A BERT-Span model for Chinese named entity recognition in rehabilitation medicine.

Background: Due to various factors such as the increasing aging of the population and the upgrading of people's health consumption needs, the demand group for rehabilitation medical care is expanding. Currently, China's rehabilitation medical care encounters several challenges, such as inadequate awareness and a scarcity of skilled professionals. Enhancing public awareness about rehabilitation and improving the quality of rehabilitation services are particularly crucial. Named entity recognition is an essential first step in information processing as it enables the automated extraction of rehabilitation medical entities. These entities play a crucial role in subsequent tasks, including information decision systems and the construction of medical knowledge graphs. Methods: In order to accomplish this objective, we construct the BERT-Span model to complete the Chinese rehabilitation medicine named entity recognition task. First, we collect rehabilitation information from multiple sources to build a corpus in the field of rehabilitation medicine, and fine-tune Bidirectional Encoder Representation from Transformers (BERT) with the rehabilitation medicine corpus. For the rehabilitation medicine corpus, we use BERT to extract the feature vectors of rehabilitation medicine entities in the text, and use the span model to complete the annotation of rehabilitation medicine entities. Result: Compared to existing baseline models, our model achieved the highest F1 value for the named entity recognition task in the rehabilitation medicine corpus. The experimental results demonstrate that our method outperforms in recognizing both long medical entities and nested medical entities in rehabilitation medical texts. Conclusion: The BERT-Span model can effectively identify and extract entity knowledge in the field of rehabilitation medicine in China, which supports the construction of the knowledge graph of rehabilitation medicine and the development of the decision-making system of rehabilitation medicine.

Exploring emerging IoT technologies in smart health research: a knowledge graph analysis.

BACKGROUND: At present, Internet of Things technology has been widely used in various fields, and smart health is also one of its important application areas. METHODS: We use the core collection of Web of Science as a data source, using tools such as CiteSpace and bibliometric methods to visually analyze 9561 articles published in the field of smart health research based on the Internet of things (IoT) in 2003-2019, including time distribution, spatial distribution, and literature co-citation analysis and keyword analysis. RESULTS: The field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. In addition, the knowledge base in this field has been initially formed, and most of the published literatures are multi-theme research. CONCLUSIONS: This study discusses the research status, research hotspots and future development trends in this field, and provides important knowledge support for subsequent research.

Innovating New Rural Cooperative Medical Scheme (NCMS) for Better Patient Satisfaction in Rural China.

With the broadening application of the New Rural Cooperative Medical Scheme (NCMS), its role in patient satisfaction in rural China has shifted to be the focus of academic research. Based on a technology acceptance model, this study will investigate the factors and mechanisms influencing patient satisfaction on NCMSS in rural places in China. In this study, based on a technology acceptance model, we developed a model that is associated with the influencing factors, patients' continued participation and patient satisfaction, and conducted an empirical analysis based on data collected from rural areas of China's Anhui Province. A NCMS's reputed reliability, value, and convenience played a key role in positively influencing patient satisfaction. However, long-term patient participation was not significantly influenced by other social factors. In order to increase patient satisfaction, NCMS policy and implementation procedure needs further government modification and innovation with the goal of improving the reimbursement ratio, reducing the financial burden, and improving patient convenience.