Math

Stats, predictive algorithms (so we can actually measure what we are always implicitly predicting), the works.

If you’ve got a project that analyzes glucose, or insulin or similar data, add it to the list:

• DUBS — dubs is about understanding past and ongoing therapy by performing simulations to measure what were previously hidden and implicit expectations.
• http://www.ncbi.nlm.nih.gov/pubmed/10994512
• http://www.2aida.net/welcome/
• http://www.ncbi.nlm.nih.gov/pubmed?itool=pubmed_Abstract&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&IdsFromResult=9183777&retmode=ref
• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3320833/
• http://www.2aida.org/aida/research2.htm
• http://oucsace.cs.ohiou.edu/~marling/smarthealth/projects.html

Documentation

• documenting what we’ve done for the FDA/creating a suite of IP-free technology requires documentation!
  • http://www.ncbi.nlm.nih.gov/pubmed/22226258
  • http://www.w3.org/Consortium/Patent-Policy-20040205/
  • http://tools.ietf.org/html/draft-morton-ippm-advance-metrics-02
  • http://www.ncbi.nlm.nih.gov/pubmed/20307387
  • http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2864162/
  • http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/GeneralHospitalDevicesandSupplies/InfusionPumps/ucm202511.htm
  • http://www.fda.gov/medicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm206153.htm
  • http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/HomeHealthandConsumer/ConsumerProducts/ArtificialPancreas/ucm259555.htm