Health Informatics
5 minute read
This section starts by discussing general aspects of Big Data and Health including data sizes, different areas including genomics, EBI, radiology and the Quantified Self movement. We review current state of health care and trends associated with it including increased use of Telemedicine. We summarize an industry survey by GE and Accenture and an impressive exemplar Cloud-based medicine system from Potsdam. We give some details of big data in medicine. Some remarks on Cloud computing and Health focus on security and privacy issues.
We survey an April 2013 McKinsey report on the Big Data revolution in US health care; a Microsoft report in this area and a European Union report on how Big Data will allow patient centered care in the future. Examples are given of the Internet of Things, which will have great impact on health including wearables. A study looks at 4 scenarios for healthcare in 2032. Two are positive, one middle of the road and one negative. The final topic is Genomics, Proteomics and Information Visualization.
Big Data and Health
This lesson starts with general aspects of Big Data and Health including listing subareas where Big data important. Data sizes are given in radiology, genomics, personalized medicine, and the Quantified Self movement, with sizes and access to European Bioinformatics Institute.
Status of Healthcare Today
This covers trends of costs and type of healthcare with low cost genomes and an aging population. Social media and government Brain initiative.
Status of Healthcare Today
(16:09)
Telemedicine (Virtual Health)
This describes increasing use of telemedicine and how we tried and failed to do this in 1994.
Medical Big Data in the Clouds
An impressive exemplar Cloud-based medicine system from Potsdam.
Medical Big Data in the Clouds
(15:02)
Medical image Big Data
Clouds and Health
McKinsey Report on the big-data revolution in US health care
This lesson covers 9 aspects of the McKinsey report. These are the convergence of multiple positive changes has created a tipping point for
innovation; Primary data pools are at the heart of the big data revolution in healthcare; Big data is changing the paradigm: these are the value pathways; Applying early successes at scale could reduce US healthcare costs by $300 billion to $450 billion; Most new big-data applications target consumers and providers across pathways; Innovations are weighted towards influencing individual decision-making levers; Big data innovations use a range of public, acquired, and proprietary data
types; Organizations implementing a big data transformation should provide the leadership required for the associated cultural transformation; Companies must develop a range of big data capabilities.
Microsoft Report on Big Data in Health
This lesson identifies data sources as Clinical Data, Pharma & Life Science Data, Patient & Consumer Data, Claims & Cost Data and Correlational Data. Three approaches are Live data feed, Advanced analytics and Social analytics.
Microsoft Report on Big Data in Health
(2:26)
EU Report on Redesigning health in Europe for 2020
This lesson summarizes an EU Report on Redesigning health in Europe for 2020. The power of data is seen as a lever for change in My Data, My decisions; Liberate the data; Connect up everything; Revolutionize health; and Include Everyone removing the current correlation between health and wealth.
EU Report on Redesigning health in Europe for 2020
(5:00)
Medicine and the Internet of Things
The Internet of Things will have great impact on health including telemedicine and wearables. Examples are given.
Medicine and the Internet of Things
(8:17)
Extrapolating to 2032
A study looks at 4 scenarios for healthcare in 2032. Two are positive, one middle of the road and one negative.
Genomics, Proteomics and Information Visualization
A study of an Azure application with an Excel frontend and a cloud BLAST backend starts this lesson. This is followed by a big data analysis of personal genomics and an analysis of a typical DNA sequencing analytics pipeline. The Protein Sequence Universe is defined and used to motivate Multi dimensional Scaling MDS. Sammon’s method is defined and its use illustrated by a metagenomics example. Subtleties in use of MDS include a monotonic mapping of the dissimilarity function. The application to the COG Proteomics dataset is discussed. We note that the MDS approach is related to the well known chisq method and some aspects of nonlinear minimization of chisq (Least Squares) are discussed.
Genomics, Proteomics and Information Visualization
(6:56)
Next we continue the discussion of the COG Protein Universe introduced in the last lesson. It is shown how Proteomics clusters are clearly seen in the Universe browser. This motivates a side remark on different clustering methods applied to metagenomics. Then we discuss the Generative Topographic Map GTM method that can be used in dimension reduction when original data is in a metric space and is in this case faster than MDS as GTM computational complexity scales like N not N squared as seen in MDS.
Examples are given of GTM including an application to topic models in Information Retrieval. Indiana University has developed a deterministic annealing improvement of GTM. 3 separate clusterings are projected for visualization and show very different structure emphasizing the importance of visualizing results of data analytics. The final slide shows an application of MDS to generate and visualize phylogenetic trees.
\TODO{These two videos need to be uploaded to youtube}
Genomics, Proteomics and Information Visualization I
(10:33)
Genomics, Proteomics and Information Visualization: II
(7:41)
Proteomics and Information Visualization
(131)
Resources
- https://wiki.nci.nih.gov/display/CIP/CIP+Survey+of+Biomedical+Imaging+Archives [@wiki-nih-cip-survey]
- http://grids.ucs.indiana.edu/ptliupages/publications/Where\%20does\%20all\%20the\%20data\%20come\%20from\%20v7.pdf [@fox2011does]
http://www.ieee-icsc.org/ICSC2010/Tony\%20Hey\%20-\%2020100923.pdf(this link does not exist any longer)- http://quantifiedself.com/larry-smarr/ [@smarr13self]
- http://www.ebi.ac.uk/Information/Brochures/ [@www-ebi-aboutus]
- http://www.kpcb.com/internet-trends [@www-kleinerperkins-internet-trends]
- http://www.slideshare.net/drsteventucker/wearable-health-fitness-trackers-and-the-quantified-self [@www-slideshare-wearable-quantified-self]
- http://www.siam.org/meetings/sdm13/sun.pdf [@archive–big-data-analytics-healthcare]
- http://en.wikipedia.org/wiki/Calico_\%28company\%29 [@www-wiki-calico]
- http://www.slideshare.net/GSW_Worldwide/2015-health-trends [@www-slideshare-2015-health trends]
- http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Industrial-Internet-Changing-Competitive-Landscape-Industries.pdf [@www-accenture-insight-industrial-internet]
- http://www.slideshare.net/schappy/how-realtime-analysis-turns-big-medical-data-into-precision-medicine [@www-slideshare-big-medical-data-medicine]
- http://medcitynews.com/2013/03/the-body-in-bytes-medical-images-as-a-source-of-healthcare-big-data-infographic/ [@medcitynews-bytes-medical-images]
http://healthinformatics.wikispaces.com/file/view/cloud_computing.ppt(this link does not exist any longer)- https://www.mckinsey.com/~/media/mckinsey/industries/healthcare%20systems%20and%20services/our%20insights/the%20big%20data%20revolution%20in%20us%20health%20care/the_big_data_revolution_in_healthcare.ashx [@www-mckinsey-industries-healthcare]
https://partner.microsoft.com/download/global/40193764(this link does not exist any longer)- https://ec.europa.eu/eip/ageing/file/353/download_en?token=8gECi1RO
http://www.liveathos.com/apparel/app- http://debategraph.org/Poster.aspx?aID=77 [@debategraph-poster]
http://www.oerc.ox.ac.uk/downloads/presentations-from-events/microsoftworkshop/gannon(this link does not exist any longer)http://www.delsall.org(this link does not exist any longer)- http://salsahpc.indiana.edu/millionseq/mina/16SrRNA_index.html [@www-salsahpc-millionseq]
- http://www.geatbx.com/docu/fcnindex-01.html [@www-geatbx-parametric-optimization]