背景:本研究旨在开发和验证使用机器学习(ML)预测急性肠系膜缺血(AMI)患者住院死亡率的人工智能模型。
方法:2011年1月至2023年6月在萨卡里亚大学培训研究医院诊断为AMI的患者共122例纳入研究。这些患者被分为训练队列(n=97)和验证队列(n=25),并在住院期间进一步分为幸存者和非幸存者。以血清为基础的实验室结果作为特征。使用Python中的递归特征消除(RFE)来消除超特征,以优化结果。ML算法和数据分析使用Python(版本3.7)进行。
结果:在患者中,56.5%为男性(n=69),43.5%为女性(n=53)。平均年龄为71.9岁(范围39-94岁)。住院期间的死亡率为50%(n=61)。为了获得最佳结果,该模型结合了年龄、红细胞分布宽度(RDW),C反应蛋白(CRP),D-二聚体,乳酸,球蛋白,和肌酐。测试数据的成功率如下:逻辑回归(LG),80%;随机森林(RF),60%;k最近邻(KN),52%;多层感知器(MLP),72%;支持向量分类器(SVC),84%。投票分类器(VC),汇总所有模型的选票,成功率达到84%。在模型中,注意SVC(灵敏度1.0,特异性0.77,曲线下面积(AUC)0.90,置信区间(95%):(0.83-0.84))和VC(灵敏度1.0,特异性0.77,AUC0.88,置信区间(95%):(0.83-0.84))的有效性。
结论:确定了AMI患者死亡的独立危险因素。已开发出一种使用各种ML模型来预测死亡率的高效快速方法。
BACKGROUND: This study aimed to develop and validate an artificial intelligence model using machine learning (ML) to predict hospital mortality in patients with acute mesenteric ischemia (AMI).
METHODS: A total of 122 patients diagnosed with AMI at Sakarya University Training and Research Hospital between January 2011 and June 2023 were included in the study. These patients were divided into a training cohort (n=97) and a validation cohort (n=25), and further categorized as survivors and non-survivors during hospitalization. Serum-based laboratory results served as features. Hyperfeatures were eliminated using Recursive Feature Elimination (RFE) in Python to optimize outcomes. ML algorithms and data analyses were performed using Python (version 3.7).
RESULTS: Of the patients, 56.5% were male (n=69) and 43.5% were female (n=53). The mean age was 71.9 years (range 39-94 years). The mortality rate during hospitalization was 50% (n=61). To achieve optimal results, the model incorporated features such as age, red cell distribution width (RDW), C-reactive protein (CRP), D-dimer, lactate, globulin, and creatinine. Success rates in test data were as follows: logistic regression (LG), 80%; random forest (RF), 60%; k-nearest neighbor (KN), 52%; multilayer perceptron (MLP), 72%; and support vector classifier (SVC), 84%. A voting classifier (VC), aggregating votes from all models, achieved an 84% success rate. Among the models, SVC (sensitivity 1.0, specificity 0.77, area under the curve (AUC) 0.90, Confidence Interval (95%): (0.83-0.84)) and VC (sensitivity 1.0, specificity 0.77, AUC 0.88, Confidence Interval (95%): (0.83-0.84)) were noted for their effectiveness.
CONCLUSIONS: Independent risk factors for mortality were identified in patients with AMI. An efficient and rapid method using various ML models to predict mortality has been developed.