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：现代机器学习（ML）与边缘设备中可穿戴医疗传感器（WMSs）的进步，推动了面向智慧医疗的ML驱动疾病检测方法发展。传统基于ML的疾病检测方法依赖于为每种疾病及其对应的WMS数据定制独立模型。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，且每新增一种疾病都需要重新设计架构并从零开始训练。此外，在边缘设备中部署多个ML模型会过度占用内存、加速电池消耗并增加检测流程复杂度。为解决上述挑战，我们提出DOCTOR——一种基于WMSs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）和回放式CL算法：CL算法使框架能够持续学习按序引入不同数据分布、分类类别及疾病检测任务的新任务；通过数据保存方法和合成数据生成（SDG）模块抑制灾难性遗忘。数据保存方法保留先前任务中信息量最大的真实训练数据子集用于示例回放；SDG模块在保留数据隐私前提下，对真实训练数据的概率分布建模并生成合成数据用于生成式回放。多头DNN使DOCTOR能基于用户WMS数据同时检测多种疾病。我们通过多项CL实验证明，DOCTOR使用单个DNN模型即可维持高疾病分类准确率。在复杂场景下，相较于模型大小不足350KB的朴素微调框架，DOCTOR的平均测试准确率提升1.43倍，F1分数提升1.25倍，后向迁移性能高出0.41。