Existing deepfake detection methods heavily rely on specific training data distributions and struggle to generalize to unknown forgery techniques. To address the challenge, this paper focuses on two critical research gaps: (1) the lack of systematic mining of standard features across multiple forgery methods; (2) the unresolved distribution shift problem in the strong supervised learning paradigm. To tackle these issues, we propose a self-supervised learning framework based on feature disentanglement, which enhances the generalization ability of detection models by uncovering the intrinsic features of forged content. The core method comprises three key components: self-supervised sample construction and training samples for feature disentanglement, which are generated via an image self-mixing mechanism; feature disentanglement network, where the input image is decomposed into two parts—content features irrelevant to forgery and discriminative forgery-related features; and conditional decoder verification, where both types of features are used to reconstruct the image, with forgery-related features serving as conditional vectors to guide the reconstruction process. Orthogonal constraints on features are enforced to mitigate the overfitting problem in traditional methods. Experimental results demonstrate that, compared with state-of-the-art methods, the proposed framework exhibits superior generalization performance in cross-unknown forgery technique detection tasks, effectively breaking through the dependency bottleneck of traditional supervised learning on training data distributions. This study provides a universal solution for deepfake detection that does not rely on specific forgery techniques. The model’s robustness in real-world complex scenarios is significantly improved by mining the common essence of forgery features.

Reliable mortality risk prediction help professional medical staffs make better decisions for patient healthcare. While previous death risk prediction tools and models for critically ill patients have always been depended on labor-intensive collection of data and mainly focused on static features, which may affect the applicability and accuracy of the models. By utilizing routine time-series clinical data collected during regular workflows of patient care, this study developed an mortality risk prediction model based on an LSTM network which incorporates with a dual self-attention design, which is designed specifically to enhance the model’s focus on critical periods and important features. The model’s performance was further compared with the traditional machine learning algorithms in an independent testing dataset. The results suggested that the LSTM model incorporating dual self-attention performed best in predicting risk of death. With an excellent AUROC value of 0.9583, and an AUPRC value of 0.8191, It has a very good calibration performance verified by a much lower brier score. Furthermore, we confirmed that physiologic variables related to respiration and circulation are key predictors of mortality. The desirable predictive performance of this model provides additional evidence for its use in clinical practice.