Cardiovascular disease is still the main cause of disability and death around the world. It may damage the heart, arteries, veins, or the entire cardiovascular system and induce coronary heart disease, hypertension, heart failure, vascular calcification, etc. The critically ill patients with cardiovascular disorders admitted to the medical intensive care unit show a diverse patient population. A noteworthy substantial proportion of patients in the medical intensive care unit with non-cardiac illness have associated cardiac co-morbidities. These critically ill patients need a judicious and appropriate treatment plan to save their life.
With the development of big data, bibliometrics and artificial intelligence, prescriptions could be evaluated and make recommended modifications with intelligent algorithms to ensure rational and quality medical care. Bibliometrics is a useful approach to describe the development trend of the research field. Machine learning algorithms could be carried out to forecast heart attacks and give treatment advice.
This Research Topic aims to evaluate the risk of critically ill patients with cardiovascular disorders or patients with non-cardiac illness who may suffer associated cardiac co-morbidities with artificial intelligence.
We welcome submissions focusing on, but not limited to, the following subtopics,
? Development in artificial intelligence, especially machine learning and deep learning, for assessing the disease process and development in critically ill patients with cardiovascular disorders
? Utilizing artificial intelligence to evaluate cardiovascular risk and treatment priority in critically ill patients
? Artificial intelligence algorithms to assist doctors in making accurate prescriptions for critically ill patients with cardiovascular disorders with minimal adverse reactions
? Further research progress in the application of artificial intelligence in critically ill patients with cardiovascular disorders
? Bibliometric approach to provide an overall assessment of the current status, hotspots and trends in cardiovascular comorbidities
Cardiovascular disease is still the main cause of disability and death around the world. It may damage the heart, arteries, veins, or the entire cardiovascular system and induce coronary heart disease, hypertension, heart failure, vascular calcification, etc. The critically ill patients with cardiovascular disorders admitted to the medical intensive care unit show a diverse patient population. A noteworthy substantial proportion of patients in the medical intensive care unit with non-cardiac illness have associated cardiac co-morbidities. These critically ill patients need a judicious and appropriate treatment plan to save their life.
With the development of big data, bibliometrics and artificial intelligence, prescriptions could be evaluated and make recommended modifications with intelligent algorithms to ensure rational and quality medical care. Bibliometrics is a useful approach to describe the development trend of the research field. Machine learning algorithms could be carried out to forecast heart attacks and give treatment advice.
This Research Topic aims to evaluate the risk of critically ill patients with cardiovascular disorders or patients with non-cardiac illness who may suffer associated cardiac co-morbidities with artificial intelligence.
We welcome submissions focusing on, but not limited to, the following subtopics,
? Development in artificial intelligence, especially machine learning and deep learning, for assessing the disease process and development in critically ill patients with cardiovascular disorders
? Utilizing artificial intelligence to evaluate cardiovascular risk and treatment priority in critically ill patients
? Artificial intelligence algorithms to assist doctors in making accurate prescriptions for critically ill patients with cardiovascular disorders with minimal adverse reactions
? Further research progress in the application of artificial intelligence in critically ill patients with cardiovascular disorders
? Bibliometric approach to provide an overall assessment of the current status, hotspots and trends in cardiovascular comorbidities
