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
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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。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.
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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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