Application of artificial intelligence in HE risk prediction modelling and research advances.

Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency. It severely affects the quality of life of patients and may lead to death. Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment. In order to identify the risk of hepatic encephalopathy in patients in advance, many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible, so as to develop preventive and early management strategies. Most conventional hepatic encephalopathy risk prediction models currently assess the probability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators, however, their accuracy, sensitivity and positive predictive value are not high. The application of artificial intelligence to clinical predictive modelling is a very hot and promising area, which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis. To date, there have been few studies using AI techniques to predict hepatic encephalopathy. Therefore, this paper reviews the research progress of hepatic encephalopathy risk prediction models, and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models. It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models. [ABSTRACT FROM AUTHOR]