Technical Architecture
Last updated
Last updated
Integrates data sources from wearable devices (such as the PulseX smart bracelet), medical devices, mobile applications, etc., and uploads health data via Bluetooth, LoRa, Wi-Fi, and other protocols.
Uses decentralized storage (such as IPFS) to protect user data privacy while reducing the risk of single-point failures.
Incorporates federated learning, differential privacy, and homomorphic encryption to ensure the anonymized storage and analysis of user data.
Trains models based on health data using Transformer architectures (e.g., BioBERT) for pre-training on massive datasets to enhance foundational understanding.
Through federated learning, models are continually updated on the user side, ensuring data privacy while maintaining personalization and generalization.
Employs time-series analysis models (e.g., RNN, LSTM) to predict user health trends, and integrates reinforcement learning to provide personalized treatment recommendations.
Utilizes multi-task learning models to analyze the complex relationships between physiological data and disease characteristics.
Deploys models using ONNX Runtime, combining edge AI technologies for low-latency inference on devices.
Integrates Generative Adversarial Networks (GAN) or diffusion models to simulate various treatment options and recommend the optimal choice for users.
Converts health data into tokens compliant with ERC-721 or ERC-1155 standards, managed through blockchain smart contracts for access control and revenue distribution.
Offers open APIs and SDKs, enabling healthcare institutions and researchers to use anonymized data for new drug development, disease research, and DeSci collaboration.
