Developing a risk prediction model for COVID-19 infection in heart transplant recipients using artificial intelligence

Aim: Describe the utility of an inverse reinforcement learning pathway to develop a novel model to predict and manage the spread of COVID-19. Materials & methods: Convolutional neural network (CNN) with multilayer perceptron (MLP) modeling functions utilized inverse reinforcement learning to predict COVID-19 outcomes based on a comprehensive array of factors. Results: Our model demonstrates a sensitivity of 0.67 in the receiver operating characteristic curve and can correctly identify approximately 67% of the positive cases. Conclusion: We demonstrate the ability to augment clinical decision-making with a novel artificial intelligence (AI) solution that accurately predicted the susceptibility of transplant patients to COVID-19. This enables physicians to administer treatment and take appropriate preventative measures based on patients' risk factors. Artificial intelligence (AI) is about creating intelligent machines that can replicate human cognitive functions like learning and problem-solving. These AI systems can help to predict and address problems, and their power lies in learning from large datasets. AI can help to understand and respond to COVID-19 risks, particularly for certain groups of patients, like heart transplant patients. Figuring out how COVID-19 affects heart transplant patients is tough because we don't have much information about it. Combining medical knowledge with AI seems to be an effective way to solve problems during the pandemic. However, using AI in medicine is challenging as the use and adoption of AI in clinical practice remains limited, with many AI algorithms at the design and development stage. Considerable progress has been made, but continued research is needed for better monitoring and diagnosis.