机器学习在心血管疾病辅助诊断模型中的效果

Effect of machine learning in the auxiliary diagnosis model of cardiovascular disease

我国心血管病死亡率居城乡居民总死亡率的首位,且心血管疾病的发病率仍持续增高。近十年来我国切实推进心血管健康事业建设,国家号召将心血管疾病的主战场由医院转向社区,因此迫切需要提升基层医疗服务质量来满足人民群众日益增长的健康需求。数字化信息时代的来临,使得机器学习广泛应用于图像辨别、语音识别和自然语言处理,人工智能在电商、家居、物流、交通等方面普遍运用,但对医疗保健的影响才刚刚开始。随着医疗数据可用性的提高和大数据分析方法的快速发展,人工智能在医疗领域的成功应用成为可能。在相关临床问题的指导下,强大的人工智能技术可以提取海量数据中隐藏的临床信息,进而辅助医生进行临床决策。近年来随着国家、社会对基层医疗的重视及互联网信息技术的发展,机器学习技术运用于心血管疾病的诊断和预测已成为热门。机器学习正在逐渐改变医生诊断疾病和临床决策的方式,但每个心血管疾病的诊断和决策都需要在疾病和统计学方面进行一定程度的分析,选择最优的机器学习算法才能更好地解决临床问题。本文通过比较近5年来有关心血管疾病辅助诊断模型的曲线下面积、敏感性、特异性、准确性、F1值、C统计值等多个量化指标来评估不同疾病分类下机器学习的优势选择,系统总结了不同人工智能方法在各心血管疾病诊断和预测等方面中的运用,并对相关辅助诊断模型进行评价。

Based on the survey,cardiovascular disease mortality ranks first in the total mortality rate of urban and rural residents in China.The incidence of cardiovascular diseases is still increasing.In the past decade,China has effectively promoted the construction of cardiovascular health.The state has called for shifting the main battlefield of cardiovascular disease from hospitals to communities.Therefore,improving the quality of primary care services are necessary to meet the growing health demand of the people.With the advent of the digital information age,machine learning is widely used in image recognition,speech recognition and natural language processing.Artificial intelligence(AI)is widely used in e-commerce,home,logistics and transportation,however,its impact on medical care has just begun.With the improvement of medical data availability and the rapid development of big data analysis methods,the successful application of artificial intelligence in medical field becomes possible.Under the guidance of relevant clinical problems,powerful AI technology can extract the clinical information hidden in massive data and then assist doctors in clinical decision-making.In recent years,with the national and social attention to primary medical care and the development of Internet information technology,the application of machine learning technology in the diagnosis and prediction of cardiovascular diseases has become a hot topic.Machine learning is gradually changing the way doctors diagnose diseases and their clinical decision-making,but the diagnosis and decision-making of each cardiovascular disease requires a certain degree of analysis with regard to disease and statistics,and the selection of the optimal machine learning algorithm can better solve clinical problems.By comparing the area under the curve,sensitivity,specificity,accuracy,F1 value,C statistical value and other quantitative indicators of the auxiliary diagnosis model of cardiovascular diseases in recent 5 years,this paper evaluates the advantages of machine learning under different disease classifications,systematically summarises the application of different AI methods in the diagnosis and prediction of cardiovascular diseases and evaluates related auxiliary diagnosis models.