Core Concepts
DOCTOR proposes a multi-disease detection continual learning framework based on wearable medical sensors to address challenges in disease detection methods, offering adaptability and memory efficiency.
Abstract
The DOCTOR framework introduces a novel approach to disease detection using wearable medical sensors and machine learning. It addresses issues of adaptability, memory consumption, and privacy concerns by employing a multi-headed deep neural network and a replay-style continual learning algorithm. The framework demonstrates improved accuracy and efficiency in detecting multiple diseases simultaneously.
Key points:
- Introduction of DOCTOR framework for multi-disease detection with wearable medical sensors.
- Challenges in conventional disease detection methods addressed by the proposed continual learning approach.
- Utilization of multi-headed deep neural network and replay-style algorithm for efficient disease detection.
- Demonstration of improved accuracy and efficiency in detecting multiple diseases simultaneously.
Stats
DOCTOR achieves 1.43× better average test accuracy, 1.25× better F1-score, and 0.41 higher backward transfer than naive fine-tuning.
Model size is less than 350KB.