The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event extraction has become a research hotspot in academia. Many academic conferences publish it as an evaluation task and provide a series of high-quality annotation data. This article aims at the characteristics of discrete attributes of tumor-related medical events and proposes a medical event. The standard extraction method realizes the combined extraction of the primary tumor site and primary tumor size characteristics, as well as the extraction of tumor metastasis sites. In addition, given the problems of the small number and types of annotation texts for tumor-related medical events, a key-based approach is proposed. A pseudo-data-generation algorithm that randomly replaces information in the whole domain improves the transfer learning ability of the standard extraction method for different types of tumor-related medical event extractions. The proposed method won third place in the clinical medical event extraction and evaluation task of the CCKS2020 electronic medical record. A large number of experiments on the CCKS2020 dataset verify the effectiveness of the proposed method.

Background: People with multiple sclerosis (PwMS) suffer from some comorbidities, including physical and psychiatric disorders, low quality of life (QoL), hormonal dysregulation, and hypothalamic-pituitary-adrenal axis dysfunction. The current study aimed to investigate the effects of eight weeks of tele-yoga and tele-Pilates on the serum levels of prolactin and cortisol and selected physical and psychological factors. Methods: Forty-five females with relapsing remitting multiple sclerosis, based on age (18–65), expanded disability status scale (0–5.5), and body mass index (20–32), were randomly assigned to tele-Pilates, tele-yoga, or control groups (n = 15). Serum blood samples and validated questionnaires were collected before and after interventions. Results: Following online interventions, there was a significant increase in the serum levels of prolactin (p = 0.004) and a significant decrease in cortisol (p = 0.04) in the time × group interaction factors. In addition, significant improvements were observed in depression (p = 0.001), physical activity levels (p < 0.001), QoL (p ≤ 0.001), and the speed of walking (p < 0.001). Conclusion: Our findings suggest that tele-yoga and tele-Pilates training could be introduced as patient-friendly, non-pharmacological, add-on therapeutic methods for increasing prolactin and decreasing cortisol serum levels and achieving clinically relevant improvements in depression, walking speed, physical activity level, and QoL in female MS patients.

The World Health Organization has identified nervous system diseases as one of the biggest public health problems, including autism spectrum disorder (ASD). Considering the extensive benefits of physical activity (PA), the literature on the PA research of ASD has increased each year, but there is a lack of bibliometric analyses in this field. To investigate the research achievements worldwide, this paper adopts bibliometrics to analyze the trend in the academic literature on the PA research of ASD published from 1980 to 2021. The documents were retrieved from the Web of Science database, and the search strategy was to combine the keywords related to “physical activity” and “autism spectrum disorder” by using the Boolean operator tools “OR” and “AND” in the title. A total of 359 English documents were retrieved. Microsoft Excel, Data Wrapper, VOSviewer, and Biblioshiny were used for the visual analysis. We found that the number of published documents increased the fastest from 2017 to 2021, which may be due to the promulgation of the Global Action Plan for Physical Activity 2018–2030 and the influence of COVID-19 on the world. The United States and the University of California systems are in the leading position in this field. Cooperation among countries with different levels of development will help to jointly promote the PA research progress on ASD. The focus themes include “individual effect”, “social support” and “activity dose”. The analysis of the frontier topic points out that researchers are paying increasing attention to how to improve the health and physical fitness of this group through PA. This research clearly puts forward a comprehensive overview, theme focus, and future trends in this field, which may be helpful to guide future research.