Effect of AI deep learning techniques on possible complications and clinical nursing quality of patients with coronary heart disease

Abstract

Artificial Intelligence (AI) is a new science and technology that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence, and is an important branch of computer science (Iranirad et al., 2017; Evans, 2019). It is an attempt to understand the nature of intelligence and thus to produce an intelligent machine that can respond in a similar way to human intelligence. This field covers a very wide range, including robots, image recognition, language recognition, natural language processing, food security and expert systems etc (Li et al., 2018; Jackson & Cameron, 2020; Hollands et al., 2018). There are various traditional machine learning algorithms for AI implementation, but the most effective and powerful algorithm is the deep learning algorithm, so AI technology at this stage usually refers to deep learning (Zeng et al., 2019). With the rapid social and economic development, and the continuous improvement of living standards, people have paid more and more attention to their health issues and hoped to get high-quality and convenient health care services. The application of AI deep learning technology in medical industry has also obtained tentatively favorable effect, especially in data processing of medical history and predication of illness conditions (Lustberg et al., 2018). As one of the most common cardiovascular diseases in China, the patients with coronary heart disease have severe conditions and rapid changes, heavy clinical treatment and nursing, presenting various risks of potential cardiovascular disease (Weber et al., 2019). Therefore, the patients with coronary heart disease were taken as research objects in this paper to analyze and explore the application value of AI deep learning techniques in prediction of possible complications of this disease, and its effect on improvement of nursing quality.