Machine learning drug discovery based on graph neural network and large language model

The COVID-19 pandemic has presented an urgent need to understand the long-term health implications faced by survivors. Post-COVID-19 complications, such as acute kidney injury, arrhythmia, and stroke, pose significant challenges to public health. Despite extensive research on COVID-19 complications, a comprehensive understanding of the risk factors remains elusive due to the potential confounding variables present in the data. Traditional statistical models, while insightful, may not fully capture the causal relationships between these risk factors and post-COVID-19 complications. Motivated by this gap in the literature, we propose a novel approach using causal inference models to predict the likelihood of post-COVID-19 complications based on patient demographics and pre-existing conditions. Our model, trained on a dataset of COVID-19 inpatients in Wuhan Province, China, estimates the causal effect of these factors on the likelihood of patients experiencing post-COVID-19 complications. This approach allows us to isolate the causal impact of each factor while accounting for potential confounders, providing a more accurate understanding of the underlying mechanisms driving these relationships. Unlike traditional models that predict the probability of certain outcomes, our model provides insights into the causal relationships between risk factors and complications, offering a more reliable and comprehensive understanding of the underlying mechanisms. This approach can help identify at-risk patients, inform targeted interventions, and contribute to the development of effective prevention and treatment strategies. Our work aims to contribute to the current understanding of the virus and inform public health policies and interventions.