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Decision Support Notifications
Get notifications telling you the most important thing (MIT) you can do at any given time to optimize your health and happiness.
notifications-screenshot-slide.png
203.93538913362704

🤖 Decision Support Notifications

notifications-screenshot-slide.png

What is it?

Decision Support Notifications are a way to get notifications telling you the most important thing (MIT) you can do at any given time to optimize your health and happiness.

How does it work?

Decision Support Notifications work by using a combination of machine learning and control theory to predict the future and optimize your health and happiness.

Time-series data on food, drugs, and symptoms is fed into a machine learning model that which input values produce the most significant improvements in symptom severity ratings or biomarker levels.

The system monitors your body and environment in real-time, and then identifies when the most significant input factors are outside their optimal ranges. When this happens, the system sends you a notification telling you what you can do to optimize your health and happiness.

References

  1. SunilDeshpande_S2014_ETD.pdf (asu.edu)
  2. LocalControl: An R Package for Comparative Safety and Effectiveness Research | Journal of Statistical Software (jstatsoft.org)
  3. bbotk: A brief introduction (r-project.org)
  4. artemis-toumazi/dfpk (github.com)
  5. miroslavgasparek/MPC_Cancer: Model Predictive Control for the optimisation of the tumour treatment through the combination of the chemotherapy and immunotherapy. (github.com)
  6. Doubly Robust Learning — econml 0.12.0 documentation
  7. A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention (nih.gov)
  8. The promise of machine learning in predicting treatment outcomes in psychiatry - Chekroud - 2021 - World Psychiatry - Wiley Online Library
  9. CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence - Agata Blasiak, Jeffrey Khong, Theodore Kee, 2020 (sagepub.com)
  10. Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation (aimspress.com)
  11. Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks (nips.cc)
  12. Estimating counterfactual treatment outcomes over time through adversarially balanced representations | OpenReview
  13. https://dash.harvard.edu/bitstream/handle/1/37366470/AGUILAR-SENIORTHESIS-2019.pdf?isAllowed=y&sequence=1