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

心脏病仍然是世界上过早死亡的最常见原因。尽管有现代医学治疗，但这些患者进一步心脏病发作或猝死的风险增加。这通常很难在短期或长期内预测。已经开发了几种风险预测模型来研究复发事件的风险。然而，这些风险评分并不总是准确的，并不是在所有患者中都表现得很好。这是由于许多因素，包括心脏病发作的诊断标准和定义随着时间的推移而演变。此外，心脏病发作后的治疗策略在过去的许多年里取得了显着的进展。在过去的几年里，基于人工智能程序的更新的数据分析技术已经开发出来，上级传统的统计分析，目前在几个科学领域中使用。我们建议使用机器学习技术来分析从心脏病发作患者收集的数据，看看这些数据是否提供心脏病发作后心脏事件的上级风险预测。使用最先进的技术，我们将分析从布里斯托心脏研究所心脏病发作患者的电子记录和预先存在的数据库中常规收集的数据。使用机器学习技术，我们将创建算法，可以更好地预测约10000名患者在第一阶段的未来心血管事件。这将在测试机器学习领域内成熟的几种技术后进行。我们将在大数据分析中使用最能预测全国范围内心脏病发作患者数据中未来心脏事件的模型。此外，我们还将研究该模型在预测心脏病发作后长期结局方面的实用性。此外，将使用称为过程挖掘的技术研究心脏病发作患者的患者管理途径。这将使我们能够确定这些患者入院治疗的瓶颈。如果研究证实新模式的性能优于现有模式，这可能会对患者护理产生重大影响。它将帮助我们识别未来心脏事件风险最高的患者。最终，这将通过更密集的监测和治疗来改变这些患者群体的护理。

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.