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 methods for disease detection 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. In addition, 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 a 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 (SDG) module. The data preservation method preserves the most informative subset of real training data from previous missions for exemplar replay. The SDG module models the probability distribution of the real training data and generates synthetic data for generative replay while retaining 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 disease classification accuracy with a single DNN model in various CL experiments. In complex scenarios, DOCTOR achieves 1.43 times better average test accuracy, 1.25 times better F1-score, and 0.41 higher backward transfer than the naive fine-tuning framework with a small model size of less than 350KB.

翻译：机器学习和边缘设备中可穿戴医疗传感器的现代进展，使得基于机器学习的疾病检测在智慧医疗中成为可能。传统的机器学习驱动疾病检测方法依赖于为每种疾病及其对应的可穿戴医疗传感器数据定制独立模型。然而，此类方法缺乏对分布偏移和新任务分类类别的适应性。此外，每增加一种新疾病，都需要从头重新设计并重新训练模型。而且，在边缘设备中安装多个机器学习模型会消耗过多内存、加快电池消耗并使检测过程复杂化。为应对这些挑战，我们提出了DOCTOR，一种基于可穿戴医疗传感器的多疾病检测持续学习框架。它采用多头深度神经网络和一种回放式持续学习算法。该持续学习算法使框架能够持续学习新的任务，这些任务中不同的数据分布、分类类别和疾病检测任务是顺序引入的。它通过一种数据保留方法和一个合成数据生成模块来抵消灾难性遗忘。数据保留方法从先前任务中保留最具信息量的真实训练数据子集用于样本回放。合成数据生成模块对真实训练数据的概率分布进行建模，并生成用于生成式回放的合成数据，同时保留数据隐私。多头深度神经网络使DOCTOR能够基于用户可穿戴医疗传感器数据同时检测多种疾病。我们在各种持续学习实验中证明了DOCTOR在单一深度神经网络模型下保持高疾病分类准确率的有效性。在复杂场景下，DOCTOR实现了比朴素微调框架高1.43倍的平均测试准确率、高1.25倍的F1分数以及高0.41的后向传递，且模型尺寸小于350KB。