With the ongoing expansion of tourism, a conflict has arisen between economic growth in the tourism industry and environmental preservation, which has attracted the interest of government and academic groups. Because it enables the adaption of tourist activities and buildings in the tourism area in order to protect the natural resources of the scenic area while seeking economies of scale, the tourism environmental carrying capacity system is an essential tool for resolving this conundrum. It also enables tourist sites to grow sustainably while understanding their limitations and carrying capacity. This study uses Citespace 6.1.2 and VOSviewer 1.6.18 analysis software to conduct a bibliometric analysis and review of 297 articles on tourism environmental carrying capacity. This analysis includes early warning studies, assessment models and management tools, and analyses of keyword co-occurrence and emergent word co-occurrence. The article’s conclusion makes recommendations for further research, including the division of each interest group, improved dynamic forecast and early warning of tourism environmental carrying capacity, and the development of an objective, scientific model of tourism carrying capacity.
Background: Machine learning (ML) and deep learning(DL) technology has been used widely in the quality assurance. Due to the complexity of intensity modulated radiotherapy(IMRT)technology, the implementation of patient-specific quality assurance (PSQA) before the treatment has become an essential part in the IMRT. Therefore, this paper is aim to establish the different machine learning classification predict models of gamma pass rates for specific dosimetric verification of pelvic IMRT plan which based on the radiomic features and to explore the best prediction model. Methods: Retrospective analysis of the 3D dosimetric verification results based on measurements with gamma pass rate criteria of 3%/2 mm and 10% dose threshold of 196 pelvic intensity-modulated radiotherapy plans was carried. Prediction models were established by extracting radiomic features data. Four machine learning algorithms, random forest, support vector machine, adaptive boosting and gradient boosting decision trees, were used to calculate the AUC value, sensitivity and specificity respectively. The classification performance of the four prediction models were evaluated. Results: The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision trees models were 0.93,0.85,0.93,0.96, and 0.38,0.69,0.46, and 0.46, respectively. The AUC values for the random forest model and the adaptive boosting model were 0.81 and 0.82, respectively, and the AUC values for the support vector machine and gradient boosting decision tree models were 0.87. Conclusions: Machine learning methods based on radiomics can be used to establish a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity modulated radiotherapy. The classification performance of support vector machine model and gradient boosting decision trees model is better than that of random forest model and adaptive boosting model.The prediction model for a specific site is helpful to improve the performance of the model.
Background SHAP values are suggested as a unique measure of feature importance in machine learning prediction models. It can explain the output of any machine learning prediction model and can also participate in the construction of machine learning prediction models as a feature selection mechanism for handling high-dimensional data. In this study ,the SHAP values and extreme gradient boosting(XGBoost) algorithm were combined to select the best radiomics features for the establishment of the gamma pass rate(GPR) prediction model.The feasibility and effectiveness of the prediction model were evaluated . Methods Retrospective analysis of the 3D dosimetric verification results based on measurements with GPR criteria of 3%/2 mm and 10% dose threshold of 196 pelvic intensity-modulated radiation therapy (IMRT) was carried. Radiomic features were extracted from the dose files, from which the XGBoost algorithm based on SHAP values was used to select the optimal feature subset as the input for the prediction model. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140 respectively, and the AUC values, recall and F1 scores were calculated to assess the classification performance of the prediction models. Results The prediction model constructed based on the 110 features selected by SHAP values had an AUC value of 0.81, a recall of 0.93 and an F1 score of 0.82, which were better than the other three models. Conclusion It is feasible to use the SHAP values in combination with the XGBoost algorithm to select the best subset of radiomic features for the GPR prediction models. The global explanations and single-sample explanations of the model output through SHAP values may offer reference for medical physicists to provide high-quality plans, promoting the clinical application and implementation of GPR prediction models, and providing safe and efficient personalized QA management for patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.