应用可解释机器学习方法预测急性心肌梗死合并糖尿病患者住院死亡风险

数据集获取（Figshare）

• 源站标题：Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mellitus
• DOI：10.6084/m9.figshare.28748829.v1
• 发布日期：2025-04-08
• 文件数：2 个
• 文件总体积：1.85 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Application_of_an_interpretable_machine_learning_method_to_predict_the_risk_of_death_during_hospitalization_in_patients_with_acute_myocardial_infarction_combined_with_diabetes_mellitus/28748829

命令行下载

curl -L -o "tacd_a_2481662_sm5286.docx" "https://ndownloader.figshare.com/files/53492270"
curl -L -o "tacd_a_2481662_sm5285.docx" "https://ndownloader.figshare.com/files/53492273"

文件清单

• tacd_a_2481662_sm5286.docx（1.34 MB）
• tacd_a_2481662_sm5285.docx（511.43 KB）

文件总体积 1.85 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Predicting the prognosis of patients with acute myocardial infarction (AMI) combined with diabetes mellitus (DM) is crucial due to high in-hospital mortality rates. This study aims to develop and validate a mortality risk prediction model for these patients by interpretable machine learning (ML) methods.
Data were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.2). Predictors were selected by Least absolute shrinkage and selection operator (LASSO) regression and checked for multicollinearity with Spearman’s correlation. Patients were randomly assigned to training and validation sets in an 8:2 ratio. Seven ML algorithms were used to construct models in the training set. Model performance was evaluated in the validation set using metrics such as area under the curve (AUC) with 95% confidence interval (CI), calibration curves, precision, recall, F1 score, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The significance of differences in predictive performance among models was assessed utilising the permutation test, and 10-fold cross-validation further validated the model’s performance. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were applied to interpret the models.
The study included 2,828 patients with AMI combined with DM. Nineteen predictors were identified through LASSO regression and Spearman’s correlation. The Random Forest (RF) model was demonstrated the best performance, with an AUC of 0.823 (95% CI: 0.774–0.872), high precision (0.867), accuracy (0.873), and PPV (0.867). The RF model showed significant differences (p This study demonstrates the potential of ML methods, particularly the RF model, in predicting in-hospital mortality risk for AMI patients with DM. The SHAP and LIME methods enhance the interpretability of ML models.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28748829.v1（source: https://tandf.figshare.com/articles/dataset/Application_of_an_interpretable_machine_learning_method_to_predict_the_risk_of_death_during_hospitalization_in_patients_with_acute_myocardial_infarction_combined_with_diabetes_mellitus/28748829）。