Artificial Intelligence in Predicting Early and Long Term Outcomes in Cardiovascular Disease

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Heart attacks remains the most common cause of premature death in the world. Despite modern medical treatment, there is increased risk of these patients having further heart attacks or indeed sudden death. This is often very difficult to predict in short or long term basis. Several risk prediction models have been developed to study the risk of recurrent events. However, these risks scores are not always accurate and do not perform very well in all patients. This is due to a number of factors including the fact that the diagnostic criteria and definitions of heart attack have evolved over time. Furthermore, the treatment strategies following a heart attack has progressed significantly over last many years.Newer data analysis techniques based on artificial intelligence programme have been developed in the last few years that are superior to traditional utilised statistical analysis, and are currently utilized in several scientific fields. We proposed to use the machine learning techniques to analyse the data gathered from patients with heart attacks to see if these provide superior risk prediction of cardiac events following a heart attack.Using state of the art technology, we will analyse the routinely collected data from the electronic records and pre-existing database in patients with heart attacks at the Bristol Heart Institute. Using machine learning techniques, we will create algorithms that may better predict future cardiovascular events in the first phase in about 10000 patients. The will be undertaken after testing several techniques that are well established within the field of machine learning. We will use then use the model that best predicts the future cardiac events in patient data in patients with heart attack a national level in a big data analysis.In addition, we will also study the utility of this model to predict long term outcomes following a heart attack. Furthermore, pathways in patient management in patients with heart attack will be studied, using a technique called as process mining. This will allow us to identify the bottleneck in admission to treatment of these patients.If the study confirms the performance of the new model better than pre-existing, this is likely to have significant impact on patient care straightaway. It will help us identify patients that are at the highest risk of future cardiac events. Ultimately, this the transform the care in these patient groups by more intensive monitoring and treatment.