ABSTRACT
Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients' lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.
ABSTRACT
[This corrects the article DOI: 10.1007/s00779-021-01520-9.].
ABSTRACT
BACKGROUND: With the continuation of the COVID-19 pandemic, some COVID-19 patients have become reinfected with the virus. Viral gene sequencing has found that some of these patients were reinfected by the different and others by same strains. This has raised concerns about the effectiveness of immunity after infection and the reliability of vaccines. To this end, we conducted a systematic review to assess the characteristics of patients with reinfection and possible causes. METHODS: A systematic search was conducted across eight databases: PubMed, Embase, Web of Science, The Cochrane Library, CNKI, WanFang, VIP and SinoMed from December 1, 2019 to September 1, 2021. The quality of included studies were assessed using JBI critical appraisal tools and Newcastle-Ottawa Scale. RESULTS: This study included 50 studies from 20 countries. There were 118 cases of reinfection. Twenty-five patients were reported to have at least one complication. The shortest duration between the first infection and reinfection was 19 days and the longest was 293 days. During the first infection and reinfection, cough (51.6% and 43.9%) and fever (50% and 30.3%) were the most common symptoms respectively. Nine patients recovered, seven patients died, and five patients were hospitalized, but 97 patients' prognosis were unknown. B.1 is the most common variant strain at the first infection. B.1.1.7, B.1.128 and B.1.351 were the most common variant strains at reinfection. Thirty-three patients were infected by different strains and 9 patients were reported as being infected with the same strain. CONCLUSIONS: Our research shows that it is possible for rehabilitated patients to be reinfected by SARS-COV-2. To date, the causes and risk factors of COVID-19 reinfection are not fully understood. For patients with reinfection, the diagnosis and management should be consistent with the treatment of the first infection. The public, including rehabilitated patients, should be fully vaccinated, wear masks in public places, and pay attention to maintaining social distance to avoid reinfection with the virus.