人工智能在肝衰竭预警及预后体系中的应用与挑战

胡美倩; 李君

doi:10.12449/JCH251104

人工智能在肝衰竭预警及预后体系中的应用与挑战

DOI: 10.12449/JCH251104

胡美倩,
李君^, ,

浙江大学医学院附属第一医院传染病重症诊治全国重点实验室，杭州 310003

基金项目:

浙江省尖兵领雁项目 (2025C02130)

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：胡美倩负责撰写论文，绘制图表；李君负责拟定写作思路，指导论文撰写并修改论文。

详细信息

通信作者:
李君， lijun2009@zju.edu.cn （ORCID： 0000-0002-7236-8088）

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出版历程
- 收稿日期: 2025-09-01
- 录用日期: 2025-10-14
- 出版日期: 2025-11-25

Application and challenges of artificial intelligence in prediction and prognosis systems for liver failure

Meiqian HU,
Jun LI^{,
,}

State Key Laboratory for Diagnosis and Treatment of Infectious Diseases，The First Affiliated Hospital，Zhejiang University School of Medicine，Hangzhou 310003，China

Research funding:

Key R&D Program of Zhejiang (2025C02130)

More Information

Corresponding author: LI Jun， lijun2009@zju.edu.cn （ORCID： 0000-0002-7236-8088）

摘要

摘要: 肝衰竭是由多种因素引起的严重肝损害，导致肝脏合成、解毒、代谢和生物转化功能严重障碍或失代偿，出现以黄疸、凝血功能障碍、肝肾综合征、肝性脑病及腹水等为主要表现的一组临床综合征，早期精准预后预测对改善患者临床结局至关重要。近年来，基于人工智能（AI）的预警预测模型正逐步改变传统诊疗模式。本文系统综述了利用机器学习构建的预警及预后模型在急性肝衰竭及慢加急性肝衰竭中的应用进展，相关模型在风险分层与预后预测方面展现出良好性能。随着技术的不断发展，AI有望为肝衰竭的早期干预和精准治疗提供新机遇，从而显著改善患者预后。
- 肝功能衰竭 /
- 人工智能 /
- 机器学习 /
- 早期诊断 /
- 预后
Abstract: Liver failure is a severe liver injury caused by multiple factors， leading to significant impairment or decompensation of the liver’s synthetic， detoxification， metabolic， and biotransformation functions， and it is a group of clinical syndromes with the main manifestations of jaundice， coagulation disorder， hepatorenal syndrome， hepatic encephalopathy， and ascites. Early and accurate prognostic prediction is crucial for improving the clinical outcome of patients. In recent years， artificial intelligence （AI）-based early warning and prediction models are gradually transforming the traditional diagnostic and therapeutic approaches. This article systematically reviews the advances in the application of machine learning-based early warning and prediction models in acute liver failure and acute-on-chronic liver failure， and related models have shown good performance in risk stratification and prognosis prediction. With the continuous development of related technologies， AI is expected to provide new opportunities for the early intervention and precise treatment of liver failure， thereby significantly improving the prognosis of patients.
- Liver Failure /
- Artificial Intelligence /
- Machine Learning /
- Early Diagnosis /
- Prognosis

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图 1 ACLF临床预警及预后评估工作流程图（以COSSH诊断标准及评分模型为例）

Figure 1. Clinical early warning and prognosis assessment workflow diagram （taking the COSSH diagnostic criteria and scoring model as an example）

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表 1 AI在疾病预测及预警中的应用

Table 1. The application of AI in disease prediction and early warning

算法类型	原理	应用场景
监督学习
线性回归	所有数据点的最佳拟合线	连续型（数值）结果预测
逻辑回归	采用逻辑函数对分类因变量建模	临床结局预测；疾病类别划分
Cox回归	通过特征预测个体随时间变化的事件风险	风险随时间变化的事件预测
支持向量机	通过核技巧实现高维特征的非线性分类	风险预测；疾病分类；图像识别
K近邻算法	基于特征向量间欧氏距离进行预测	连续/分类结局预测
决策树	树状模型进行是/否决策和结果预测	临床事件的预测；疾病类别划分
随机森林	多棵决策树组成的集成模型	临床事件的预测；疾病类别划分
梯度提升	较弱预测模型的分阶段集成	临床事件的预测；疾病类别划分
分类与回归树	通过递归分割数据生成规则	临床事件的预测；疾病类别划分
贝叶斯网络	用带概率的有向图描述变量关系	临床事件的预测；疾病类别划分
无监督学习
K均值聚类	基于欧氏距离将无标签数据分组	群体内的聚类识别
潜在类别模型	识别潜在亚型，辅助分群	辅助分群与预后判断
主成分分析	基于正交变换的无监督线性降维方法	降维可视化；特征提取；消除共线性
层次聚类分析	合并最相似的数据点生成一棵展现数据层次的分类树	数据内在层次结构分析
神经网络与深度学习
人工神经网络	模拟神经元信号传递的计算结构	多模态数据融合分析
卷积神经网络	仿生物视觉皮层连接模式	图像识别；自然语言处理
循环神经网络	沿时间序列构建有向图	手写/语音识别；临床事件的预测
长短期记忆网络	特殊的循环神经网络，通过门控机制解决长期依赖	动态预警；多参数监护
Transformer	基于自注意力机制，通过位置编码保留时序信息	多模态融合；长序列预测

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表 2 基于AI的肝衰竭预警及预后常用模型总结

Table 2. Summary of commonly used AI-based models for liver failure early warning and prognosis

模型	应用人群	评价指标	评分公式	临床结局	机器学习算法
ALF预后评分模型
KCC^［20］	APAP相关暴发性肝衰竭、非APAP 相关暴发性肝衰竭	动脉血pH、肌酐、HE 等级、PT、年龄、TBil、病因	/	住院期间病死率	逻辑回归
MELD^［21］	终末期肝病	TBil、INR、肌酐、病因	3.78×lnTBil（mg/dL）+11.2× ln（INR）+9.6×ln肌酐（mg/dL） +6.43×病因	90天病死率	Cox比例风险回归、逻辑回归
ALFSG-PI^［22］	APAP诱发ALF、非APAP诱发ALF	HE等级、病因、血管升压药的使用、TBil、 INR	2.67-0.95×HE+1.56×病因 -1.25×血管升压药使用 -0.70×lnTBil（mg/dL）-1.35 ×lnINR	21天病死率	逻辑回归
DIALF-5^［23］	非APAP诱发ALF	INR、HE等级、血管升压药的使用、N-乙酰半胱氨酸和人工肝支持系统使用	/	21天病死率	Cox比例风险回归
ACLF预后评分模型
COSSH-ACLF Ⅱ^［24］	慢性肝病急性失代偿（不论有无肝硬化）	INR、HE等级、中性粒细胞、TBil、血尿素、年龄	1.649×ln（INR）+0.457×HE 评分+0.425×ln（中性粒细胞） +0.396×ln（TBil）+0.576× ln（血尿素）+0.033×年龄	28/90天病死率	竞争风险回归、 LASSO回归
COSSH-ACLF^［10］	慢性肝病急性失代偿（不论有无肝硬化）	HBV-SOFA评分、 INR、TBil、年龄	0.741×INR+0.523×HBV- SOFA+0.026×年龄+0.003× TBil（μmol/L）	28/90天病死率	Cox比例风险回归
CLIF-C ACLF^［27］	肝硬化急性失代偿性	CLIF-OF评分、年龄、白细胞	10×0.33×CLIF-OF+0.04×v年龄+0.63×ln（白细胞）-2	28/90天病死率	逻辑回归、竞争风险回归
CLIF-C MET^［28］	肝硬化急性失代偿性	年龄、4-羟基-3-甲氧基苯乙二醇硫酸盐、己酰肉碱、D-半乳糖醛酸	［0.023 96×年龄+0.329 81× log₂（4-羟基-3-甲氧基苯乙二醇硫酸盐）+0.456 02×log₂（己酰肉碱）+0.272 26×log₂（D-半乳糖醛酸）-18.156 1］/0.096 5	7/14/28/90天病死率	Cox比例风险回归、竞争风险回归
CLIF-SIG^［29］	肝硬化急性失代偿性	28个最具区分度的基因表达水平	/	系统性炎症严重程度、28天和90天病死率	LASSO回归
APASL-AARC^［17］	慢性肝病急性恶化	TBil、肌酐、INR、HE 等级、血乳酸	/	28天病死率	Cox比例风险回归
NACSELD-ACLF^［12］	肝硬化急性失代偿性	HE等级、肾脏替代治疗、机械通气、平均动脉压	/	≥2个器官衰竭者30天病死率	逻辑回归
ACLF预警评分模型
COSSH-onset-ACLF^［26］	慢性肝病急性失代偿（不论有无肝硬化）	ALT、TBil、INR、铁蛋白	0.101×ln（ALT）+0.819× ln（TBil）+2.820×ln（INR） +0.016×ln（铁蛋白）	ACLF发生率	LASSO-Cox 回归
注：HBV-SOFA，乙型肝炎相关序贯器官衰竭评估评分；/，无具体公式，根据评价指标判断。

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表 3 基于AI的肝衰竭预警及预后标志物总结

Table 3. Summary of AI-based biomarkers for liver failure early warning and prognosis

组学类型	生物标志物	来源	目标疾病	预后影响	机器学习算法
基因组学	rs3129859-C、HLA- DRB1^*12∶02^［46］	全血DNA	HBV-ACLF	携带rs3129859-C或HLA-DRB1^*12∶02 风险等位基因，则凝血差、易出现腹水，且28天病死率更高	逻辑回归、线性回归
转录组学	SEMA6B、THBS1、 MERTK、PPARG、 ETS2、VSIG4	PBMC	HBV-ACLF	SEMA6B、THBS1、MERTK、PPARG、 ETS2和VSIG4高表达标志着剧烈的炎症反应和高短期病死率	层次聚类和主成分分析
	COL4A2^［32］	肝组织	ALF	COL4A2高表达提示疾病更严重，短期预后差	LASSO回归、支持向量机
	VCAN、CXCR2^［52］	PBMC/肝组织	HBV-ACLF	早期VCAN⁺CD14⁺单核细胞驱动炎症风暴；后期CXCR2⁺中性粒细胞导致免疫耗竭及恶化	UMAP、RNA 速率、 SCENIC、CellChat、 Cox比例风险回归
	Annexin A1^［53］	PBMC/ 肝组织	ALF/ACLF	Annexin A1高表达提示机体抗炎代偿能力强，预后较好	UMAP、SCENIC、CellChat
蛋白质组学	APOC3、HRG、TF、 KLKB蛋白^［54］	血浆	HBV-ACLF	APOC3、HRG、TF和KLKB1在 ACLF中显著下调，与高病死率相关，可用于慢性乙型肝炎区分	逻辑回归、Cox比例风险回归、主成分分析
代谢组学	哌可酸、N-乙酰天门冬酰谷氨酸、3-脲基丙酸、 γ-羧乙基羟色胺^［55］	血浆	HBV-ACLF	哌可酸、N-乙酰天门冬酰谷氨酸、 3-脲基丙酸的水平升高，γ-羧乙基羟色胺的水平降低预示ACLF的发生和不良预后	随机森林、逻辑回归
代谢组学	FGF21、C6∶0-和C8∶0- 肉碱^［56］	血浆	ACLF	FGF21、C6∶0-和C8∶0-肉碱高表达提示高短期病死率	偏最小二乘判别分析
微生物组学	基因丰富度、宏基因组物种丰富度^［57］	粪便	ACLF	基因丰富度、MGS丰富度显著下降提示疾病更严重，短期预后差	逻辑回归、随机森林
注：PBMC，外周血单个核细胞；SEMA6B，信号素6B；THBS1，血小板反应蛋白1；MERTK，原癌基因酪氨酸蛋白激酶MER；PPARG，过氧化物酶体增殖物激活受体γ；ETS2，ETS原癌基因2型转型录因子；VSIG4，含V-set和免疫球蛋白结构域蛋白4；COL4A2，Ⅳ型胶原蛋白α2链；VCAN，蛋白聚糖Versican；CXCR2，C-X-C基序趋化因子受体2；Annexin A1，膜联蛋白A1；FGF21，成纤维细胞生长因子21。

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