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Your emerald eyes,
Like dewdrops glistening on green grass.
The shine in them,
Is like the twinkling of the stars.
You’re ivory skin,
Reminds me of the moonlight.
Like a gorgeous lily
Colored in silvery white.
Your sunkissed hair,
Blowing gently in the breeze.
I do not look long,
My breath may freeze.
You’re sideways glance,
As sharp as a knife.
Like a Greek goddess or a marble sculpture,
Brought to life.
Your poise, your grace,
Your beauty brings out,
The poet within.
The tinkle of your soft voice remains in my ears,
Long after you are long gone.
Your memory drives me crazy,
Makes me want to break out in a song.
Alas, my dear
I am in love with your beauty
But not with you.
This sounds like an obsession,
For this love is not true.
I am a passionate man,
With much passion to spare.
As soon as you leave my thoughts,
Someone else is already there.
- A poem: Confessions of a Lover (soliloquoywhisper.wordpress.com)
- Rumi Poems (yilmazalimoglu.com)
- Poem about Wheelbarrow of the World (fmpoetry.wordpress.com)
- ‘Eurydice’ Emerges From Darkness (3quarksdaily.com)
- Poem-a-day #7 (yearofthebooks.wordpress.com)
- Rust, Pain, Beauty and Time (hokku.wordpress.com)
- Personal Note: On Audrey Hepburn’s Poem ‘beauty tips’ (marylouisewehunt.wordpress.com)
- Poem: Layla and Majnun (yilmazalimoglu.com)
- 10 minute poem (dulcedolce.wordpress.com)
- “April Is National Poetry Month” and related posts (thencbla.blogspot.com)
- Heritage Provider Network Announces the Heritage Health Prize Will Include $230,000 in Progress Prizes (prnewswire.com)
- $3.2M in prizes for predicting hospitalization (revolutionanalytics.com)
- Heritage Health Prize: Is $3 million enough to improve the U.S. health care system? (slate.com)
If Netflix was about 1 mill USD to better online video choices, here is a chance to earn serious money, write great code, and save lives!
Heritage Health Prize
Launching April 4
hospitals each year, according to the latest survey from the American
Hospital Association. Studies have concluded that in 2006 well over
$30 billion was spent on unnecessary hospital admissions. Each of
these unnecessary admissions took away one hospital bed from someone
else who needed it more.
Prize Goal & Participation
The goal of the prize is to develop a predictive algorithm that can identify patients who will be admitted to the hospital within the next year, using historical claims data.
Official registration will open in 2011, after the launch of the prize. At that time, pre-registered teams will be notified to officially register for the competition. Teams must consent to be bound by final competition rules.
Registered teams will develop and test their algorithms. The winning algorithm will be able to predict patients at risk for an unplanned hospital admission with a high rate of accuracy. The first team to reach the accuracy threshold will have their algorithms confirmed by a judging panel. If confirmed, a winner will be declared.
The competition is expected to run for approximately two years. Registration will be open throughout the competition.
Registered teams will be granted access to two separate datasets of de-identified patient claims data for developing and testing algorithms: a training dataset and a quiz/test dataset. The datasets will be comprised of de-identified patient data. The datasets will include:
- Outpatient encounter data
- Hospitalization encounter data
- Medication dispensing claims data, including medications
- Outpatient laboratory data, including test outcome values
The data for each de-identified patient will be organized into two sections: “Historical Data” and “Admission Data.” Historical Data will represent three years of past claims data. This section of the dataset will be used to predict if that patient is going to be admitted during the Admission Data period. Admission Data represents previous claims data and will contain whether or not a hospital admission occurred for that patient; it will be a binary flag.
The training dataset includes several thousand anonymized patients and will be made available, securely and in full, to any registered team for the purpose of developing effective screening algorithms.
The quiz/test dataset is a smaller set of anonymized patients. Teams will only receive the Historical Data section of these datasets and the two datasets will be mixed together so that teams will not be aware of which de-identified patients are in which set. Teams will make predictions based on these data sets and submit their predictions to HPN through the official Heritage Health Prize web site. HPN will use the Quiz Dataset for the initial assessment of the Team’s algorithms. HPN will evaluate and report back scores to the teams through the prize website’s leader board.
Scores from the final Test Dataset will not be made available to teams until the accuracy thresholds are passed. The test dataset will be used in the final judging and results will be kept hidden. These scores are used to preserve the integrity of scoring and to help validate the predictive algorithms.
Teams can begin developing and testing their algorithms as soon as they are registered and ready. Teams will log onto the official Heritage Health Prize website and submit their predictions online. Comparisons will be run automatically and team accuracy scores will be posted on the leader board. This score will be only on a portion of the predictions submitted (the Quiz Dataset), the additional results will be kept back (the Test Dataset).
Once a team successfully scores above the accuracy thresholds on the online testing (quiz dataset), final judging will occur. There will be three parts to this judging. First, the judges will confirm that the potential winning team’s algorithm accurately predicts patient admissions in the Test Dataset (again, above the thresholds for accuracy).
Next, the judging panel will confirm that the algorithm does not identify patients and use external data sources to derive its predictions. Lastly, the panel will confirm that the team’s algorithm is authentic and derives its predictive power from the datasets, not from hand-coding results to improve scores. If the algorithm meets these three criteria, it will be declared the winner.
Failure to meet any one of these three parts will disqualify the team and the contest will continue. The judges reserve the right to award second and third place prizes if deemed applicable.
- HPN Health Prize: The X-Prize of Health Care (medicineandtechnology.com)
- $3 million machine learning prize (heritagehealthprize.com)
- Heritage Providers Continues to Promote $3 Million Dollar Prize to Create An Algorithm To Predict and Prevents Hospitalizations (ducknetweb.blogspot.com)
- Netflix Prize-Style Competition Predicts Hospitalizations (fastcompany.com)
- For Data Crunchers, A Glittering Prize (online.wsj.com)
- The American Hospital Association Awards Its Exclusive Endorsement to HR Solutions’ Physician Engagement Survey (prweb.com)
Under the terms of the agreement, the companies will work together to create a version of Revolution’s software that takes advantage of IBM Netezza’s i-class technology so that Revolution R Enterprise can run in-database in an optimal fashion.
For information about IBM Netezza, please visit: http://www.netezza.com.
For Information on IBM Information Management, please visit: http://www.ibm.com/software/data/information-on-demand/
For information on IBM Business Analytics, please visit the online press kit: http://www.ibm.com/press/us/en/presskit/27163.wss
Follow IBM and Analytics on Twitter: http://twitter.com/ibmbizanalytics
Follow IBM analytics on Tumblr: http://smarterplanet.tumblr.com/tagged/new_intelligence
IBM YouTube Analytics Channel: http://www.youtube.com/user/ibmbusinessanalytics
For information on IBM Smarter Systems: http://www-03.ibm.com/systems/smarter/
About Revolution Analytics
Revolution Analytics is the leading commercial provider of software and services based on the open source R project for statistical computing. Led by predictive analytics pioneer Norman Nie, the company brings high performance, productivity and enterprise readiness to R, the most powerful statistics language in the world. The company’s flagship Revolution R product is designed to meet the production needs of large organizations in industries such as finance, life sciences, retail, manufacturing and media. Used by over 2 million analysts in academia and at cutting-edge companies such as Google, Bank of America and Acxiom, R has emerged as the standard of innovation in statistical analysis. Revolution Analytics is committed to fostering the continued growth of the R community through sponsorship of the Inside-R.org community site, funding worldwide R user groups and offers free licenses of Revolution R Enterprise to everyone in academia.
Netezza, an IBM Company, is the global leader in data warehouse, analytic and monitoring appliances that dramatically simplify high-performance analytics across an extended enterprise. IBM Netezza’s technology enables organizations to process enormous amounts of captured data at exceptional speed, providing a significant competitive and operational advantage in today’s data-intensive industries, including digital media, energy, financial services, government, health and life sciences, retail and telecommunications.
The IBM Netezza TwinFin® appliance is built specifically to analyze petabytes of detailed data significantly faster than existing data warehouse options, and at a much lower total cost of ownership. It stores, filters and processes terabytes of records within a single unit, analyzing only the relevant information for each query.
Using Revolution R Enterprise & Netezza Together
Revolution Analytics and IBM Netezza have announced a partnership to integrate Revolution R Enterprise and the IBM Netezza TwinFin Data Warehouse Appliance. For the first time, customers seeking to run high performance and full-scale predictive analytics from within a data warehouse platform will be able to directly leverage the power of the open source R statistics language. The companies are working together to create a version of Revolution’s software that takes advantage of IBM Netezza’s i-class technology so that Revolution R Enterprise can run in-database in an optimal fashion.
This partnership integrates Revolution R Enterprise with IBM Netezza’s high performance data warehouse and advanced analytics platform to help organizations combat the challenges that arise as complexity and the scale of data grow. By moving the analytics processing next to the data, this integration will minimize data movement – a significant bottleneck, especially when dealing with “Big Data”. It will deliver high performance on large scale data, while leveraging the latest innovations in analytics.
With Revolution R Enterprise for IBM Netezza, advanced R computations are available for rapid analysis of hundreds of terabyte-class data volumes — and can deliver 10-100x performance improvements at a fraction of the cost compared to traditional analytics vendors.
- On-Demand Webinar: Revolution R Enterprise: 100% R and More
- Free Downloads: Revolution R Community
- Product Information:
- IBM’s bet: Commerce can be just as big as analytics (zdnet.com)
- Revolution Analytics announces partnership with IBM Netezza (revolutionanalytics.com)
- Netezza Chief Talks About “Formative” PTC Days, IBM Deal History, and the Future of Big Data (xconomy.com)
- Gartner Ranks Data Warehousing Leaders (informationweek.com)
- IBM Acquires Netezza in $1.7 Billion Deal (dailyfinance.com)
- HP To Acquire Analytics Specialist Vertica (consultramy.wordpress.com)
- SAP, IBM Team up on In-memory Analytics (pcworld.com)
CDC.gov has a great tool for showing United States statistics on death and injury, drillable by various details.
The tool is hosted at http://wisqars.cdc.gov:8080/cdcMapFramework/
As a test I decided to map out injuries due to fire arms , and compare firearm deaths of white people versus the whole population.(see firearm deaths file)
See white people are more likely than black people to own guns (also read http://www.ncbi.nlm.nih.gov/pubmed/9572612 ), but it seems statistically they are less likely to be injured by firearms- so it could affect support for gun control laws on a racial ground- that was my null hypothesis. No politics, just plain statistics. I dont know- why dont you look at the data and decide-
- On MLK Day, A Continued Fight Against Health Disparities (health.change.org)
- CDC finds health disparities among races (seattletimes.nwsource.com)
- CDC: Binge Drinking ‘Huge U.S. Health Problem’ (webmd.com)
- On Health Disparities & Inequalities in the U.S.: 2011 Report (medhumanities.org)
- Tainted food sickens 48 mln each year: CDC (reuters.com)
- Flu Is Widespread in 11 States (webmd.com)
- CDC: Asthma rate in US up a little to 8.2 pct (seattletimes.nwsource.com)