首页|人工智能在慢加急性肝衰竭预后预测模型中的研究现状

人工智能在慢加急性肝衰竭预后预测模型中的研究现状

扫码查看
慢加急性肝衰竭(ACLF)是在慢性肝病基础上出现的急性肝功能恶化,且以肝脏和/或肝外器官衰竭和短期高病死率为主要特征的复杂临床综合征.目前缺乏有效的治疗手段,内科综合治疗下病死率高达50%~90%.开发简单快捷、准确性高的ACLF预后预测模型,能帮助临床医师早期准确判断ACLF患者预后,识别预后不良患者,从而实施早期干预,可在一定程度上改善预后,有助于降低病死率.随着计算机科学的不断发展,数据处理能力愈发强大,人工智能越来越受到重视,在肝脏疾病的诊断、治疗、预后预测等多方面均有应用.本文结合国内外研究现状,对常见的ACLF预后模型和机器学习预后预测模型进行综述,总结最新研究进展,为ACLF预后预测模型未来发展提供新思路.
Current status of research on artificial intelligence in prognostic prediction models for acute-on-chronic liver failure
Acute-on-chronic liver failure(ACLF)is a complex clinical syndrome of acute liver function deterioration on the basis of chronic liver diseases,characterized by hepatic and/or extra-hepatic organ failure and a high short-term mortality rate.At present,there is still a lack of effective treatment methods,and the mortality rate of ACLF reaches 50%-90%after comprehensive medical treatment.A simple,rapid,and accurate prognostic prediction model for ACLF can help clinicians accurately judge the prognosis of ACLF patients in the early stage,identify the patients with poor prognosis,and provide early interventions,which can improve patient prognosis to some extent and help to reduce mortality rates.With the continuous development of computer science and increasingly powerful data processing capabilities,artificial intelligence is gaining more attention and has been applied in various aspects of liver diseases including diagnosis,treatment,and prognostic prediction.With reference to the current status of research in China and globally,this article reviews the common prognostic models for ACLF and machine learning-based prognostic prediction models and summarizes the latest research advances,in order to provide new perspectives for the future development of prognostic prediction models for ACLF.

Acute-On-Chronic Liver FailureArtificial IntelligenceMachine LearningPrognosis

姜伟、常秀君、曾帆、兰蕴平

展开 >

成都中医药大学医学与生命科学学院,成都 610075

电子科技大学附属医院/四川省人民医院重症医学中心,成都 610072

慢加急性肝功能衰竭 人工智能 机器学习 预后

四川省科技攻关计划

2023YFS0134

2024

临床肝胆病杂志
吉林大学

临床肝胆病杂志

CSTPCD北大核心
影响因子:1.428
ISSN:1001-5256
年,卷(期):2024.40(9)