Modern advances in machine learning (ML) and wearable medical sensors (WMSs)
in edge devices have enabled ML-driven disease detection for smart healthcare.
Conventional ML-driven disease detection methods rely on customizing individual
models for each disease and its corresponding WMS data. However, such methods
lack adaptability to distribution shifts and new task classification classes.
Also, they need to be rearchitected and retrained from scratch for each new
disease. Moreover, installing multiple ML models in an edge device consumes
excessive memory, drains the battery faster, and complicates the detection
process. To address these challenges, we propose DOCTOR, a multi-disease
detection continual learning (CL) framework based on WMSs. It employs a
multi-headed deep neural network (DNN) and an exemplar-replay-style CL
algorithm. The CL algorithm enables the framework to continually learn new
missions where different data distributions, classification classes, and
disease detection tasks are introduced sequentially. It counteracts
catastrophic forgetting with a data preservation method and a synthetic data
generation module. The data preservation method efficiently preserves the most
informative subset of training data from previous missions based on the average
training loss of each data instance. The synthetic data generation module
models the probability distribution of the real training data and then
generates as much synthetic data as needed for replays while maintaining data
privacy. The multi-headed DNN enables DOCTOR to detect multiple diseases
simultaneously based on user WMS data. We demonstrate DOCTOR's efficacy in
maintaining high multi-disease classification accuracy with a single DNN model
in various CL experiments. DOCTOR achieves very competitive performance across
all CL scenarios relative to the ideal joint-training framework while
maintaining a small model size.

本文提出了基于可穿戴医疗传感器的、适用于多病种的连续学习框架 DOCTOR，采用多头深度神经网络和举例回放样式的连续学习算法，实现智能医疗领域中多疾病检测任务的高效处理。