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