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.

翻译：现代机器学习（ML）与边缘设备中可穿戴医疗传感器（WMSs）的进展，推动了面向智慧医疗的ML驱动疾病检测方法。传统ML驱动的疾病检测方法依赖于针对每种疾病及其对应WMS数据定制个体模型。然而，此类方法缺乏对数据分布偏移和新任务分类类别的适应性。此外，它们需要为每种新疾病重新设计架构并从头训练。更关键的是，在边缘设备中部署多个ML模型会消耗过多内存、加快电池耗电速度，并增加检测流程的复杂度。为解决上述挑战，我们提出DOCTOR——一种基于WMS的多疾病检测持续学习（CL）框架。该框架采用多头深度神经网络（DNN）与范例重放型CL算法。CL算法使框架能够持续学习新任务，其中不同数据分布、分类类别及疾病检测任务被依次引入。它通过数据保存方法和合成数据生成模块来对抗灾难性遗忘。数据保存方法基于每个数据实例的平均训练损失，高效保留先前任务中最具信息量的训练数据子集。合成数据生成模块对真实训练数据的概率分布进行建模，随后生成重放所需的任意数量合成数据，同时维护数据隐私。多头DNN使DOCTOR能够基于用户WMS数据同时检测多种疾病。我们在多项CL实验中展示了DOCTOR仅使用单个DNN模型即可保持高多疾病分类准确率的有效性。相较于理想的联合训练框架，DOCTOR在全部CL场景中均展现出极具竞争力的性能，同时保持较小的模型尺寸。