Algorithmic resilience in an adverse event: Causal representation learning with foundation health models and digital twin simulation

Abstract

Unfavorable experiences also present sudden changes in the distribution of clinical data streams, which tend to cause significant deterioration in the performance of traditional clinical decision support algorithms. The models of artificial intelligence used to date are primarily missing the ability to be generalized across the acute perturbations of physiology or system because most of them are not algorithmically resilient. This paper presents a Causal Foundation Model, which combines causal representation learning with large pre trained multimodal foundation models and digital twin-based simulation to become better robust to adverse clinical events. The framework limits latent representations by matching them with underlying causal factors by structural causal models and interventional training and a digital twin environment is used to simulate controlled adverse events like septic shock, pulmonary embolism and equipment failure. The evaluation of model performance was done on intensive care unit outcome prediction tasks given conditions of a normal and unfavorable condition to determine that the results were all in a form of mean values with standard deviations and ninety five percent confidence intervals. The proposed model was found to have the lowest mean penalty error of organ failure score prediction of 0.214 +- 0.003 and Brier penalty mortality prediction on the first attempt of 0.078 +- 0.002 significant at a p < 0.01 compared to recurrent and transformer-based baselines. The reduction in the performance loss was found to be very significant p = 0.001 very significant paired statistical testing confirmed that the major clinical events. These findings indicate that within a context of causal constraints, foundation models, and training on digital twins, statistically significant and clinically significant increases in resilience, accuracy, and capability in early warning are achieved, which can be used to further make clinical-based artificial intelligence systems more reliable and trustworthy.