肝性脑病是由肝功能不全引起的中枢神经系统功能紊乱的临床综合征。它严重影响患者的生活质量，并可能导致死亡。准确预测肝性脑病的发生风险对于早期干预和治疗至关重要。为了提前识别患者的肝性脑病风险，许多研究都在致力于努力开发工具及方法，以尽早识别肝性脑病的风险，从而制定预防和早期管理策略。目前大多数传统的肝性脑病风险预测模型通过分析临床数据和生化指标等因素，来评估患者发生肝性脑病的概率，然而其准确性、灵敏性和阳性预测值都不高。人工智能在临床预测模型的应用是一个非常热门和有前景的领域，它可以利用大量的数据和复杂的算法来提高诊断和预后的准确性和效率。到目前为止，利用人工智能技术预测肝性脑病的研究还很少。因此，本文综述了肝性脑病风险预测模型的研究进展，探讨人工智能在肝性脑病风险预测模型中的应用前景，并指出人工智能在肝性脑病风险预测模型研究中的挑战及未来研究方向，以促进肝性脑病风险预测模型的发展和临床应用。

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.