🔱Transfer Learning

Model Regional Adaptation

Utilizing region-specific health data (e.g., chronic disease characteristics in the Asian population) to fine-tune pre-trained models and adapt them to local health management needs.

Through Zero-Shot Learning, the model gains the ability for cross-regional application.

Example: Diabetes Management for the Asian Population

Given the high diabetes prevalence and its close association with dietary habits in the Asian population, pre-trained models are fine-tuned using health data from regions like China, Japan, and Korea. For example, the model can recognize insulin resistance characteristics common in the Asian population and, combined with local dietary habits (e.g., high carbohydrate intake), provide personalized dietary recommendations.

Personalized Fine-Tuning

Combining individual user data (e.g., genetic traits, lifestyle habits) to dynamically adjust model parameters and offer more accurate health recommendations.

Example: Exercise Recommendations Based on User Genetic Data For users carrying specific genetic variants (e.g., ACTN3 gene, related to muscle explosiveness), the model dynamically adjusts the exercise plan, recommending more explosive training (e.g., sprinting, weightlifting) rather than endurance training (e.g., long-distance running).