SlideShare a Scribd company logo
1 of 68
Download to read offline
The present and future role of computers in medicine,[object Object],Ian Foster,[object Object],Computation Institute,[object Object],Argonne National Lab & University of Chicago,[object Object]
Credits,[object Object],Thanks for support from,[object Object],Chan Soon-Shiong Foundation,[object Object],Department of Energy,[object Object],National Institutes of Health,[object Object],National Science Foundation,[object Object],And for many helpful conversations, Carl Kesselman, Jonathan Silverstein, Steve Tuecke, Stephan Erberich, Steve Graham, Ravi Madduri, and Patrick Soon-Shiong,[object Object]
Biology is shifting from being an observational science to a quantitative molecular science,[object Object],   Old biology: measure one/two things in two/three conditions,[object Object],High cost per measurement,[object Object],Analysis straightforward as little data,[object Object],Enormously difficult to work out pathways due to inadequate data,[object Object],   New biology: measure 10,000 things under many conditions,[object Object],Low cost per measurement,[object Object],Analysis no longer straightforward,[object Object],Payoff can be bigger: potential to understand a complex system,[object Object],Ajay Jain, UCSF,[object Object]
Change health care ,[object Object],from ,[object Object],an empirical, qualitative systemof silos of information,[object Object],to a model of,[object Object],predictive, quantitative, shared,evidence-based outcomes ,[object Object]
The health care information technology chasm,[object Object], Health care IT [is] rarely used to provide clinicians with evidence-based decision support and feedback; to support data-driven process improvement; or to link clinical care and research.,[object Object],Computational Technology for Effective Health Care, NRC, 2009,[object Object]
AAPM Foster July 2009
AAPM Foster July 2009
AAPM Foster July 2009
Digital power =,[object Object],computing x communicationxstorage x content,[object Object],Moore’s law,[object Object],doubles,[object Object], every 18,[object Object], months,[object Object],   disk law,[object Object],doubles,[object Object],x  every 12,[object Object],    months,[object Object],       fiber law,[object Object],doubles,[object Object],xevery9,[object Object],       months,[object Object],community law,[object Object],n,[object Object],x    2,[object Object],    where n is    # people,[object Object],John SeelyBrown,[object Object]
(Intel),[object Object]
Microprocessor trends,[object Object],AMD,[object Object],Dual core (April 2005),[object Object],Quad core (October 2007),[object Object],Intel,[object Object],Dual core (July 2005),[object Object],Quad core (December 2006),[object Object],Sun,[object Object],Niagara: 8 cores * 4 threads/core (November 2005),[object Object],Niagara2: 8 cores * 8 threads/core (August 2007),[object Object],IBM POWER6,[object Object],2 cores * 4 threads/core (May 2007),[object Object],Tilera 64 cores,[object Object],Dan Reed, Microsoft,[object Object],11,[object Object]
Marching towards manycore,[object Object],Intel’s 80 core prototype,[object Object],2-D mesh interconnect,[object Object],62 W power,[object Object],Tilera 64 core system,[object Object],8x8 grid of cores,[object Object],5 MB coherent cache,[object Object],4 DDR2 controllers,[object Object],2 10 GbE interfaces,[object Object],IBM Cell,[object Object],PowerPC and 8 cores,[object Object],Dan Reed, Microsoft,[object Object],12,[object Object]
1E+17,[object Object],multi-Petaflop,[object Object],Petaflop,[object Object],Blue Gene/L,[object Object],1E+14,[object Object],Thunder,[object Object],Red Storm,[object Object],Earth,[object Object],Blue Pacific,[object Object],ASCI White, ASCI Q,[object Object],SX-5,[object Object],ASCI Red Option,[object Object],ASCI Red,[object Object],T3E,[object Object],SX-4,[object Object],NWT,[object Object],CP-PACS,[object Object],1E+11,[object Object],CM-5,[object Object],Paragon,[object Object],T3D,[object Object],Delta,[object Object],SX-3/44,[object Object],Doubling time = 1.5 yr.,[object Object],i860 (MPPs),[object Object],VP2600/10,[object Object],SX-2,[object Object],CRAY-2,[object Object],Y-MP8,[object Object],S-810/20,[object Object],X-MP4,[object Object],Peak Speed (flops),[object Object],Cyber 205,[object Object],X-MP2 (parallel vectors),[object Object],1E+8,[object Object],CRAY-1,[object Object],CDC STAR-100 (vectors),[object Object],CDC 7600,[object Object],ILLIAC IV,[object Object],CDC 6600 (ICs),[object Object],IBM Stretch,[object Object],1E+5,[object Object],IBM 7090 (transistors),[object Object],IBM 704,[object Object],IBM 701,[object Object],UNIVAC,[object Object],ENIAC (vacuum tubes),[object Object],1E+2,[object Object],1940,[object Object],1950,[object Object],1960,[object Object],1970,[object Object],1980,[object Object],1990,[object Object],2000,[object Object],2010,[object Object],Year Introduced,[object Object],The evolution of the fastest supercomputer,[object Object],Argonne,[object Object],My laptop,[object Object]
The Argonne IBM BG/P,[object Object]
www.top500.org,[object Object],1,[object Object],2,[object Object],3-4,[object Object],>128K,[object Object]
Simulation of the human arterial tree on the TeraGrid,[object Object],G. Karniadakis et al.,[object Object]
AAPM Foster July 2009
Storage costs,[object Object],(PC Magazine, Oct 2, 2007),[object Object]
Informationbig bang,[object Object],All informationper year,[object Object],100Exabytes,[object Object],Uniqueinformationper year,[object Object],All human documentsproduced last 40,000 years(to 1997),[object Object],12 Exabytes,[object Object],    2000       2001     2002      2003      2004     2005     2006      2007     2008      2009,[object Object],© Stuart Card, basedon Lesk, Berkeley SIMS, Landauer, EMC,[object Object]
Growth of Genbank(1982-2005),[object Object],Broad Institute,[object Object]
More data does not always mean more knowledge,[object Object],Folker Meyer, Genome Sequencing vs. Moore’s Law: Cyber Challenges for the Next Decade, CTWatch, August 2006.,[object Object]
The Red Queen’s race,[object Object],  "Well, in our country," said Alice … "you'd generally get to somewhere else — if you run very fast for a long time, as we've been doing.”,[object Object],  "A slow sort of country!" said the Queen. "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!",[object Object]
Computing ondemand,[object Object],Public PUMA knowledge base,[object Object],Information about proteins analyzed against ~2 million gene sequences,[object Object],Back officeanalysis on Grid,[object Object],Millions of BLAST, BLOCKS, etc., onOSG and TeraGrid,[object Object],Natalia Maltsev et al.,[object Object]
AAPM Foster July 2009
AAPM Foster July 2009
1.6 Tbps,[object Object],Cost perGigabit-Mile,[object Object],320 Gbps,[object Object],Moore’sLaw,[object Object],2.5 Gbps,[object Object],50 Mbps,[object Object],1984,[object Object],1994,[object Object],1998,[object Object],2000,[object Object],1993,[object Object],1998,[object Object],2002,[object Object],Opticalnetworkingbreakthrough!,[object Object],Revolution,[object Object],Capacity increase and new economics,[object Object],Nortel,[object Object]
Optical switches,[object Object],Lucent,[object Object]
Empiricism,[object Object],Theory,[object Object],Simulation,[object Object],Data,[object Object],New ways of knowing,[object Object],300 BCE,[object Object],1700,[object Object],1950,[object Object],1990,[object Object],Enhanced by the power of collaboration,[object Object]
AAPM Foster July 2009
Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes,[object Object],Patients with same diagnosis,[object Object]
Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes,[object Object],Non-responderstoxic responders,[object Object],Non-toxic responders,[object Object],Patients with same diagnosis,[object Object],Misdiagnosed,[object Object]
Leukemia and Lymphoma,[object Object],After Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA ,[object Object]
Leukemia and Lymphoma,[object Object],After Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA ,[object Object]
Currently, 17% of Burkitt's Lymphoma are incorrectly diagnosed as Diffuse Large B Cell Lymphoma,[object Object],Classic,[object Object],Burkitt’sLymphoma,[object Object],Atypical,[object Object],Burkitt’sLymphoma,[object Object],Diffuse Large,[object Object],B Cell Lymphoma,[object Object],Louis Staudt, National Cancer Institute,[object Object]
Classic Burkitt Lymphoma    Cure Rate,[object Object],Diagnosis,[object Object],Atypical Burkitt Lymphoma Cure Rate,[object Object],Treatment,[object Object],Patients’ Actual Disease,[object Object],Burkitt’sLymphoma,[object Object],60 %,[object Object],80 %,[object Object],Intensive chemotherapy,[object Object],Burkitt’sLymphoma,[object Object],Diffuse Large B Cell Lymphoma,[object Object],0 %,[object Object],15 %,[object Object],CHOPregimen,[object Object],Burkitt’sLymphoma,[object Object]
Survival estimates for patients with Burkitt's Lymphoma,[object Object],Best treatment for Burkitt’s Lymphoma,[object Object],Best treatment for Diffuse Large B Cell Lymphoma,[object Object],Dave et al, NEJM, June 8, 2006.,[object Object]
Burkitt’s,[object Object],Lymphoma,[object Object],Diffuse Large,[object Object], B-cell Lymphoma,[object Object],Classic   Atypical,[object Object],Louis Staudt, National Cancer Institute,[object Object]
Imaging biomarkers: Diffusion Tensor Imaging and brain injury,[object Object],Kraus et al., Brain (2007), 130, 2508-2519,[object Object]
Enabling quantitative medicine,[object Object],Collect a lot of patient data,[object Object],Analyze data to infer effective treatments,[object Object],Identify personalized treatment plans,[object Object],Clinical practice,[object Object],Basic research,[object Object],Clinical trials,[object Object]
Challenges,[object Object],Increasing volumes of data, types of data: genomics, blood proteins, imaging, …,[object Object],New science and treatments are hidden in the data, not the biology (biomarkers),[object Object],Too much for the individual physician or researcher to absorb,[object Object], … have to pay attention to cognitive support … computer-based tools and systems that offer clinicians and patients assistance for thinking about and solving problems related to specific instances of health care.,[object Object],NRC Report on Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions, 2009,[object Object]
Bridging silos to enable quantitative medicine,[object Object],Basic research,[object Object],ongoing investigative studies,[object Object],Outcomes, tissue bank,[object Object],screening tests,[object Object],pathways,[object Object], library,[object Object],Clinical practice,[object Object],Clinical trials,[object Object],trial subjects, outcomes,[object Object]
Addressing urban health needs,[object Object]
Important characteristics,[object Object],We must integrate systems that may not have worked together before,[object Object],These are human systems, with differing goals, incentives, capabilities,[object Object],All components are dynamic—change is the norm, not the exception,[object Object],Processes are evolving rapidly too,[object Object],We are not building something simple like a bridge or an airline reservation system,[object Object]
Healthcare is acomplex adaptive system,[object Object],A complex adaptive system is a collection of individual agents that have the freedom to act in ways that are not always predictable and whose actions are interconnected such that one agent’s actions changes the context for other agents.,[object Object],Crossing the Quality Chasm, IOM, 2001; pp 312-13,[object Object],[object Object]
Agents are independent and intelligent
Goals and behaviors often in conflict
Self-organization through adaptation and learning
No single point(s) of control
Hierarchical decomp-osition has limited value,[object Object]
We need to function in the zone of complexity,[object Object],Low,[object Object],Chaos,[object Object],Agreement,[object Object],about,[object Object],outcomes,[object Object],Plan and control,[object Object],High,[object Object],Low,[object Object],High,[object Object],Certainty about outcomes,[object Object],Ralph Stacey, Complexity and Creativity in Organizations, 1996,[object Object]
We call these groupingsvirtual organizations (VOs),[object Object],A set of individuals and/or institutions engaged in  the controlled sharing of resources in pursuit of a common goal   ,[object Object],   But U.S. health system is marked by fragmented  and inefficient VOs with insufficient mechanisms for controlled sharing,[object Object],    Healthcare = dynamic, overlapping VOs, linking,[object Object],Patient – primary care,[object Object],Sub-specialist – hospital,[object Object],Pharmacy – laboratory,[object Object],Insurer – …,[object Object],   I advocate … a model of virtual integration rather than true vertical integration … G. Halvorson, CEO Kaiser,[object Object]
The Grid paradigm,[object Object],Principles and mechanisms for dynamic VOs,[object Object],Leverage service oriented architecture (SOA),[object Object],Loose coupling of data and services,[object Object],Open software,architecture,[object Object],Engineering,[object Object],Biomedicine,[object Object],Computer science,[object Object],Physics,[object Object],Healthcare,[object Object],Astronomy,[object Object],Biology,[object Object],1995             2000            2005            2010,[object Object]
The Grid paradigm and healthcare information integration,[object Object],[Grid architecture joint work with Carl Kesselman,   Steve Tuecke, Stephan Erberich, and others] ,[object Object],Manage who can do what,[object Object],Make data usable and useful,[object Object],Platform services,[object Object],Name data and move it around,[object Object],Make data accessible over the network,[object Object],Data sources,[object Object],Radiology,[object Object],Medical records,[object Object],Pathology,[object Object],Genomics,[object Object],Labs,[object Object],RHIO,[object Object]
The Grid paradigm and healthcare information integration,[object Object],Enhance user cognitive processes,[object Object],Security and policy,[object Object],Incorporate into business processes,[object Object],Transform data into knowledge,[object Object],Integration,[object Object],Platform services,[object Object],Management,[object Object],Publication,[object Object],Data sources,[object Object],Radiology,[object Object],Medical records,[object Object],Pathology,[object Object],Genomics,[object Object],Labs,[object Object],RHIO,[object Object]
The Grid paradigm and healthcare information integration,[object Object],Cognitive support,[object Object],Security and policy,[object Object],Valueservices,[object Object],Applications,[object Object],Analysis,[object Object],Integration,[object Object],Platform services,[object Object],Management,[object Object],Publication,[object Object],Data sources,[object Object],Radiology,[object Object],Medical records,[object Object],Pathology,[object Object],Genomics,[object Object],Labs,[object Object],RHIO,[object Object]
We partition the multi-faceted interoperability problem,[object Object],Process interoperability,[object Object],Integrate work across healthcare enterprise,[object Object],Data interoperability,[object Object],Syntactic: move structured data among system elements,[object Object],Semantic: use information across system elements,[object Object],Systems interoperability,[object Object],Communicate securely, reliably among system elements,[object Object],Applications,[object Object],Analysis,[object Object],Integration,[object Object],Management,[object Object],Publication,[object Object]
Publication:Make information accessible,[object Object],Make data available in a remotely accessible, reusable manner,[object Object],Leave mediation for integration layer,[object Object],Gateway from local policy/protocol into wide area mechanisms (transport, security, …),[object Object]
Imaging clinical trials,[object Object],NeuroblastomaCancerFoundation,[object Object],Childrens Oncology Group,[object Object]
Stephan Erberich,,[object Object],Carl Kesselman, et al.,[object Object]
As of Oct19, 2008:,[object Object],122 participants,[object Object],105 services,[object Object],70 data,[object Object],35 analytical ,[object Object]
Data movement in clinical trials,[object Object],(Center for Health Informatics),[object Object]
Community public health:Digital retinopathy screening network,[object Object],(Center for Health Informatics),[object Object]
Integration:Making data usable and useful,[object Object],?,[object Object],Adaptive approach,[object Object],100%,[object Object],Degree of communication,[object Object],Loosely coupled approach,[object Object],Rigid standards-based approach,[object Object],0%,[object Object],0%                          100%,[object Object],  Degree of prior syntactic  and semantic agreement,[object Object]
Integration via mediation,[object Object],[object Object]
Scoped to domain use
Multiple concurrent use
Bottom up mediation
between standards and versions

More Related Content

What's hot

Role of Big Data in Medical Diagnostics
Role of Big Data in Medical DiagnosticsRole of Big Data in Medical Diagnostics
Role of Big Data in Medical DiagnosticsNishant Agarwal
 
Frankie Rybicki slide set for Deep Learning in Radiology / Medicine
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrankie Rybicki slide set for Deep Learning in Radiology / Medicine
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
 
Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...
Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...
Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...NHS England
 
Framework for efficient transformation for complex medical data for improving...
Framework for efficient transformation for complex medical data for improving...Framework for efficient transformation for complex medical data for improving...
Framework for efficient transformation for complex medical data for improving...IJECEIAES
 
HEALTH PREDICTION ANALYSIS USING DATA MINING
HEALTH PREDICTION ANALYSIS USING DATA  MININGHEALTH PREDICTION ANALYSIS USING DATA  MINING
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
 
SMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan Phd
SMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan PhdSMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan Phd
SMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan PhdHealthcare consultant
 
Ijarcet vol-2-issue-4-1393-1397
Ijarcet vol-2-issue-4-1393-1397Ijarcet vol-2-issue-4-1393-1397
Ijarcet vol-2-issue-4-1393-1397Editor IJARCET
 
A novel methodology for diagnosing the heart disease using fuzzy database
A novel methodology for diagnosing the heart disease using fuzzy databaseA novel methodology for diagnosing the heart disease using fuzzy database
A novel methodology for diagnosing the heart disease using fuzzy databaseeSAT Journals
 
IRJET - Comparative Study of Cardiovascular Disease Detection Algorithms
IRJET - Comparative Study of Cardiovascular Disease Detection AlgorithmsIRJET - Comparative Study of Cardiovascular Disease Detection Algorithms
IRJET - Comparative Study of Cardiovascular Disease Detection AlgorithmsIRJET Journal
 
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATADISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATAcseij
 
Big data in IoT for healthcare - www.pepgra.com
Big data in IoT for healthcare - www.pepgra.comBig data in IoT for healthcare - www.pepgra.com
Big data in IoT for healthcare - www.pepgra.comPEPGRA Healthcare
 
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop ClusterIRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop ClusterIRJET Journal
 
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
 
Healthcare data's perfect storm
Healthcare data's perfect stormHealthcare data's perfect storm
Healthcare data's perfect stormHitachi Vantara
 
5 Reasons Why Radiology Needs Artificial Intelligence
5 Reasons Why Radiology Needs Artificial Intelligence5 Reasons Why Radiology Needs Artificial Intelligence
5 Reasons Why Radiology Needs Artificial IntelligenceSimon Harris
 
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONMULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
 
Machine Learning for Disease Prediction
Machine Learning for Disease PredictionMachine Learning for Disease Prediction
Machine Learning for Disease PredictionMustafa Oğuz
 
Disease prediction in big data healthcare using extended convolutional neural...
Disease prediction in big data healthcare using extended convolutional neural...Disease prediction in big data healthcare using extended convolutional neural...
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
 

What's hot (20)

Role of Big Data in Medical Diagnostics
Role of Big Data in Medical DiagnosticsRole of Big Data in Medical Diagnostics
Role of Big Data in Medical Diagnostics
 
Frankie Rybicki slide set for Deep Learning in Radiology / Medicine
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrankie Rybicki slide set for Deep Learning in Radiology / Medicine
Frankie Rybicki slide set for Deep Learning in Radiology / Medicine
 
Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...
Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...
Why does data matter? Professor Stephen Keevil, Head of Medical Physics, Guy’...
 
Framework for efficient transformation for complex medical data for improving...
Framework for efficient transformation for complex medical data for improving...Framework for efficient transformation for complex medical data for improving...
Framework for efficient transformation for complex medical data for improving...
 
HEALTH PREDICTION ANALYSIS USING DATA MINING
HEALTH PREDICTION ANALYSIS USING DATA  MININGHEALTH PREDICTION ANALYSIS USING DATA  MINING
HEALTH PREDICTION ANALYSIS USING DATA MINING
 
SMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan Phd
SMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan PhdSMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan Phd
SMART HEALTH PREDICTION USING DATA MINING by Dr.Mahboob Khan Phd
 
Ijarcet vol-2-issue-4-1393-1397
Ijarcet vol-2-issue-4-1393-1397Ijarcet vol-2-issue-4-1393-1397
Ijarcet vol-2-issue-4-1393-1397
 
A novel methodology for diagnosing the heart disease using fuzzy database
A novel methodology for diagnosing the heart disease using fuzzy databaseA novel methodology for diagnosing the heart disease using fuzzy database
A novel methodology for diagnosing the heart disease using fuzzy database
 
IRJET - Comparative Study of Cardiovascular Disease Detection Algorithms
IRJET - Comparative Study of Cardiovascular Disease Detection AlgorithmsIRJET - Comparative Study of Cardiovascular Disease Detection Algorithms
IRJET - Comparative Study of Cardiovascular Disease Detection Algorithms
 
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATADISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATA
 
Big data in IoT for healthcare - www.pepgra.com
Big data in IoT for healthcare - www.pepgra.comBig data in IoT for healthcare - www.pepgra.com
Big data in IoT for healthcare - www.pepgra.com
 
Artificial Intelligence and Diagnostics
Artificial Intelligence and DiagnosticsArtificial Intelligence and Diagnostics
Artificial Intelligence and Diagnostics
 
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop ClusterIRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop Cluster
 
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
 
Healthcare data's perfect storm
Healthcare data's perfect stormHealthcare data's perfect storm
Healthcare data's perfect storm
 
5 Reasons Why Radiology Needs Artificial Intelligence
5 Reasons Why Radiology Needs Artificial Intelligence5 Reasons Why Radiology Needs Artificial Intelligence
5 Reasons Why Radiology Needs Artificial Intelligence
 
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONMULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
 
Machine Learning for Disease Prediction
Machine Learning for Disease PredictionMachine Learning for Disease Prediction
Machine Learning for Disease Prediction
 
50120140506011
5012014050601150120140506011
50120140506011
 
Disease prediction in big data healthcare using extended convolutional neural...
Disease prediction in big data healthcare using extended convolutional neural...Disease prediction in big data healthcare using extended convolutional neural...
Disease prediction in big data healthcare using extended convolutional neural...
 

Similar to AAPM Foster July 2009

Quantitative Medicine Feb 2009
Quantitative Medicine Feb 2009Quantitative Medicine Feb 2009
Quantitative Medicine Feb 2009Ian Foster
 
Paper id 36201506
Paper id 36201506Paper id 36201506
Paper id 36201506IJRAT
 
EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...
EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...
EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...Servio Fernando Lima Reina
 
Clinical Genomics and Medicine
Clinical Genomics and MedicineClinical Genomics and Medicine
Clinical Genomics and MedicineWarren Kibbe
 
Advantages And Disadvantages Of EHR
Advantages And Disadvantages Of EHRAdvantages And Disadvantages Of EHR
Advantages And Disadvantages Of EHRCarla Jardine
 
ICBO 2014, October 8, 2014
ICBO 2014, October 8, 2014ICBO 2014, October 8, 2014
ICBO 2014, October 8, 2014Warren Kibbe
 
Next generation electronic medical records and search a test implementation i...
Next generation electronic medical records and search a test implementation i...Next generation electronic medical records and search a test implementation i...
Next generation electronic medical records and search a test implementation i...lucenerevolution
 
Real-World Evidence: The Future of Data Generation and Usage
Real-World Evidence: The Future of Data Generation and UsageReal-World Evidence: The Future of Data Generation and Usage
Real-World Evidence: The Future of Data Generation and UsageApril Bright
 
Federal Research & Development for the Florida system Sept 2014
Federal Research & Development for the Florida system Sept 2014 Federal Research & Development for the Florida system Sept 2014
Federal Research & Development for the Florida system Sept 2014 Warren Kibbe
 
Data supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbeData supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbeWarren Kibbe
 
Informatics and the merging of research and quality measures with bedside care
Informatics and the merging of research and quality measures with bedside careInformatics and the merging of research and quality measures with bedside care
Informatics and the merging of research and quality measures with bedside careMike Hogarth, MD, FACMI, FACP
 
The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...
The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...
The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...marcus evans Network
 
HEALTH PREDICTION ANALYSIS USING DATA MINING
HEALTH PREDICTION ANALYSIS USING DATA  MININGHEALTH PREDICTION ANALYSIS USING DATA  MINING
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
 
Stephen Friend Dana Farber Cancer Institute 2011-10-24
Stephen Friend Dana Farber Cancer Institute 2011-10-24Stephen Friend Dana Farber Cancer Institute 2011-10-24
Stephen Friend Dana Farber Cancer Institute 2011-10-24Sage Base
 
Heart Disease Prediction Using Data Mining
Heart Disease Prediction Using Data MiningHeart Disease Prediction Using Data Mining
Heart Disease Prediction Using Data MiningIRJET Journal
 
Simplifying semantics for biomedical applications
Simplifying semantics for biomedical applicationsSimplifying semantics for biomedical applications
Simplifying semantics for biomedical applicationsSemantic Web San Diego
 

Similar to AAPM Foster July 2009 (20)

Quantitative Medicine Feb 2009
Quantitative Medicine Feb 2009Quantitative Medicine Feb 2009
Quantitative Medicine Feb 2009
 
Paper id 36201506
Paper id 36201506Paper id 36201506
Paper id 36201506
 
EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...
EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...
EUSFLAT 2019: explainable neuro fuzzy recurrent neural network to predict col...
 
Clinical Genomics and Medicine
Clinical Genomics and MedicineClinical Genomics and Medicine
Clinical Genomics and Medicine
 
Improving EMRs 2009
Improving EMRs 2009Improving EMRs 2009
Improving EMRs 2009
 
Advantages And Disadvantages Of EHR
Advantages And Disadvantages Of EHRAdvantages And Disadvantages Of EHR
Advantages And Disadvantages Of EHR
 
ICBO 2014, October 8, 2014
ICBO 2014, October 8, 2014ICBO 2014, October 8, 2014
ICBO 2014, October 8, 2014
 
Next generation electronic medical records and search a test implementation i...
Next generation electronic medical records and search a test implementation i...Next generation electronic medical records and search a test implementation i...
Next generation electronic medical records and search a test implementation i...
 
IT Tools Supporting P4 Medicine
IT Tools Supporting P4 MedicineIT Tools Supporting P4 Medicine
IT Tools Supporting P4 Medicine
 
Real-World Evidence: The Future of Data Generation and Usage
Real-World Evidence: The Future of Data Generation and UsageReal-World Evidence: The Future of Data Generation and Usage
Real-World Evidence: The Future of Data Generation and Usage
 
Federal Research & Development for the Florida system Sept 2014
Federal Research & Development for the Florida system Sept 2014 Federal Research & Development for the Florida system Sept 2014
Federal Research & Development for the Florida system Sept 2014
 
Pavia wsp october 2011
Pavia wsp october 2011Pavia wsp october 2011
Pavia wsp october 2011
 
Data supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbeData supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbe
 
Informatics and the merging of research and quality measures with bedside care
Informatics and the merging of research and quality measures with bedside careInformatics and the merging of research and quality measures with bedside care
Informatics and the merging of research and quality measures with bedside care
 
The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...
The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...
The Randomized Controlled Trial: The Gold Standard of Clinical Science and a ...
 
HEALTH PREDICTION ANALYSIS USING DATA MINING
HEALTH PREDICTION ANALYSIS USING DATA  MININGHEALTH PREDICTION ANALYSIS USING DATA  MINING
HEALTH PREDICTION ANALYSIS USING DATA MINING
 
Stephen Friend Dana Farber Cancer Institute 2011-10-24
Stephen Friend Dana Farber Cancer Institute 2011-10-24Stephen Friend Dana Farber Cancer Institute 2011-10-24
Stephen Friend Dana Farber Cancer Institute 2011-10-24
 
Heart Disease Prediction Using Data Mining
Heart Disease Prediction Using Data MiningHeart Disease Prediction Using Data Mining
Heart Disease Prediction Using Data Mining
 
Simplifying semantics for biomedical applications
Simplifying semantics for biomedical applicationsSimplifying semantics for biomedical applications
Simplifying semantics for biomedical applications
 
C0344023028
C0344023028C0344023028
C0344023028
 

More from Ian Foster

Global Services for Global Science March 2023.pptx
Global Services for Global Science March 2023.pptxGlobal Services for Global Science March 2023.pptx
Global Services for Global Science March 2023.pptxIan Foster
 
The Earth System Grid Federation: Origins, Current State, Evolution
The Earth System Grid Federation: Origins, Current State, EvolutionThe Earth System Grid Federation: Origins, Current State, Evolution
The Earth System Grid Federation: Origins, Current State, EvolutionIan Foster
 
Better Information Faster: Programming the Continuum
Better Information Faster: Programming the ContinuumBetter Information Faster: Programming the Continuum
Better Information Faster: Programming the ContinuumIan Foster
 
ESnet6 and Smart Instruments
ESnet6 and Smart InstrumentsESnet6 and Smart Instruments
ESnet6 and Smart InstrumentsIan Foster
 
Linking Scientific Instruments and Computation
Linking Scientific Instruments and ComputationLinking Scientific Instruments and Computation
Linking Scientific Instruments and ComputationIan Foster
 
A Global Research Data Platform: How Globus Services Enable Scientific Discovery
A Global Research Data Platform: How Globus Services Enable Scientific DiscoveryA Global Research Data Platform: How Globus Services Enable Scientific Discovery
A Global Research Data Platform: How Globus Services Enable Scientific DiscoveryIan Foster
 
Foster CRA March 2022.pptx
Foster CRA March 2022.pptxFoster CRA March 2022.pptx
Foster CRA March 2022.pptxIan Foster
 
Big Data, Big Computing, AI, and Environmental Science
Big Data, Big Computing, AI, and Environmental ScienceBig Data, Big Computing, AI, and Environmental Science
Big Data, Big Computing, AI, and Environmental ScienceIan Foster
 
AI at Scale for Materials and Chemistry
AI at Scale for Materials and ChemistryAI at Scale for Materials and Chemistry
AI at Scale for Materials and ChemistryIan Foster
 
Coding the Continuum
Coding the ContinuumCoding the Continuum
Coding the ContinuumIan Foster
 
Data Tribology: Overcoming Data Friction with Cloud Automation
Data Tribology: Overcoming Data Friction with Cloud AutomationData Tribology: Overcoming Data Friction with Cloud Automation
Data Tribology: Overcoming Data Friction with Cloud AutomationIan Foster
 
Research Automation for Data-Driven Discovery
Research Automation for Data-Driven DiscoveryResearch Automation for Data-Driven Discovery
Research Automation for Data-Driven DiscoveryIan Foster
 
Scaling collaborative data science with Globus and Jupyter
Scaling collaborative data science with Globus and JupyterScaling collaborative data science with Globus and Jupyter
Scaling collaborative data science with Globus and JupyterIan Foster
 
Learning Systems for Science
Learning Systems for ScienceLearning Systems for Science
Learning Systems for ScienceIan Foster
 
Data Automation at Light Sources
Data Automation at Light SourcesData Automation at Light Sources
Data Automation at Light SourcesIan Foster
 
Team Argon Summary
Team Argon SummaryTeam Argon Summary
Team Argon SummaryIan Foster
 
Thoughts on interoperability
Thoughts on interoperabilityThoughts on interoperability
Thoughts on interoperabilityIan Foster
 
Computing Just What You Need: Online Data Analysis and Reduction at Extreme ...
Computing Just What You Need: Online Data Analysis and Reduction  at Extreme ...Computing Just What You Need: Online Data Analysis and Reduction  at Extreme ...
Computing Just What You Need: Online Data Analysis and Reduction at Extreme ...Ian Foster
 
NIH Data Commons Architecture Ideas
NIH Data Commons Architecture IdeasNIH Data Commons Architecture Ideas
NIH Data Commons Architecture IdeasIan Foster
 
Going Smart and Deep on Materials at ALCF
Going Smart and Deep on Materials at ALCFGoing Smart and Deep on Materials at ALCF
Going Smart and Deep on Materials at ALCFIan Foster
 

More from Ian Foster (20)

Global Services for Global Science March 2023.pptx
Global Services for Global Science March 2023.pptxGlobal Services for Global Science March 2023.pptx
Global Services for Global Science March 2023.pptx
 
The Earth System Grid Federation: Origins, Current State, Evolution
The Earth System Grid Federation: Origins, Current State, EvolutionThe Earth System Grid Federation: Origins, Current State, Evolution
The Earth System Grid Federation: Origins, Current State, Evolution
 
Better Information Faster: Programming the Continuum
Better Information Faster: Programming the ContinuumBetter Information Faster: Programming the Continuum
Better Information Faster: Programming the Continuum
 
ESnet6 and Smart Instruments
ESnet6 and Smart InstrumentsESnet6 and Smart Instruments
ESnet6 and Smart Instruments
 
Linking Scientific Instruments and Computation
Linking Scientific Instruments and ComputationLinking Scientific Instruments and Computation
Linking Scientific Instruments and Computation
 
A Global Research Data Platform: How Globus Services Enable Scientific Discovery
A Global Research Data Platform: How Globus Services Enable Scientific DiscoveryA Global Research Data Platform: How Globus Services Enable Scientific Discovery
A Global Research Data Platform: How Globus Services Enable Scientific Discovery
 
Foster CRA March 2022.pptx
Foster CRA March 2022.pptxFoster CRA March 2022.pptx
Foster CRA March 2022.pptx
 
Big Data, Big Computing, AI, and Environmental Science
Big Data, Big Computing, AI, and Environmental ScienceBig Data, Big Computing, AI, and Environmental Science
Big Data, Big Computing, AI, and Environmental Science
 
AI at Scale for Materials and Chemistry
AI at Scale for Materials and ChemistryAI at Scale for Materials and Chemistry
AI at Scale for Materials and Chemistry
 
Coding the Continuum
Coding the ContinuumCoding the Continuum
Coding the Continuum
 
Data Tribology: Overcoming Data Friction with Cloud Automation
Data Tribology: Overcoming Data Friction with Cloud AutomationData Tribology: Overcoming Data Friction with Cloud Automation
Data Tribology: Overcoming Data Friction with Cloud Automation
 
Research Automation for Data-Driven Discovery
Research Automation for Data-Driven DiscoveryResearch Automation for Data-Driven Discovery
Research Automation for Data-Driven Discovery
 
Scaling collaborative data science with Globus and Jupyter
Scaling collaborative data science with Globus and JupyterScaling collaborative data science with Globus and Jupyter
Scaling collaborative data science with Globus and Jupyter
 
Learning Systems for Science
Learning Systems for ScienceLearning Systems for Science
Learning Systems for Science
 
Data Automation at Light Sources
Data Automation at Light SourcesData Automation at Light Sources
Data Automation at Light Sources
 
Team Argon Summary
Team Argon SummaryTeam Argon Summary
Team Argon Summary
 
Thoughts on interoperability
Thoughts on interoperabilityThoughts on interoperability
Thoughts on interoperability
 
Computing Just What You Need: Online Data Analysis and Reduction at Extreme ...
Computing Just What You Need: Online Data Analysis and Reduction  at Extreme ...Computing Just What You Need: Online Data Analysis and Reduction  at Extreme ...
Computing Just What You Need: Online Data Analysis and Reduction at Extreme ...
 
NIH Data Commons Architecture Ideas
NIH Data Commons Architecture IdeasNIH Data Commons Architecture Ideas
NIH Data Commons Architecture Ideas
 
Going Smart and Deep on Materials at ALCF
Going Smart and Deep on Materials at ALCFGoing Smart and Deep on Materials at ALCF
Going Smart and Deep on Materials at ALCF
 

Recently uploaded

Male Infertility, Antioxidants and Beyond
Male Infertility, Antioxidants and BeyondMale Infertility, Antioxidants and Beyond
Male Infertility, Antioxidants and BeyondSujoy Dasgupta
 
power point presentation of Clinical evaluation of strabismus
power point presentation of Clinical evaluation  of strabismuspower point presentation of Clinical evaluation  of strabismus
power point presentation of Clinical evaluation of strabismusChandrasekar Reddy
 
Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.Vaikunthan Rajaratnam
 
AORTIC DISSECTION and management of aortic dissection
AORTIC DISSECTION and management of aortic dissectionAORTIC DISSECTION and management of aortic dissection
AORTIC DISSECTION and management of aortic dissectiondrhanifmohdali
 
Using Data Visualization in Public Health Communications
Using Data Visualization in Public Health CommunicationsUsing Data Visualization in Public Health Communications
Using Data Visualization in Public Health Communicationskatiequigley33
 
MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.whalesdesign
 
Red Blood Cells_anemia & polycythemia.pdf
Red Blood Cells_anemia & polycythemia.pdfRed Blood Cells_anemia & polycythemia.pdf
Red Blood Cells_anemia & polycythemia.pdfMedicoseAcademics
 
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.aarjukhadka22
 
FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...
FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...
FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...Shubhanshu Gaurav
 
Different drug regularity bodies in different countries.
Different drug regularity bodies in different countries.Different drug regularity bodies in different countries.
Different drug regularity bodies in different countries.kishan singh tomar
 
How to cure cirrhosis and chronic hepatitis naturally
How to cure cirrhosis and chronic hepatitis naturallyHow to cure cirrhosis and chronic hepatitis naturally
How to cure cirrhosis and chronic hepatitis naturallyZurück zum Ursprung
 
Female Reproductive Physiology Before Pregnancy
Female Reproductive Physiology Before PregnancyFemale Reproductive Physiology Before Pregnancy
Female Reproductive Physiology Before PregnancyMedicoseAcademics
 
ORAL HYPOGLYCAEMIC AGENTS - PART 2.pptx
ORAL HYPOGLYCAEMIC AGENTS  - PART 2.pptxORAL HYPOGLYCAEMIC AGENTS  - PART 2.pptx
ORAL HYPOGLYCAEMIC AGENTS - PART 2.pptxNIKITA BHUTE
 
SGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdf
SGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdfSGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdf
SGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdfHongBiThi1
 
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdfCONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdfDolisha Warbi
 
historyofpsychiatryinindia. Senthil Thirusangu
historyofpsychiatryinindia. Senthil Thirusanguhistoryofpsychiatryinindia. Senthil Thirusangu
historyofpsychiatryinindia. Senthil Thirusangu Medical University
 
CPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing studentCPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing studentsaileshpanda05
 

Recently uploaded (20)

Male Infertility, Antioxidants and Beyond
Male Infertility, Antioxidants and BeyondMale Infertility, Antioxidants and Beyond
Male Infertility, Antioxidants and Beyond
 
power point presentation of Clinical evaluation of strabismus
power point presentation of Clinical evaluation  of strabismuspower point presentation of Clinical evaluation  of strabismus
power point presentation of Clinical evaluation of strabismus
 
Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.Generative AI in Health Care a scoping review and a persoanl experience.
Generative AI in Health Care a scoping review and a persoanl experience.
 
AORTIC DISSECTION and management of aortic dissection
AORTIC DISSECTION and management of aortic dissectionAORTIC DISSECTION and management of aortic dissection
AORTIC DISSECTION and management of aortic dissection
 
Using Data Visualization in Public Health Communications
Using Data Visualization in Public Health CommunicationsUsing Data Visualization in Public Health Communications
Using Data Visualization in Public Health Communications
 
MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.MedMatch: Your Health, Our Mission. Pitch deck.
MedMatch: Your Health, Our Mission. Pitch deck.
 
Red Blood Cells_anemia & polycythemia.pdf
Red Blood Cells_anemia & polycythemia.pdfRed Blood Cells_anemia & polycythemia.pdf
Red Blood Cells_anemia & polycythemia.pdf
 
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
Bulimia nervosa ( Eating Disorders) Mental Health Nursing.
 
FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...
FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...
FDMA FLAP - The first dorsal metacarpal artery (FDMA) flap is used mainly for...
 
Different drug regularity bodies in different countries.
Different drug regularity bodies in different countries.Different drug regularity bodies in different countries.
Different drug regularity bodies in different countries.
 
GOUT UPDATE AHMED YEHIA 2024, case based approach with application of the lat...
GOUT UPDATE AHMED YEHIA 2024, case based approach with application of the lat...GOUT UPDATE AHMED YEHIA 2024, case based approach with application of the lat...
GOUT UPDATE AHMED YEHIA 2024, case based approach with application of the lat...
 
How to cure cirrhosis and chronic hepatitis naturally
How to cure cirrhosis and chronic hepatitis naturallyHow to cure cirrhosis and chronic hepatitis naturally
How to cure cirrhosis and chronic hepatitis naturally
 
How to master Steroid (glucocorticoids) prescription, different scenarios, ca...
How to master Steroid (glucocorticoids) prescription, different scenarios, ca...How to master Steroid (glucocorticoids) prescription, different scenarios, ca...
How to master Steroid (glucocorticoids) prescription, different scenarios, ca...
 
Female Reproductive Physiology Before Pregnancy
Female Reproductive Physiology Before PregnancyFemale Reproductive Physiology Before Pregnancy
Female Reproductive Physiology Before Pregnancy
 
ORAL HYPOGLYCAEMIC AGENTS - PART 2.pptx
ORAL HYPOGLYCAEMIC AGENTS  - PART 2.pptxORAL HYPOGLYCAEMIC AGENTS  - PART 2.pptx
ORAL HYPOGLYCAEMIC AGENTS - PART 2.pptx
 
SGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdf
SGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdfSGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdf
SGK RỐI LOẠN KALI MÁU CỰC KỲ QUAN TRỌNG.pdf
 
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdfCONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
CONNECTIVE TISSUE (ANATOMY AND PHYSIOLOGY).pdf
 
Immune labs basics part 1 acute phase reactants ESR, CRP Ahmed Yehia Ismaeel,...
Immune labs basics part 1 acute phase reactants ESR, CRP Ahmed Yehia Ismaeel,...Immune labs basics part 1 acute phase reactants ESR, CRP Ahmed Yehia Ismaeel,...
Immune labs basics part 1 acute phase reactants ESR, CRP Ahmed Yehia Ismaeel,...
 
historyofpsychiatryinindia. Senthil Thirusangu
historyofpsychiatryinindia. Senthil Thirusanguhistoryofpsychiatryinindia. Senthil Thirusangu
historyofpsychiatryinindia. Senthil Thirusangu
 
CPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing studentCPR.nursingoutlook.pdf , Bsc nursing student
CPR.nursingoutlook.pdf , Bsc nursing student
 

AAPM Foster July 2009

Editor's Notes

  1. Medicine is approaching a profound transition as the methods of molecular medicine start to transform the nature of health care.What is the significance of such methods? For the researcher, it is a paradigm shift, as the number of things that can be measured increases dramatically.
  2. Researchers express a vision for a scientific revolution in health care, from the qualitative to the quantitative-- A revolution based on information and thus computing
  3. However, even as we talk about transformation and revolution, we must recognize that computing is poorly used in health care today.These are the words of a recent National Research Council report.Thus, I will seek in my remarks today to shed light on three questions: how information technology is evolving, how this evolution may impact medicine, and how changes in medicine and health care will stress information techology.
  4. The story of computers is one of exponentials
  5. The story of computers is one of exponentials
  6. The story of computers is one of exponentials
  7. But things are not quite as bad as that
  8. What does this mean for medicine?We will certainly continue to see increasingly sophisticated computer applications aiding the physician in their tasks of observing, diagnosing, and treating – what used to be solely the domain the human senses, the brain, and the hands.More accurate, higher resolution, and more automated data acquisition systems.Computer-aided diagnosis and treatment planning systems that use large-scale data analysis and computer simulations.Automated radiation treatment and surgery systems. However, I want to focus here on some larger systems issues relating to quantitative medicine.
  9. Using gene expression microarrays, we find that these two diseases have quite different phenotypes—that quite different genes are expressed in the two conditions.Here, columns are patients; rows are genes.Not sure what is the significance of the Stage 1/Stage 2.”The beauty of gene expression profiling data is that it is quantitative and highly reproducible. Because of this, these data can be used to generate multivariate statistical models of the clinical behavior of cancer that have great predictive power.” -- http://lymphochip.nih.gov/Staudt_Adv_Immunol_2005.pdf
  10. And of course, we must not forget image-based biomarkers, as used in computer aided diagnosis of breast cancer, or as shown here, in an attempt to identify biomarkers for traumatic brain injury.ROIs used in a study at UIC(A) forceps minor (green), cortico-spinal tract (purple), inferior frontal-occipital fasciculus (red), external capsule (yellow), sagittal stratum (blue) (B) anterior corona radiata (green), superior longitudinal fasciculus (red), posterior corona radiata (blue); (C) cingulum (red), corpus callosum body (blue), splenium (yellow), and genu (green), and forceps major (purple).
  11. Then, by tracking the personalized treatment plan, we collect more patient data.Success demands that we integrate, to a far greater degree than previously possible, clinical practice, basic research, and clinical trials. A profound challenge for health care system and for information technology.
  12. Collecting and managing the enormous quantities of data that are now feasible, and required for EBM, is a huge challenge.However, merely putting in place the systems required to collect large quantities of data is not enough.We then need to make sense of that data. A challenge both for the physician and the researcher.
  13. These problems arise at multiple scales. E.g. …
  14. What these (and other examples that we will not have time to review) have in common …
  15. We cite [Rouse, Health Care as a CAS: Implications for Design… , NAE 2008] for the righthand side aprt.Must supportDynamic composition for a specific purposeEvolving community, function, environmentMessy data, failure, incomplete knowledgeNice, but insufficientData standardsPlatform standardsFederal policies
  16. Another perspective on the problem. A few words of explanation. If we are deploying a hospital IT system, we are (hopefully) in the bottom left hand corner.“You can’t achieve success via central planning.” Quoted in Crossing the Quality Chasm, p. 312In our scenarios, we don’t have that ability to control.
  17. What is the alternative? We can put in place mechanisms that facilitate groups with some common goal to form and function.Over time, things change, these groups evolve.If we are successful, they can expand, perhaps merge.Challenges: make this easy. Leverage scale effects.
  18. These are issues that the grid community has been working on for many years. We call these groupings Virtual Organizations.In healthcare today, there are of course many such “VOs.”But they are hard to form, fragmented, …
  19. Principles and mechanisms that has been under development for some years.First CS, then physical sciences, then biology, most recently biomedicine –
  20. What are these grid mechanisms and concepts, then? Hard to say something sensible in a few minutes.But basically it is about separating out concerns in a way that reduces barriers to entry and permits flexible use.
  21. API vs. protocol? “Illities”?
  22. [Create an image here.]For example DICOM and HL7 combine messaging and data model in the same interoperability standard. People are contextualizing this problem at the data interoperability level.  Systems interoperability often neglected.  An area of differentiation, bringing in best practice in industry and science into health care space. Open source platform.  Experience with systems interoperability standards: IETF, OASIS, W3C, 
  23. Scaling via automating data adaptersRepresentations of those things and semantics of those representations.Talk about how services are published, data modeling, etc.Publish data basesPublish servicesName published objects
  24. Loose coupling and encapsulationInteroperability through integration based on data mediation Evolutionary in nature Set of scalable systems and methods Explicit in architecture – data integration layerDemonstrated in GSI, GridFTP, MDS, ECOG
  25. Most images are never seen—and are not available—outside their originating institution
  26. Recap …
  27. 6 representative challenges
  28. Data sharing is the most important