I am still angry with THE netflix for 1 mill I lost out. No sweat! this time the money is 3 times as much, it is legit, and yes baby you can change the world, make it a better place and get rich.! see details below-http://www.heritagehealthprize.com/c/hhp/Data
IMPORTANT NOTE: The information provided below is intended only to provide general guidance to participants in the Heritage Health Prize Competition and is subject to the Competition Official Rules. Any capitalized term not defined below is defined in the Competition Official Rules. Please consult the Competition Official Rules for complete details.
Heritage Provider Network is providing Competition Entrants with deidentified member data collected during a forty-eight month period that is allocated among three data sets (the “Data Sets”). Competition Entrants will use the Data Sets to develop and test their algorithms for accurately predicting the number of days that the members will spend in a hospital (inpatient or emergency room visit) during the 12-month period following the Data Set cut-off date.
HHP_release2.zip contains the latest files, so you can ignore HHP_release1.zip. SampleEntry.CSV shows you how an entry should look.
Data Sets will be released to Entrants after registration on the Website according to the following schedule:
April 4, 2011
Claims Table – Y1 and DaysInHospital Table – Y2
May 4, 2011
All other Data Sets except Labs Table and Rx Table
By now, people have had a good chance to poke around the first portion of the data. Now the fun starts! HPN have released two more years’-worth of data, set the accuracy threshold and are opening up the competition to entries. The data are available from the Heritage Health Prize page. Good luck to all participants!
The Deloitte/FIDE Chess Ratings Competition results
The Deloitte/FIDE Chess Ratings Competition attracted one of the strongest fields ever seen in a Kaggle Competition. The competition attracted 189 teams, ranging from chess ratings experts to Netflix Prize winners. As Jeff Sonas wrote on the Kaggle blog last week, the competition has far exceeded his expectations. A big congratulations the provisional winner, Tim Salimans, an econometrician at Erasmus University in Rotterdam. We look forward to reading about the approaches used by top performers on the Kaggle blog. We also look forward to the results of the FIDE prize, which could see the introduction of a new chess ratings system.
ICDAR 2011 Competition Results
The ICDAR 2011 competition also finished recently. The competiiton required participants to develop an algorithm that correctly matched handwriting samples. The winners were Lewis Griffin and Andrew Newell from the University College London who achieved Kaggle’s first ever perfect score by managing to match every sample correctly! Andrew and Lewis have posted a description of their winning method on the Kaggle blog.
Revolution R Enterprise
Since R is the most popular language used by Kaggle members, the Revolution Analytics team is making Revolution R Enterprise (the pre-eminent commercial version of R) available free of charge to Kaggle members. Revolution R Enterprise has several advantages over standard R, including the ability to seemlessly handle larger datasets. To get your free copy, visit http://info.revolutionanalytics.com/Kaggle.html.
As many of you know, Kaggle offers a free platform, Kaggle-in-Class, for instructors who want to host competitions for their students. For those interested in hearing more about the use of Kaggle-in-Class as a teaching tool, Susan Holmes and Nelson Ray from Stanford University share their experience in a webinar organized by the Consortium for the Advancement of Undergraduate Statistics Education.
To join the preview group, go to the APIs Console and click the Prediction API slider to “ON,” and then sign up for a Google Storage account.
For the past several months, I have been member of a semi-public beta test/group/forum – that is headed by Travis Green of the Google Prediction API Team (not the hockey player). Basically in helping the Google guys more feedback on the feature list for model building via cloud computing. I couldn’t talk about it much , because it was all NDA hush hush.
Anyways- as of today the version 1.2 of Google Prediction API has been launched. What does this do to the ordinary Joe Modeler? Well it helps gives your models -thats right your plain vanilla logistic regression,arima, arimax, models an added ensemble option of using Google’s Machine Learning Continue reading “Google releases V1.2 of Google Prediction API”
More than 71 Million individuals in the United States are admitted to
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.
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.