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Objective To create and verify a machine learning model that integrates social determinants of health (SDoH) for assessing post-stroke depression (PSD) and examining the association between SDoH and disease outcomes. Methods Data were acquired from the National Health and Nutrition Examination Survey. Logistic regression was employed to analyse the association between SDoH and PSD, whereas Cox regression was utilized to assess the correlation between SDoH and all-cause mortality in PSD. The Boruta algorithm was employed for feature selection, and four machine learning models were constructed (CatBoost, Logistic, Multilayer Perceptron, and Random Forest) to evaluate the predictive effectiveness, calibration, and clinical applicability of these ML models. SHAP values were computed to ascertain the predictive significance of each feature in the model that exhibited the highest predictive performance. Results Logistic regression analysis revealed a significant positive correlation between SDoH and PSD prevalence( p for trend < 0.0001). Compared to the other three models, CatBoost (AUC = 0.966) demonstrated the best overall predictive performance. Moreover, the decision curve analysis (DCA) and calibration curve findings demonstrated that the CatBoost model possessed considerable clinical utility and consistent predictive efficacy. The ten-fold cross-validation method further confirmed the model’s robustness and generalization ability. Conclusions A linear relationship exists between SDoH and PSD, with CatBoost demonstrating the best performance in predicting PSD. SHAP values emphasize the importance of SDoH.

This study takes the implementation of the “Guiding Opinions on Launching Pilot Programs for Mandatory green Insurance” in 2013 as a quasi-natural experiment. Based on the panel data of A-share listed companies from 2008 to 2023, it systematically examines the impact of the green insurance policy on the performance of heavy-polluting enterprises by using the difference-in-differences (DID) method. The research findings show that the green insurance policy significantly improves the performance level of heavy-polluting enterprises. Mechanism tests indicate that the green insurance promotes the improvement of corporate performance through two paths: incentivizing enterprises’ technological innovation and enhancing their short-term debt financing capacity. Further heterogeneity analysis reveals that the policy has a more prominent role in improving the performance of enterprises with lower environmental investment levels, non-state-owned nature, and less financing constraints. The test of moderating effects finds that the board size negatively moderates the promoting effect of the green insurance on corporate performance, while the proportion of independent directors exerts a positive moderating effect. This study provides theoretical support and empirical evidence for the optimal design of the green insurance policy and the green and low-carbon transformation of heavy-polluting enterprises, and has important implications for improving the green insurance system and serving the high-quality development of the economy.

Motivated by the previous success of Two-Dimensional Convolutional Neural Network (2D CNN) on image recognition, researchers endeavor to leverage it to characterize videos. However, one limitation of applying 2D CNN to analyze videos is that different frames of a video share the same 2D CNN kernels, which may result in repeated and redundant information utilization, especially in the spatial semantics extraction process, hence neglecting the critical variations among frames. In this paper, we attempt to tackle this issue through two ways. 1) Design a sequential channel filtering mechanism, i.e., Progressive Enhancement Module (PEM), to excite the discriminative channels of features from different frames step by step, and thus avoid repeated information extraction. 2) Create a Temporal Diversity Loss (TD Loss) to force the kernels to concentrate on and capture the variations among frames rather than the image regions with similar appearance. Our method is evaluated on benchmark temporal reasoning datasets Something-Something V1 and V2, and it achieves visible improvements over the best competitor by 2.4% and 1.3%, respectively. Besides, performance improvements over the 2D-CNN-based state-of-the-arts on the large-scale dataset Kinetics are also witnessed.