Georgedescribes whatmachinelearning is witha pipeline figureJack reviewshow the UHteam isinvolved inthe projectThe groupparticipatesin a poll onfacilitatorsJames talksabout issuesregardingusing digitaltoolsJack shows thedifferencebetween simplemediation andmoderatedmediationThe futureadverse eventmanagementsystem isshownJack presentson logic modelsand how thiscan addressprogram needsThe groupparticipatesin a poll onbarriersThe currentadverse eventmanagementsystem isshownMary showshistoricaltrends inadverseeventsMary showshistorical trendsin participantsreceivingservices inDDDMary talksabout thesysteminfrastructureand whereOEAIDD fits inDifferentassumptionsbuilt into alogic modelis presentedJack presentson how wemight mergedata withclinicaloutcomesGeorge describesthe scope of theproject byidentifyingproblems andsolutionsAn exampleof the AERdashboardis presentedTheproposedarchitectureof the systemis shownJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsWe learn howallergens andmedicationsmay predictAERsAn exampleof topfeatures forthe modelsis shownData toWisdomtriangle isshownDisney castleand dreamsfor theorganizationis presentedMary showshistorical trendsin adverseevents perparticipants inDDDLove betweenprocess focusedevaluations andquantitativemethods ishighlightedAn exampleof how modelmetrics worksis shownTable withHoike toNaauao acrossdifferent playersis shownProgress isindicatedthroughweavingrelationshipsJames talksabout hisstory in thefoster homesystemMary reviewsdifferent fivedifferent playersinvolved in theprojectMarydescribespotentialimpacts ofthe projectGeorgedescribes whatmachinelearning is witha pipeline figureJack reviewshow the UHteam isinvolved inthe projectThe groupparticipatesin a poll onfacilitatorsJames talksabout issuesregardingusing digitaltoolsJack shows thedifferencebetween simplemediation andmoderatedmediationThe futureadverse eventmanagementsystem isshownJack presentson logic modelsand how thiscan addressprogram needsThe groupparticipatesin a poll onbarriersThe currentadverse eventmanagementsystem isshownMary showshistoricaltrends inadverseeventsMary showshistorical trendsin participantsreceivingservices inDDDMary talksabout thesysteminfrastructureand whereOEAIDD fits inDifferentassumptionsbuilt into alogic modelis presentedJack presentson how wemight mergedata withclinicaloutcomesGeorge describesthe scope of theproject byidentifyingproblems andsolutionsAn exampleof the AERdashboardis presentedTheproposedarchitectureof the systemis shownJamesdiscusses waysthat we mightaddress issuesusing digitaltoolsWe learn howallergens andmedicationsmay predictAERsAn exampleof topfeatures forthe modelsis shownData toWisdomtriangle isshownDisney castleand dreamsfor theorganizationis presentedMary showshistorical trendsin adverseevents perparticipants inDDDLove betweenprocess focusedevaluations andquantitativemethods ishighlightedAn exampleof how modelmetrics worksis shownTable withHoike toNaauao acrossdifferent playersis shownProgress isindicatedthroughweavingrelationshipsJames talksabout hisstory in thefoster homesystemMary reviewsdifferent fivedifferent playersinvolved in theprojectMarydescribespotentialimpacts ofthe project

OEAIDD Data Party BINGO - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. George describes what machine learning is with a pipeline figure
  2. Jack reviews how the UH team is involved in the project
  3. The group participates in a poll on facilitators
  4. James talks about issues regarding using digital tools
  5. Jack shows the difference between simple mediation and moderated mediation
  6. The future adverse event management system is shown
  7. Jack presents on logic models and how this can address program needs
  8. The group participates in a poll on barriers
  9. The current adverse event management system is shown
  10. Mary shows historical trends in adverse events
  11. Mary shows historical trends in participants receiving services in DDD
  12. Mary talks about the system infrastructure and where OEAIDD fits in
  13. Different assumptions built into a logic model is presented
  14. Jack presents on how we might merge data with clinical outcomes
  15. George describes the scope of the project by identifying problems and solutions
  16. An example of the AER dashboard is presented
  17. The proposed architecture of the system is shown
  18. James discusses ways that we might address issues using digital tools
  19. We learn how allergens and medications may predict AERs
  20. An example of top features for the models is shown
  21. Data to Wisdom triangle is shown
  22. Disney castle and dreams for the organization is presented
  23. Mary shows historical trends in adverse events per participants in DDD
  24. Love between process focused evaluations and quantitative methods is highlighted
  25. An example of how model metrics works is shown
  26. Table with Hoike to Naauao across different players is shown
  27. Progress is indicated through weaving relationships
  28. James talks about his story in the foster home system
  29. Mary reviews different five different players involved in the project
  30. Mary describes potential impacts of the project