[Establishment of comprehensive evaluation models of physical fitness of the elderly based on machine learning].

The present study aims to establish comprehensive evaluation models of physical fitness of the elderly based on machine learning, and provide an important basis to monitor the elderly's physique. Through stratified sampling, the elderly aged 60 years and above were selected from 10 communities in Nanchang City. The physical fitness of the elderly was measured by the comprehensive physical assessment scale based on our previous study. Fuzzy neural network (FNN), support vector machine (SVM) and random forest (RF) models for comprehensive physical evaluation of the elderly people in communities were constructed respectively. The accuracy, sensitivity and specificity of the comprehensive physical fitness evaluation models constructed by FNN, SVM and RF were above 0.85, 0.75 and 0.89, respectively, with the FNN model possessing the best prediction performance. FNN, RF and SVM models are valuable in the comprehensive evaluation and prediction of physical fitness, which can be used as tools to carry out physical evaluation of the elderly.

[Development of comprehensive vitality scale for the elderly based on classical test theory and analytic hierarchy process].

The present study aims to construct an elderly vitality index evaluation system and develop a comprehensive vitality evaluation scale for the elderly to reasonably evaluate the vitality level of the elderly in China, so as to provide a reference for promoting the realization of "active aging" and "healthy aging". Literature research and in-depth interview were used to collect the senile vitality sensitive indexes. The indexes were screened and corrected by Delphi expert consultation method, item analysis method based on classical test theory, factor analysis method, and reliability and validity analysis method. The analytic hierarchy process was used to calculate the weight of each level of indexes. An elderly vitality evaluation system including 4 first-level indexes and 24 second-level indexes was constructed. The consistency test results of all levels of indicators showed that the consistency index (CI) and consistent ratio (CR) were both less than 0.1, which met the requirements and showed satisfactory consistency. The weights of exercise vitality, nutritional vitality, psychological vitality and social vitality were 0.263, 0.141, 0.455 and 0.141, respectively. In conclusion, the comprehensive vitality scale constructed for the Chinese elderly is reliable and scientific, and can be used to evaluate the vitality of the elderly.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer.

Objective: Using visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated. Methods: On April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used. Results: A total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal "New England Journal of Medicine" of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future. Conclusions: An increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.