急性胰腺炎机器学习模型的研究进展

殷民月; 朱锦舟; 刘璐; 高静雯; 林嘉希; 许春芳

doi:10.3969/j.issn.1001-5256.2023.12.034

急性胰腺炎机器学习模型的研究进展

DOI: 10.3969/j.issn.1001-5256.2023.12.034

苏州大学附属第一医院消化内科，江苏苏州 215000

基金项目:

国家自然科学基金 (82000540);

苏州市科技计划 (SKY2021038)

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：殷民月、朱锦舟负责课题设计，撰写、修改论文；刘璐、高静雯、林嘉希参与文献检索与归纳；许春芳负责指导撰写文章并最后定稿。

详细信息

通信作者:
许春芳， xcf601@163.com （ORCID： 0000-0001-5648-3003）

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出版历程
- 收稿日期: 2023-04-02
- 录用日期: 2023-05-06
- 出版日期: 2023-12-12

Research advances in machine learning models for acute pancreatitis

Department of Gastroenterology，The First Affiliated Hospital of Soochow University，Suzhou，Jiangsu 215000，China

Research funding:

National Natural Science Foundation of China (82000540);

Science and Technology Plan of Suzhou City (SKY2021038)

More Information

Corresponding author: XU Chunfang， xcf601@163.com （ORCID： 0000-0001-5648-3003）

摘要

摘要: 急性胰腺炎是一种需要早期干预的消化系统急症，当进展为中度重症或重症急性胰腺炎时，患者病死率显著升高。机器学习凭借强大的计算和学习能力，充分利用临床数据对急性胰腺炎进行早期预测，取得了显著成果。本文综述机器学习在预测急性胰腺炎严重程度、并发症和死亡中的研究进展，为进一步通过人工智能协助急性胰腺炎临床诊疗提供理论依据和新思路。
- 急性胰腺炎 /
- 人工智能 /
- 有监督机器学习 /
- 无监督机器学习
Abstract: Acute pancreatitis （AP） is a gastrointestinal disease that requires early intervention， and when it progresses to moderate-severe AP （MSAP） or severe AP （SAP）， there will be a significant increase in the mortality rate of patients. Machine learning （ML） has achieved great success in the early prediction of AP using clinical data with the help of its powerful computational and learning capabilities. This article reviews the research advances in ML in predicting the severity， complications， and death of AP， so as to provide a theoretical basis and new insights for clinical diagnosis and treatment of AP through artificial intelligence.
- Acute Pancreatitis /
- Artificial Intelligence /
- Supervised Machine Learning /
- Unsupervised Machine Learning

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表 1 机器学习在AP中应用研究的基本特点

Table 1. Characteristics of studies on application of machine learning in acute pancreatitis

作者	年份	实验设计	研究目的	样本量	算法	AUC	敏感度/特异度
监督学习
Pearce^［13］	2006	回顾性	严重程度	265	核函数-逻辑回归	0.82	0.87/0.71
Sun^［14］	2021	回顾性	严重程度	945	随机森林	0.73	0.784/0.618
Langmead^［15］	2021	前瞻性	严重程度	60/133	随机森林	0.91	NA
Thapa^［16］	2021	回顾性	严重程度	61 894	逻辑回归 XGBoost 神经网络	0.780 0.921 0.811	0.850/0.503 0.851/0.830 0.851/0.561
Kui^［17］	2022	前瞻性	严重程度	1 184	XGBoost	0.81	NA
Yuan^［18］	2022	回顾性	严重程度	5 460	XGBoost 线性核函数-支持向量机径向基核函数-支持向量机逻辑核函数-支持向量机逻辑回归	0.952 0.838 0.885 0.862 0.892	NA
İnce^［19］	2023	回顾性	严重程度	1 334	梯度提升树模型	NA	NA
Luo^［20］	2023	回顾性	严重程度	631/109	随机森林 K近邻算法朴素贝叶斯	0.961 0.947 0.954	0.900/0.815 0.933/0.667 1.000/0.482
Kiss^［22］	2022	前瞻性	严重程度	2 387	XGBoost	0.757	NA
Fei^［23］	2018	回顾性	并发急性肺损伤	217	神经网络逻辑回归	0.859±0.048 0.701±0.041	0.875/0.833 0.750/0.708
Xu^［8］	2021	回顾性	并发多器官功能衰竭	455	逻辑回归朴素贝叶斯支持向量机 AdaBoost 二次判别分析反向传播网络	0.840 0.864 0.839 0.826 0.865 0.862	0.654/0.900 0.812/0.783 0.713/0.839 0.802/0.804 0.832/0.774 0.832/0.765
Zhang^［24］	2023	前瞻性	新发糖尿病	3 477	支持向量机 CatBoost 随机森林 L1-逻辑回归 L2-逻辑回归神经网络	0.489 0.783 0.788 0.819 0.805 0.751	NA
Hameed^［25］	2022	回顾性	死亡	6 258	随机森林决策树 XGBoost 逻辑回归多层感知器神经网络	0.942 0.690 0.944 0.875 0.902 0.908	0.273/0.997 0.414/0.967 0.364/0.993 0.141/0.994 0.251/0.992 0.278/0.989
无监督学习
Kimita^［26］	2022	前瞻性	识别AP相关细胞因子谱	107	聚类分析	NA	NA
注：AUC，受试者工作特征曲线下面积；NA，原文未提供数据。

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