预测静脉注射免疫球蛋白抵抗性川崎病患者的机器学习模型：基于重庆人群的回顾性研究

数据集获取（Figshare）

• 源站标题：Data_Sheet_2_A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population.pdf
• DOI：10.3389/fped.2021.756095.s002
• 发布日期：2021-11-08
• 文件数：1 个
• 文件总体积：276.02 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_A_Machine_Learning_Model_to_Predict_Intravenous_Immunoglobulin-Resistant_Kawasaki_Disease_Patients_A_Retrospective_Study_Based_on_the_Chongqing_Population_pdf/16947085

命令行下载

curl -L -o "Data_Sheet_2_A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Ch.pdf" "https://ndownloader.figshare.com/files/31357960"

文件清单

• Data_Sheet_2_A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Ch.pdf（276.02 KB）

文件总体积 276.02 KB（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

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.
Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.
Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).
Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fped.2021.756095.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_A_Machine_Learning_Model_to_Predict_Intravenous_Immunoglobulin-Resistant_Kawasaki_Disease_Patients_A_Retrospective_Study_Based_on_the_Chongqing_Population_pdf/16947085）。