Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019

Zhou, Dongnan; Zhu, Di; Li, Nana; Han, Bing

doi:10.1097/jom.0000000000002258

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…The coefficient of determination, average fitting relative error, and relative error of prediction of the GAM were better than those of the other two models. 19 The ARIMA model was also suitable for predicting the future number of patients with COPD in China. As a common time-series prediction method, the ARIMA model fully utilizes the temporal information of the original dataset to make accurate predictions.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

Teng,

Jian,

Chen

et al. 2023

COPD

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To predict the future number of patients with chronic obstructive pulmonary disease (COPD) in China and compare the three prediction models. Methods: A generalized additive model (GAM), autoregressive integrated moving average (ARIMA) model, and curve-fitting method were used to fit and predict the number of patients with COPD in China. Data on the number of patients with COPD in China from 1990 to 2019 were obtained from the Global Burden of Disease (GBD) database. The coefficient of determination (R 2 ), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), relative error of prediction, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and compare the fitting effect, prediction effect, and reliability of the three models. Results: The GAM, ARIMA, and curve-fitting methods could predict future trends in COPD in China. The performance of the GAM is the best among the three models, whereas the curve fitting method is the worst, and the ARIMA (0,1,2) model is in between. The prediction results of the three models showed that the number of patients with COPD in China is expected to increase from 2020 to 2025. Conclusion: GAM and AIRMA models are recommended for predicting the future prevalence of COPD in China. The number of patients with COPD in China is expected to increase in the next few years. The prevention and control of COPD in China still needs to be strengthened. Using appropriate models to predict future trends in COPD will provide support for health policymakers.

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Section: Discussionmentioning

confidence: 99%

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

Teng,

Jian,

Chen

et al. 2023

COPD

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“…The model can extract hidden patterns and interaction patterns between elements in fashion trends from massive data, helping designers better present and convey the concept of fashion art . Mahaveerakannan R et al Automated prediction and detection of forest fires using artificial intelligence techniques [6]. Meanwhile, predictive models are a powerful competitive tool for clothing companies.…”

Section: Introductionmentioning

confidence: 99%

Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design

Huang,

Zhang

2024

SCPE

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With the rapid development of the fashion industry and the increasing diversification of consumer needs, accurately predicting the fashion trends of clothing elements has become an urgent problem in the field of art design. In order to solve these problems, this paper uses deep learning technology for fashion trend prediction and optimization of prediction models. Firstly, the EfficientNet-b7 model is constructed as an attribute predictor to accurately extract the attributes of clothing image elements. Then, based on user information, popular elements are grouped and counted to solve the problem of different populations having different opinions on popular trends. The prediction model is constructed based on the bidirectional long short-term memory network encoder decoder framework, which trains trend information as a whole and utilizes element coexistence relationships to assist in trend prediction. Meanwhile, the study uses random sampling method for clothing original adoption, and the experimental results show that the model considering coexistence relationship performs the best in terms of mean absolute error and mean absolute percentage error, which are 0.0132 and 14.68%, respectively. In addition, the model that introduces attention mechanism based on trend similarity has improved by 5.09% and 4.54% on two indicators compared to the latest model. The experimental results indicate that coexistence relationships can help improve the performance of prediction models. The attention mechanism based on trend similarity can further improve model performance by similarity comparison between historical information and changing trend, and selecting similar historical information as an important influencing factor for future trends.

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“…According to the International Labor Organization, approximately 2.3 million people die from work-related diseases and injuries each year (2). The annual number of occupational diseases diagnosed in China increased from 12,212 in 2005 to 19,428 in 2019, with an average annual growth rate of 3.37% (3,4). The Chinese Government has prioritized occupational health as one of 15 major health projects (5), and the National Health Commission (NHC) has unveiled new strategies to reduce the incidence of occupational diseases, focusing on major industries, occupational hazards, and victims (6).…”

Section: Introductionmentioning

confidence: 99%

Implementation Evaluation of a Cluster Randomized Controlled Trial to Promote the Use of Respiratory Protective Equipment Among Migrant Workers Exposed to Organic Solvents in Small and Medium-Sized Enterprises

Lin

Zou

et al. 2022

Front. Public Health

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BackgroundWhile the effectiveness of several occupational healthcare interventions has been demonstrated, successful implementation of such programs among internal migrant workers (IMWs) in small and medium-sized enterprises (SMEs) has been limited. This study aimed to evaluate the implementation of a three-arm cluster randomized controlled trial promoting respiratory protective equipment (RPE) use among IMWs exposed to organic solvents in SMEs and to assess the association between participants' compliance and effectiveness of intervention.MethodsA total of 60 SMEs were randomly allocated to a low- or high-intensive intervention group, or a control group that did not receive any intervention. The low-intensive intervention group was subjected to both traditional and mHealth occupational health education. The high-intensive intervention group was subjected to the low-intensive group activities and peer education. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to guide implementation evaluation of this 6-months intervention. Generalized linear mixed models (GLMMs) were used to evaluate the effects of participants' compliance with the intervention on the primary outcomes, regarding the appropriate use of RPE.ResultsOf 4,527 potentially eligible participants, 1,211 individuals were enrolled, with a reach rate of 26.8%. Sixty of the 66 SMEs approached (90.9%) SMEs adopted the intervention. Fidelity to traditional education (100.0%) and mHealth intervention (97.5%) was higher than fidelity to peer education (20.0%). Peer leaders cited inconvenient time and unfamiliarity with peers as two major barriers to delivering peer education. Compared with the control group, IMWs who complied with the interventions in both groups were more likely to wear RPE appropriately [low-intensive group: adjusted odds ratio (aOR) = 2.58, 95% confidence interval (CI): 1.56–4.28; high-intensive group: aOR = 7.52, 95% CI: 3.72–15.23]. Most participants (95.8%) were satisfied with the program and 93.1% stated that they would maintain the use of RPE in the future.ConclusionsA multi-component occupational health intervention to promote the use of RPE among IMWs in SMEs was feasible and acceptable. Peer education had great potential to enhance the occupational health behavior of IMWs, and thus strategies to improve participants' adherence to this component warrant further investigation.Clinical Trial Registrationhttp://www.chictr.org.cn, identifier: ChiCTR-IOR-15006929.

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Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019

Cited by 5 publications

References 24 publications

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design

Implementation Evaluation of a Cluster Randomized Controlled Trial to Promote the Use of Respiratory Protective Equipment Among Migrant Workers Exposed to Organic Solvents in Small and Medium-Sized Enterprises

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