With the continuous development of new drugs and immunotherapy, the survival period of patient with multiple myeloma (MM) is continuously prolonged, and the disease is becoming chronic. Due to the involvement of multiple systems and numerous complications, the daily nursing for MM faces significant challenges. The doctor-nurse-patient integration model and the whole life cycle health management model for daily nursing of MM are expected to reduce the social burden related to diseases, improve patients’ quality of life, and reduce medical costs. This article provides a review on three aspects of MM doctor-nurse-patient integration, whole life cycle health management, and daily health management involving multiple systems.
Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
