Role of Artificial Intelligence in Cardiovascular Risk Prediction and Prevention

Globally, cardiovascular diseases (CVDs) continue to be the leading cause of death, making precise risk assessment and efficacious preventative measures imperative. Although essential, traditional cardiovascular risk assessment instruments like the Framingham Risk Score have shortcomings when it comes to precisely identifying individual risks. The use of Artificial Intelligence (AI) into the prediction of cardiovascular risk presents a revolutionary strategy to overcome these constraints. Artificial Intelligence (AI), which includes deep neural networks and machine learning algorithms, improves risk assessment through the analysis of large datasets, allowing for personalised risk forecasts that go beyond traditional risk indicators. The transition from population-based risk assessment to individualised profile is signalled by this integration, which will increase accuracy and facilitate prompt actions. AI-powered models outperform conventional approaches in detecting complex risk variables and trends, providing higher forecasting accuracy. These models provide personalised risk profiles by utilising a variety of data sources, such as lifestyle, medical imaging, and genetic information. This allows for more focused preventative actions. In addition, AI applications in preventive cardiology include risk assessment, customised care plans, and early diagnosis via sophisticated imaging analysis. Widespread adoption is hampered, nevertheless, by issues with data quality, AI model interpretability, generalizability across different populations, and ethical issues. In order to fully utilise AI to transform preventive cardiology and emphasise openness, morality, and ongoing technological breakthroughs, it will be essential to overcome these obstacles

Symmetry Breaking Constraints: Recent Results

Symmetry is an important problem in many combinatorial problems. One way of dealing with symmetry is to add constraints that eliminate symmetric solutions. We survey recent results in this area, focusing especially on two common and useful cases: symmetry breaking constraints for row and column symmetry, and symmetry breaking constraints for eliminating value symmetryComment: To appear in Proceedings of Twenty-Sixth Conference on Artificial Intelligence (AAAI-12

Limits of Preprocessing

We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity theoretic assumption, none of the considered problems can be reduced by polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, such as induced width or backdoor size. Our results provide a firm theoretical boundary for the performance of polynomial-time preprocessing algorithms for the considered problems.Comment: This is a slightly longer version of a paper that appeared in the proceedings of AAAI 201

Quantum Mechanics of 'Conscious Energy'

This paper is aiming to investigate the physical substrate of conscious process. It will attempt to find out: How does conscious process establish relations between their external stimuli and internal stimuli in order to create reality? How does consciousness devoid of new sensory input result to its new quantum effects? And how does conscious process gain mass in brain? This paper will also try to locate the origins of consciousness at the level of neurons along with the quantum effects of conscious process