Big Data and Health Care
•
6 likes•5,482 views
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Big Data is becoming economically feasible for health care. These slides describe how the cost of sensors, data processing, data storage and data analyzing are falling, how new and better forms of storage and algorithms are being implemented, and what this means for sustainable health care. These changes are enabling a move towards personalized health care.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Big Data and Health Care
Similar to Big Data and Health Care (20)
More from Jeffrey Funk
More from Jeffrey Funk (20)
Recently uploaded
Recently uploaded (20)
Big Data and Health Care
- 1. BIG DATA DIGITAL HEALTH REVOLUTION Alex A0135681 Henri A0135487 Zheng A0121892 Pham A0095804 Yin A0119974 Kavitha A0110143 For information on other technologies, see http://www.slideshare.net/Funk98/presentations
- 2. HAVE YOU EVER VISITED A DOCTOR?
- 3. ONE SIZE FITS ALL
- 4. ONE SIZE FITS ALL
- 10. SENSORS TODAY iBGStar iHealth wireless pulse oximeter Jawbon e Withings smart body analyser
- 11. iBGStar iHealth wireless pulse oximeter Jawbon e Withings smart body analyser CALORI ES EATING HABITS SLEEP BODY TEMPERATUR E HEART RATE BLOOD SUGAR SENSORS TODAY
- 14. Continuous MicroCHIPS Glucose Monitoring Google lens MIT batteryless power source Parathyroid hormone microchip injection SENSORS IN FUTURE Sensor-Laden Transdermal patch
- 15. SENSORS IN FUTURE - BioMEMS and Microsystems
- 17. SENSORS IN FUTURE - Micro supercapacitors Laser-scribed graphene micro-supercapacitors
- 18. SENSORS IN FUTURE - Reduction in MOSFET size
- 19. SENSORS IN FUTURE - External communication
- 20. SENSORS IN FUTURE - The trend in shrinking sells
- 21. SENSORS IN FUTURE - BioMEMS and Microsystems ● Size decrease ● Better and smaller communication chips and algorithms ● micro supercapacitors ● This will facilitate the arrival of these new implantable chips ● Allows for non bothersome personal medicine ● Allow for more tailored medicine ● It will require more data analysis and more processing power
- 23. The Storage Medium used is of More focus than the Quantity of Storage used. It is no longer one-size-fits-all “Data Deluge” is Fundamentally Changing the way that Storage is Approached. HARDWARE Introduction
- 24. ● Provide Real-time Or Near Real-time Responses. ● Handle Huge Data Volumes Growing Rapidly Key Characteristics of Big Data Infrastructure: ● High processing/IOPS performance ● Very Large Capacity. HARDWARE What’s Key to Efficient Data Processing?
- 25. KEY DIFFERENTIATOR ● Big Data is Largely Unstructured. ● Unstructured Data is Immutable ● Traditional File Systems have Built-in Functions to handle Insert/Update. ● Creates a Lot of Overhead in Terms of Performance, IOs Required to Access Data and the Ability to Scale HARDWARE WHY DO WE NEED A DIFFERENT APPROACH? FIG. GROWTH OF UNSTRUCTURED DATA ANNUA
- 26. ● Objects in one Large, Scalable Pool of Storage ● Stores metadata – Information about the object ● An Object ID is stored, to Locate the Data ● Objects are immutable ● No File System Hierarchy Products: ● Scality’s RING architecture ● Dell DX ● EMC’s Atmos HARDWARE OBJECT STORAGE – Choice of Storage
- 28. ● Access Times SSDs exhibit Virtually no Access time ● Random I/O Performance of SSD SSD Delivers at least 6000 IO/Sec 15 times faster than HDD(400 IO/S) ● Reliability SSDs 4-10 Times more Reliable HARDWARE Storage Medium Solid-State Drive (SSD) or Hard Disk Drive(HDD) SSD
- 29. REAL TIME APPLICATIONS OF SSD ● Read-Intensive Video-on-demand(VOD), and Image-Retrieval Applications. ● Emerging Applications (Big Data/Hadoop/Cloud) HARDWARE COMPARISON OF BOOT TIMES USING SSD & HDD
- 30. 2011 Throughput 250 MB/s , Capacity 512GB 2014: 1000 MB/s Data Transfer , Capacity 4TB Standard 2.5 inch form factor Further Scale Down of Flash Lithography Leads to Continued Performance Gains and Greater Capacity Points. HARDWARE Solid-State drives SSDs & Moore’s Law Fig 1.HDD Aerial Density follow Moore’s Law Fig2. Avg. Price Comparison of SSD Vs.
- 32. HARDWARE DATA PROTECTION – WHY DIFFER FROM TRADITIONAL APPROACHES?
- 33. RAID (REDUNDANT ARRAY OF INDEPENDENT DISKS) ● Originally Designed for Small Capacity Disks. ● Longer Time taken to Restore a Failed Drive as Capacity Increase. ● To Shorten Longer Rebuild cycles, RAID Systems Ship with Faster Processors, Leading to High Energy Consumption. REPLICATION ● Copies Add Additional Costs: Typically 133% or more Additional Storage is needed for each Additional Copy ● Storage System will get More Expensive as the amount of Data Increases HARDWARE Limitations of Traditional Approaches
- 34. How Does it Work? ● Information Dispersal Algorithms (IDAs) separate data into Unrecognizable slices of information. ● It is then dispersed to Storage Nodes in disparate Storage locations. ● It can be implemented Locally or Distributed . ● Only a Pre-defined subset of the slices From the Dispersed Storage Nodes is needed to fully Retrieve all of the Data. HARDWARE Information Dispersal - Better Approach?
- 35. ● It is Resilient against Natural disasters or Technological failures, like Drive failures, System Crashes and Network Failures. ● Data can still be Accessed in Real-time even if there are Multiple Simultaneous Failures across a String of Hosting Devices, Servers or Networks ● Five 9’s or More are Guaranteed with Overhead Low as 20% - As Opposed To 3 Copies Requiring 200% Overhead. HARDWARE Benefits of Information Dispersal
- 36. HARDWARE Cost Savings from IDA in Petabyte Storage over RAID and Replication
- 37. When looking at Number of Years without Data loss, with a 99.99999% Confidence Level, Information Dispersal doesn’t even appear on the Chart because even For a Large storage amount like 524K Terabytes, the Confidence for Years without data loss is not within anyone’s lifetime.(Theoretically Over 79 Million Years.) HARDWARE Cost Savings from IDA in Petabyte Storage over RAID and Replication
- 38. When looking at Number of Years without Data loss, with a 99.99999% Confidence Level, Information Dispersal doesn’t even appear on the Chart because even For a Large storage amount like 524K Terabytes, the Confidence for Years without data loss is not within anyone’s lifetime.(Theoretically Over 79 Million Years.) HARDWARE Cost Savings from IDA in Petabyte Storage over RAID and Replication
- 40. How make the huge dataset to match the ICD 10? ALGORITHMS Deal with the huge data
- 41. ICD 10 Clinical Modifications 69823 ICD CM Dataset • 3-7 characters • Character 1 is alpha • Character 2 is numeric • Character 3-7 can be alpha or numeric ICD 10 Procedure Coding System 76000 ICD 10 PCS Dataset • 7 characters • Each one can be alpha or numeric • Numbers 0-9; letters A-H, J- N, P-Z ALGORITHMS ICD 10 introduction
- 42. Analytics Algorithms Machine Learning Image Retrieval system Huge Nonstandard Data Source (4V) Data Feature Selection Huge multiple characters mapping databases Data Analytics Volume Velocity Variety Veracity ALGORITHMS Why we need big data
- 43. CONTENT DATA COLLECTION SENSORS DATA PROCESSING HARDWARE DATA ANALYZING ALGORITHMS SUSTAINABLE HEALTHCARE SYSTEM Deal with huge data Machine learning
- 44. Diagnosis is a relatively straightforward machine learning problem. Clinical decision making is highly suited for rule-based systems because of the nature of the data, such as ICD-10 codes, medications, etc., ALGORITHMS Machine Learning in medical diagnosis
- 45. ALGORITHMS Popular Imaging Modalities in Healthcare Domain
- 46. ALGORITHMS Medical Image Retrieval System
- 47. *ImageCLEF medical – competition on Medical Image Processing Two main tasks: ● Image–based retrieval ● Case–based retrieval source : http://www.imageclef.org/ # of images ALGORITHMS Database of ImageCLEF Data Medical competition
- 48. • This is the classic medical retrieval task. • Similar to Query by Image Example. • Given the query image, find the most similar images. http://www.imageclef.org/ # performance ALGORITHMS Image base retrieval Algorithm Performance = Difficulty * Accuracy # of images Mean average precision
- 49. • This is a more complex task; is closer to the clinical workflow. • A case description, with patient demographics, limited symptoms and test results including imaging studies, is provided (but not the final diagnosis). • The goal is to retrieve cases including images that might best suit the provided case description. http://www.imageclef.org/ ALGORITHMS Case-based retrieval Algorithm
- 50. Speed Slow Fast Accuracy Hard to keep Precision Level to study Quite hard Easy to learn Solution level Shallow Deep Machine Learning NO YES Result Hard to explain Perspective visualization ALGORITHMS Manual Calculate VS Software and Algorithm
- 52. TECHNOLOGICAL FUSION BioMEMS Hardware Object Storage Information Dispersal Machine Learning
- 53. More data can be gathered to identify patterns and interactions Doctors will use for diagnosis and decision- making Health care costs will decrease Individual patient care will improve TECHNOLOGICAL FUSION CONCLUSION
- 54. THANK YOU Q&A