Features and diversity of symptoms of moyamoya disease in adolescents: A cluster analysis

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

Analyzing symptom experiences and pathways to enhancing quality of life in adolescents with Moyamoya disease

PURPOSE

The psycho-emotional symptom experiences and health-related factors that influence the quality of life of adolescents with Moyamoya disease are still not fully understood. The aim of this study is to identify the daily physical and accompanying psychological symptom experiences of such adolescents, and to examine how health-related factors contribute to their quality of life.

DESIGN AND METHOD

This study employed a descriptive cross-sectional research design. Data on a total of 134 adolescents with Moyamoya disease was analyzed in order to develop a path analysis. Data were collected from October 2019 to January 2021 in South Korea. The theory of salutogenesis was employed in order to develop a hypothetical model for this path analysis. Participants were given a self-reporting questionnaire. Descriptive analysis and path analysis were conducted based on the results.

RESULTS

Adolescents with Moyamoya disease experienced several negative emotions simultaneously, regardless of the severity of their physical symptoms. The path analysis revealed that stress, a sense of coherence, behavior, and the support of friends were significant contributors to adolescents' quality of life.

CONCLUSIONS

Adolescents with Moyamoya disease have multifaceted symptom experiences in their daily lives. To enhance their quality of life, it is crucial to provide support that can help strengthen their capacity for stress management and enhance their sense of coherence.

PRACTICAL IMPLICATIONS

This study provides evidence that nurses should offer anticipatory information to adolescents with Moyamoya disease regarding the symptoms they will frequently encounter, and develop interventions to enhance their quality of life.

Construction of Diagnosis Model of Moyamoya Disease Based on Convolution Neural Network Algorithm

Objective

The convolutional neural network (CNN) was used to improve the accuracy of digital subtraction angiography (DSA) in diagnosing moyamoya disease (MMD), providing a new method for clinical diagnosis of MMD.

Methods

A total of 40 diagnosed with MMD by DSA in the neurosurgery department of our hospital were included. At the same time, 40 age-matched and sex-matched patients were selected as the control group. The 80 included patients were divided into training set (n = 56) and validation set (n = 24). The DSA image was preprocessed, and the CNN was used to extract features from the preprocessed image. The precision and accuracy of the preprocessed image results were evaluated.

Results

There was no significant difference in baseline data between the training set and validation set (P > 0.05). The precision and accuracy of the images before processing were 79.68% and 81.45%, respectively. After image processing, the precision and accuracy of the model are 96.38% and 97.59%, respectively. The area under the curve of the CNN algorithm model was 0.813 (95% CI: 0.718-0.826).

Conclusion

This diagnostic method based on CNN performs well in MMD detection.