Today our scientists are mapping the human brain to unlock theanswers to Alzheimer s President Barack Obama 2013 State of the Union . advances in Big Data that are necessary to analyze the huge amounts ofinformation that will be generated and increased understanding of howthoughts emotions actions and memories are represented in the brain NSF Joint effort by NSF NIH DARPA and other private partners http www whitehouse gov infogr... 14. EXAMPLE 4 GE Head Health ChallengeChallenge 1 Methods for Diagnosis and Prognosis ofMild Traumatic Brain Injuries Challenge 2 The Mechanics of Injury InnovativeApproaches For Preventing And Identifying Brain.In Challenge 1 GE and the NFL will award up to 10M fortwo types of solutions Algorithms and Analytical Tools and Biomarkers and other technologies A total of 60M infunding over a period of 4 years 15 Healthcare Continuum.Sarkar Indra Neil Biomedical informatics and translational medicine Journal of Translational Medicine8 1 2010 22 16 Data Collection and AnalysisEffectively integrating and efficiently analyzing various forms of healthcare data overa period of time can answer many of the impending healthcare problems .Jensen Peter B Lars J Jensen and S ren Brunak Mining electronic health records towardsbetter research applications and clinical care Nature Reviews Genetics 2012 17 Organization of this Presentation Introduction Motivating Examples. Sources and Techniques for Big Data inHealthcare Structured EHR Data Unstructured Clinical Notes Medical Imaging Data. Genetic Data Other Data Epidemiology Behavioral Final Thoughts and Conclusion SOURCES AND TECHNIQUESFOR BIG DATA IN HEALTHCARE. Electronic Health Records EHR data Healthcare Analytic Platform Resources ELECTRONIC HEALTHRECORDS EHR DATA.Clinical data Structured EHR Unstructured MedicalGenomic data Behavior data. DNA sequences Social network Mobility sensor Billing data ICD codes ICD stands for International Classification of Diseases ICD is a hierarchical terminology of diseases signs symptoms .and procedure codes maintained by the World Health Organization In US most people use ICD 9 and the rest of world use ICD 10 Pros Universally available Cons medium recall and medium precision forcharacterizing patients. 250 Diabetes mellitus 250 0 Diabetes mellitus without mention of complication 250 1 Diabetes with ketoacidosis 250 2 Diabetes with hyperosmolarity 250 3 Diabetes with other coma. 250 4 Diabetes with renal manifestations 250 5 Diabetes with ophthalmic manifestations 250 6 Diabetes with neurological manifestations 250 7 Diabetes with peripheral circulatory disorders 250 8 Diabetes with other specified manifestations. 250 9 Diabetes with unspecified complication Billing data CPT codes CPT stands for Current Procedural Terminology created bythe American Medical Association CPT is used for billing purposes for clinical services.can guide efï¬cient resource utilization and can potentially save millions of healthcare dollars eachyear. Effectively making predictions from such complex hospitalization datawill require the development of novel advanced analyticalmodels.