OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
OBJECTIVE: To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their famil...
Artificial intelligence (AI) technology is advancing rapidly, constantly presenting its application value and broad prospects in the medical field. Especially in the early intervention of burn diseases, the new developments, applications, and challen...
OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment effects using machine learning techniques based on the United States Critical Care Medical Information...
OBJECTIVE: To evaluate the clinical practice of intensive care unit (ICU) physicians at Hebei General Hospital in identifying patients meeting the diagnostic criteria for acute respiratory distress syndrome (ARDS) and the current status of invasive m...
OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.
Sepsis is caused by infection, which can ultimately lead to multiple organ dysfunction and even life-threatening. Early recognition and early treatment can significantly improve the prognosis of sepsis patients. However, the effect of using a single ...
Kidney disease affects a large number of people around the world, imposing a significant burden to people's health and life. If early prediction, rapid diagnosis and prognosis prediction of kidney disease can be carried out, the health of patients wi...
OBJECTIVE: To investigate the utilization status and awareness of digital hospital construction among medical staff in critical care department of primary hospitals, so as to promote the process of digital medical health.
OBJECTIVE: To compare the effectiveness of Logistic regression, BP neural network and support vector machine models in the prediction of 30-day risk of readmission in elderly patients with an exacerbation of chronic obstructive pulmonary disease (COP...