KDnuggets Home » News :: 2013 :: Jun :: News Briefs :: Heritage Health 500K Prize awarded; HHP Prize 2 will be $3M "masters" competition with real, not anonymized data ( 13:n15 )

Heritage Health 500K Prize awarded; HHP Prize 2 will be $3M “masters” competition with real, not anonymized data


POWERDOT, a team of former rivals, won $500K for 1st place in Heritage Health Prize. As a followup, HPN is launching a $3 million private "masters" competition, open to the top finishers from the first prize. The second prize will use actual health care data, with little or no anonymization.



After 2 years, 1659 teams, and over 35,000 entries, Heritage Provider Network awarded $500,000 to team POWERDOTfor their leading effort in the Heritage Health Prize.

The top 10 teams on the leaderboard and their scores were

  1. POWERDOT, 0.461197
  2. EXL Analytics Team , 0.462247
  3. J.A. Guerrero , 0.462417
  4. PANDA Team , 0.462644
  5. Opera Solutions Team , 0.462956
  6. CombinedPower Team , 0.463052
  7. Essex Lake Group Team , 0.463102
  8. Xing Zhao , 0.463125
  9. Ambrosia , 0.463133
  10. Arete Associates Team , 0.463526

Team POWERDOTTeam POWERDOT joined forces in Oct 2012 by combining former rivals and milestone prize winners. Team members include

  • David Vogel, Chief Scientist of Voloridge Investment Management,
  • Dr. Randy Axelrod, Executive VP, Providence Health & Services,
  • Rie Johnson, a machine learning researcher,
  • Willem Mestrom, Business Intelligence specialist at Independer in the Netherlands, and
  • Edward de Grijs, an engineer and software developer also from the Netherlands,
  • Tong Zhang, a machine learning researcher, and
  • Phil Brierley, Analytics Consultant of Tiberius Data Mining from Australia.

As a followup, HPN is launching a $3 million private "masters" competition, also hosted by Kaggle. The competition will be open to the top eligible finishers from the first Heritage Health Prize.

The challenge will be the same as the first prize - predict hospitalisation of individuals - with one very substantial difference: there will be little, if any, data anonymization. For privacy reasons, the public competition used data that had been very heavily anonymized. For example, nearly all information about prescriptions was held back, and diagnostic information from lab results was summarised to just some high level information. Furthermore, information like age was categorised into a few bands - the exact age of patients was not provided. In fact, the anonymization process was so complex that the approach was detailed in a peer reviewed academic journal.

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