NextGen AI MC enabled by CAP and Nobel causality Use AI MC to discover root causes for problem solving
Demonstrate AI MC by solving overweight problems
Optimal weightloss can prevent 70% illness and save lives
Solve Obesity Problems for Physicians
Problem A: What causes overweight? Problem B: Is diet > activities for weightloss?
1. collect original data by natural experiment
2. use CAP to discover optimal causal AI model
3. analyze the model for root causes of overweight
4. advise physicians on solutions to weightloss
Title: Health Problem Solving by AI MC
Abstract: Extending AI ML, AI MC is next-gen AI of machine causality (MC) for machine problem solving.
In this study, we use AI MC technology to answer the question: is diet more effective than activities for weight loss?
A public dataset recording seventeen (17) observable attributes of 2111 users has been used for AI MC modeling.
We find that two latent variables (diet and activities) can be derived from the 17 attributes to determine weight.
Based on the good-fit AI causal model, we further discover that diet plays a more important role than activities in weight loss.
This study demonstrates that AI MC technology can discover new health findings or confirm findings previously unclear,
and then effectively guide the solution development for the health problem.
Create a survey with the questions (AI MC data by natural vs. lab experiment)
Analyze models for root causes of overweight Factor 1 (f1): diet – major impact on weight. FAVCyes (high caloric food), CH2O (water)
Factor 2 (f2): activity – minor impact on weight
Advice to physcians: Diet (4.94) > Activities (-0.03)
Physicans to patients: Keto diet for weightloss?
Tech: nextgen AI, causal algorithm, container, cloud
This: demonstrate AI MC for health problem solving
Next: more examples for AI MC by Causal AI Platform Who cares: physicians, patients, researchers, technologists
